AI Adoption Reflex Area
2026-09-18
Table of Contents
Understanding Current GenAI Capabilities
- What can current GenAI tools reliably do today?
- Which capabilities are useful enough for real organizational work?
- What kinds of tasks can GenAI complete with little assistance?
- Where does GenAI still need substantial human input?
- Which capabilities are commonly underestimated?
- Which capabilities are commonly exaggerated?
- What should leaders actually understand before making adoption decisions?
Reviewing Capabilities That Have Recently Become Practical
- Which GenAI capabilities have improved significantly in the past year?
- Which previously unreliable tasks are now becoming practical?
- What recent capability changes matter most to our work?
- Which new capabilities remove barriers that existed before?
- What can users now do that would have required specialist tools earlier?
- Which existing assumptions should we revisit because the technology improved?
- Which new capabilities are worth testing in our organization?
Distinguishing Mature Capabilities From Impressive Demonstrations
- Which capabilities work consistently outside controlled demonstrations?
- What evidence shows that a capability is ready for routine work?
- Which demonstrations depend on unusually favorable conditions?
- Where does performance vary too much for dependable use?
- Which limitations become visible only during repeated use?
- How should we test whether a capability is genuinely mature?
- Which capabilities should we treat as experimental for now?
Understanding Which Types of Work GenAI Handles Well
- What characteristics make work especially suitable for GenAI?
- Which language-heavy tasks does GenAI handle particularly well?
- Where can GenAI support analysis without replacing judgment?
- Which repetitive knowledge tasks are strong candidates?
- What kinds of preparation work benefit most from GenAI?
- Which tasks look suitable but often produce weak results?
- How can we explain the strongest work patterns to employees?
- What kinds of documents can GenAI help people understand or create?
- How can GenAI work with structured and unstructured data?
- What can GenAI reliably extract from images or visual material?
- Which combinations of different input types create useful new workflows?
- What input quality problems most affect the results?
- Where do privacy or access constraints limit these capabilities?
- Which multimodal uses are most relevant to our organization?
- What can GenAI do when it has access to external tools?
- How does tool access change the kinds of work GenAI can support?
- When can GenAI retrieve current information instead of relying on prior knowledge?
- Which actions can GenAI perform rather than merely recommend?
- Where does tool use still require human confirmation?
- What new risks appear when GenAI can act in external systems?
- Which tool-enabled capabilities are most relevant to our adoption plans?
Assessing How Far GenAI Can Support Complex Multi-Step Work
- Which parts of complex work can GenAI handle independently?
- Where does a multi-step task still need human direction?
- How well can GenAI maintain context across a longer workflow?
- Which steps are most vulnerable to compounding errors?
- What checkpoints should remain under human control?
- When is it better to divide the work into smaller interactions?
- Which complex workflows are realistic candidates for deeper GenAI support?
Recognizing Where Current GenAI Still Requires Strong Human Judgment
- Which decisions should never be delegated solely to GenAI?
- Where does context matter more than the model can reliably understand?
- Which tasks depend heavily on professional experience?
- Where can plausible output hide serious errors?
- Which ethical or organizational judgments require clear human ownership?
- How should users recognize when they have reached GenAI's limits?
- Where should we deliberately preserve stronger human review?
- What meaningful capability differences exist between our available tools?
- Which tools are strongest for the work our people actually perform?
- Where do tools differ in access to organizational information?
- Which tools support documents, data, images, or external actions differently?
- What usability differences could affect adoption?
- Which differences matter enough to influence tool choice?
- How should we explain tool choices without overwhelming users?
Updating Organizational Assumptions About What GenAI Can Do
- Which organizational beliefs about GenAI are now outdated?
- Which limitations are people still assuming that no longer fully apply?
- Which capabilities are people expecting that remain unrealistic?
- What evidence should change our current view of GenAI?
- Which policies or practices were built around older capability assumptions?
- What adoption decisions should be reconsidered in light of current capabilities?
- How should we keep organizational assumptions current over time?
Understanding the Business and Competitive Impact of GenAI
Identifying Where GenAI Is Already Changing Business Activity
- Which business activities are already changing because of GenAI?
- Where are organizations replacing old working methods with GenAI-supported ones?
- Which functions are seeing the most immediate impact?
- What kinds of work are becoming faster or easier?
- Which activities are becoming possible at much lower cost?
- Where is the impact still limited despite high expectations?
- What changes are most relevant to our organization right now?
Understanding Where GenAI Could Improve Productivity
- Which activities consume substantial time without requiring constant judgment?
- Where could GenAI reduce repetitive cognitive effort?
- Which tasks involve repeated drafting, summarizing, or analysis?
- Where could people prepare work more quickly with GenAI?
- Which productivity gains would actually matter at organizational scale?
- Where might faster output simply create more low-value work?
- Which productivity opportunities deserve closer examination?
Understanding How GenAI Could Affect Cost, Capacity, and Revenue Potential
- Where could GenAI materially reduce the cost of existing work?
- Which constraints on organizational capacity could GenAI ease?
- What additional work could current teams handle with GenAI support?
- Which services could become economical at smaller scale?
- Where could GenAI improve the economics of existing offerings?
- What new revenue opportunities become realistic because effort falls?
- Which economic effects could be significant enough to change priorities?
Identifying How GenAI Could Change Decision-Making
- Which decisions are slowed by information gathering or preparation?
- Where could GenAI improve the quality of decision inputs?
- Which decisions could consider more alternatives with GenAI support?
- Where could assumptions and tradeoffs be challenged more systematically?
- Which decisions could be made faster without reducing accountability?
- Where might GenAI create false confidence instead of better decisions?
- Which decision processes are most worth rethinking?
Understanding How GenAI Is Changing Customer Expectations
- Where are customers already expecting faster responses because of GenAI?
- Which forms of personalization are becoming normal?
- Where might customers expect better explanations or support?
- Which service standards could rise as competitors adopt GenAI?
- What customer experiences may begin to feel outdated?
- Where could GenAI create expectations we should deliberately not meet?
- Which changing expectations matter most to our business?
Assessing How Competitors Are Using GenAI
- Where are competitors visibly using GenAI today?
- Which competitor uses appear to create real customer or operational value?
- Which uses seem mostly promotional?
- Where are competitors changing their cost or service model?
- Which capabilities could give competitors an execution advantage?
- What can we learn without simply copying their approach?
- Which competitive developments should influence our adoption priorities?
Exploring How GenAI Could Change the Industry
- Which industry activities are most exposed to GenAI-driven change?
- Where could GenAI alter traditional value chains?
- Which specialist capabilities may become more widely available?
- Which intermediaries could become less necessary?
- What new entrants could GenAI enable?
- Which existing advantages may become less defensible?
- What industry changes should we prepare for before they become obvious?
Identifying New Sources of Value Enabled by GenAI
- What valuable work becomes possible because GenAI lowers the effort required?
- Which customer problems could now be addressed differently?
- Where could GenAI enable more individualized service?
- Which information assets could become more useful with GenAI?
- What capabilities could we offer that were previously too expensive?
- Which opportunities depend on strengths unique to our organization?
- What new value opportunities deserve experimentation?
Understanding the Cost of Adopting GenAI Too Slowly
- What work improvements are we delaying by moving slowly?
- Where could slower adoption create a growing productivity gap?
- Which capabilities might competitors build before we do?
- What employee expectations could change while we wait?
- Which learning advantages do early adopters accumulate over time?
- Where could delay make later transformation more difficult?
- What is the practical cost of waiting another year?
Translating GenAI Developments Into Strategic Implications
- Which GenAI developments are strategically relevant rather than merely interesting?
- What assumptions in our strategy could these developments challenge?
- Which opportunities become more attractive as capabilities improve?
- Which existing risks become more important?
- What strategic options should now be reconsidered?
- Which decisions should be accelerated because the environment changed?
- What should leadership do differently because of these developments?
Assessing the Current Adoption Baseline
Determining Who Currently Has Access to GenAI
- Which employees currently have access to approved GenAI tools?
- Which teams have broad access and which do not?
- Are there differences by role, location, or employment type?
- What kinds of access do different groups actually have?
- Where is access temporary, restricted, or incomplete?
- Which groups need access but currently lack it?
- What does our actual access landscape look like?
Understanding How Many People Are Actually Using GenAI
- How many people with access use GenAI at all?
- How many use it beyond an initial experiment?
- Which teams have the highest proportion of active users?
- Where is access high but usage low?
- Which user groups are barely engaging?
- What evidence do we have beyond self-reported use?
- What does current participation tell us about adoption?
Assessing How Frequently People Use GenAI
- How often do current users return to GenAI?
- How many use it weekly or daily?
- Which groups use it only occasionally?
- Does frequency vary by role or type of work?
- What situations tend to trigger repeat use?
- Where does usage drop off after initial experimentation?
- What does frequency reveal about whether habits are forming?
Identifying What People Currently Use GenAI For
- What are the most common current GenAI uses?
- Which uses are limited to drafting or summarization?
- Where are people using GenAI for thinking or analysis?
- Which recurring tasks already include GenAI?
- What valuable uses are only appearing in isolated teams?
- Which important work areas show almost no GenAI use?
- What does the current usage mix tell us about adoption maturity?
Comparing Adoption Across Teams and Functions
- Which teams are using GenAI most extensively?
- Which functions show the lowest adoption?
- How does frequency differ across comparable teams?
- Which teams use GenAI for a broader range of work?
- What local conditions might explain the differences?
- Which comparisons are misleading because the work differs substantially?
- What can higher-adoption teams teach the rest of the organization?
Understanding Current Levels of GenAI Confidence
- How comfortable are people using GenAI without assistance?
- Where do users hesitate even when they have access?
- Which tasks make people feel least confident?
- How confident are people in judging GenAI output quality?
- Who is comfortable experimenting when the first result is weak?
- Where does confidence differ sharply from actual capability?
- What confidence gaps are most likely to limit adoption?
Assessing How Independently People Can Use GenAI
- Can people identify GenAI opportunities without being prompted?
- Can they describe their work situation clearly enough for GenAI?
- Can they improve a weak response through follow-up?
- Can they recognize when an output needs verification?
- Can they adapt a successful method to a new task?
- Where do users still depend heavily on templates or expert help?
- How independent is current GenAI use in practice?
Understanding How Managers Currently Support GenAI Use
- How often do managers encourage useful GenAI experimentation?
- Do managers discuss GenAI opportunities during normal work?
- Are employees given time to try new approaches?
- How do managers respond when experiments fail?
- Do managers understand the tools well enough to support their teams?
- Where are managerial behaviors unintentionally discouraging use?
- How much of current adoption depends on individual manager support?
Identifying Existing Examples of Valuable GenAI Use
- Which current GenAI uses are creating clear practical value?
- Where has GenAI improved speed, quality, or decision preparation?
- Which examples are repeatable rather than one-off successes?
- Who developed the most useful working approaches?
- What conditions made those examples successful?
- Which examples could inspire similar work elsewhere?
- What do our strongest examples reveal about future adoption potential?
Creating an Overall Picture of the Current Adoption Baseline
- What does the evidence say about our current adoption level?
- Where is adoption strongest and weakest?
- How broad is GenAI use across the organization?
- How deeply is GenAI integrated into recurring work?
- Which groups are confident and which still need support?
- What are the most important gaps in the current baseline?
- What should leadership understand before choosing the next actions?
Understanding How People Currently Work
Identifying the Main Responsibilities of Different Roles
- What is this role actually responsible for delivering?
- Which responsibilities consume most of the person's time?
- Which responsibilities require the most judgment?
- Which responsibilities involve heavy information processing?
- What recurring outputs does the role produce?
- Where does the role depend on other people or teams?
- Which responsibilities are most relevant to GenAI adoption?
- What tasks does this role perform every day or week?
- Which tasks follow a similar pattern each time?
- Where does the person repeatedly read, write, compare, or summarize?
- Which tasks require preparation before another activity?
- Which tasks involve repeated coordination with others?
- Which recurring tasks create the most friction?
- Which task patterns are strong candidates for GenAI support?
- Where do people currently get the information they need?
- How much time is spent searching for information?
- Which information sources are difficult to navigate?
- Where do people manually combine information from several places?
- Which information is frequently misunderstood or overlooked?
- What information cannot currently be accessed easily?
- Where could GenAI materially improve information use?
Identifying Repetitive Knowledge Work
- Which cognitive tasks are repeated with only small variations?
- Where do people recreate similar documents or analyses?
- Which recurring activities depend on familiar reasoning patterns?
- What work is mentally tiring without requiring much new judgment?
- Which repetitive tasks are still too variable for simple automation?
- Where could GenAI reduce effort without reducing quality?
- Which repetitive work should we examine first?
Understanding Where Important Decisions Are Made
- Which recurring decisions have meaningful consequences?
- Who currently prepares information for those decisions?
- What evidence or analysis do decision-makers rely on?
- Where is decision preparation slow or inconsistent?
- Which assumptions are rarely challenged?
- Where could GenAI strengthen preparation without taking over the decision?
- Which decision points are most relevant to adoption?
Mapping How Work Moves Between People and Teams
- How does work move from one role to another?
- Where are the main handoffs?
- What information is lost during handoffs?
- Where does work wait unnecessarily?
- Which handoffs create repeated clarification or rework?
- Where could GenAI improve preparation or continuity?
- Which cross-team workflows should we investigate further?
Identifying Common Sources of Friction and Delay
- What repeatedly slows people down?
- Which tasks create unnecessary waiting?
- Where do unclear requirements cause rework?
- Which information gaps delay progress?
- Where do people spend time reorganizing or rewriting material?
- Which frictions are suitable for GenAI support?
- Which frictions are actually structural problems GenAI will not solve?
- Which tools are central to everyday work?
- What information does each tool contain?
- Where do people switch repeatedly between systems?
- Which tools create the most manual copying or reformatting?
- What useful information is difficult to access from GenAI?
- Which tool limitations could constrain adoption?
- Where could GenAI complement rather than replace existing tools?
- Where do people work around official processes?
- What spreadsheets, notes, templates, or personal methods do they rely on?
- Which workarounds exist because formal tools are too slow?
- What problems are employees solving for themselves?
- Which workarounds reveal unmet needs?
- Where could GenAI replace a fragile workaround with a better method?
- Which workarounds should we understand before changing anything?
Understanding Which Work Patterns Are Most Relevant to GenAI Adoption
- Which recurring work patterns appear across many roles?
- Where do the same information problems occur repeatedly?
- Which patterns involve substantial language or knowledge work?
- Which work patterns have enough frequency to justify adoption effort?
- Which patterns are easy for people to recognize in their own work?
- Where could a small improvement create broad organizational value?
- Which patterns should shape our adoption approach?
Understanding Different User Groups
Identifying Meaningfully Different Groups of GenAI Users
- Which groups differ enough to need different adoption support?
- Should we segment by role, function, experience, or work type?
- Which differences actually affect GenAI use?
- Where would broad categories hide important variation?
- Which groups face similar adoption conditions?
- How many user groups can we meaningfully support?
- What segmentation gives us the clearest view of adoption needs?
Understanding Differences in GenAI Experience
- Which groups have never used GenAI?
- Who has only basic experience?
- Which users already work with GenAI regularly?
- Who has experience beyond simple drafting and summarization?
- Where are self-reported skills higher than practical capability?
- Which experienced users could provide useful learning?
- How should experience differences affect adoption support?
Understanding Differences in Motivation to Use GenAI
- Which groups are already motivated to use GenAI?
- Who sees little reason to change current working methods?
- What benefits matter most to different roles?
- Where is curiosity high but follow-through low?
- Which groups perceive more risk than value?
- What practical benefits matter enough to motivate each group?
- How should we address groups with fundamentally different motivations?
Identifying Different Role-Specific Needs
- What does each role need GenAI to help with?
- Which recurring tasks differ most across roles?
- What information does each role require?
- Which outputs matter most to each group?
- Where do roles need different levels of precision or review?
- Which needs can be addressed with common methods?
- Where is role-specific support genuinely necessary?
Understanding Which Groups Need the Most Initial Support
- Which groups have low confidence and low experience?
- Who faces the greatest uncertainty about acceptable use?
- Which groups have access but rarely use GenAI?
- Where would weak early experiences be especially damaging?
- Which teams lack local people who can help?
- What support gaps are most likely to hold each group back?
- Which groups should receive additional attention first?
Identifying Groups That Are Ready to Experiment More Deeply
- Which groups already use GenAI comfortably?
- Who is asking for more advanced ways to use it?
- Which teams have suitable work for deeper experimentation?
- Where do managers actively support experimentation?
- Which groups can review results critically?
- Where could advanced experiments create useful organizational learning?
- Which groups are ready to move beyond basic adoption?
Understanding Differences in Concerns and Risk Perception
- What concerns are most common in each group?
- Which groups worry most about quality?
- Who is most concerned about data or confidentiality?
- Where are job-related concerns strongest?
- Which groups perceive GenAI as more threatening than useful?
- What concerns reflect real constraints rather than misunderstanding?
- How should our response differ across groups?
Identifying Managers With Different Levels of Adoption Readiness
- Which managers actively use GenAI themselves?
- Who understands how GenAI could help their team?
- Which managers are supportive but uncertain how to lead adoption?
- Who is skeptical or disengaged?
- Where are managers unintentionally blocking experimentation?
- Which managers could help others learn?
- What different support do these manager groups need?
Understanding Differences Between Occasional and Regular Users
- What makes regular users return to GenAI?
- Why do occasional users stop after initial experiments?
- Which tasks distinguish regular from occasional use?
- Do regular users have stronger opportunity-recognition habits?
- How do confidence and skill differ between the groups?
- What organizational conditions support regular use?
- What could help occasional users become more consistent?
Understanding How Adoption Support Needs Differ Across User Groups
- Which groups need basic orientation rather than advanced methods?
- Who needs examples tied closely to their work?
- Which users need more help with confidence?
- Where is managerial support the main missing factor?
- Which groups need clearer usage boundaries?
- Who would benefit most from peer support?
- How should we vary support without creating unnecessary complexity?
Identifying High-Value Organizational GenAI Opportunities
Collecting Potential GenAI Opportunities Across the Organization
- Where are people already seeing possible GenAI uses?
- Which teams have identified opportunities but not tested them?
- What recurring problems could GenAI potentially support?
- Which opportunities appear across several functions?
- What ideas are currently hidden in individual teams?
- How can we capture opportunities without creating a bureaucratic process?
- What does the initial organization-wide opportunity landscape look like?
Identifying Repetitive Work That GenAI Could Support
- Which recurring tasks consume substantial human effort?
- Where does similar work happen repeatedly?
- Which repetitive tasks still require language or judgment?
- What parts of the work could GenAI prepare?
- Where could GenAI reduce rework?
- Which repetitive tasks are too low-value to justify special attention?
- Which patterns are most promising for practical support?
- Where do people spend significant time reading or searching?
- Which tasks require combining information from many sources?
- Where is relevant information difficult to summarize quickly?
- Which activities depend on understanding large document sets?
- Where could GenAI improve access to existing knowledge?
- What information constraints could limit these opportunities?
- Which information-intensive workflows offer the strongest potential?
Identifying Decisions That Could Benefit From Better Preparation
- Which decisions require substantial preparation?
- Where is evidence gathered inconsistently?
- Which decisions would benefit from more options being considered?
- Where are assumptions rarely challenged?
- Which decisions suffer from information overload?
- Where could GenAI improve preparation without influencing accountability?
- Which decision contexts are worth testing first?
Finding Opportunities to Improve Communication and Coordination
- Where does communication consume disproportionate effort?
- Which recurring messages are difficult to prepare?
- Where do teams repeatedly clarify the same information?
- Which handoffs create avoidable confusion?
- Where could GenAI help adapt information for different audiences?
- Which coordination problems are actually process problems rather than communication problems?
- Which communication opportunities could create visible value quickly?
Identifying Opportunities That Could Improve Quality
- Where does quality vary significantly between people or teams?
- Which outputs frequently need revision?
- Where are important checks performed inconsistently?
- Which work could benefit from additional critique or review?
- Where could GenAI help people identify missing information?
- Which quality improvements would matter most to customers or decision-makers?
- Which opportunities are practical without creating excessive review burden?
Identifying Work That Becomes Practical Only With GenAI
- What valuable work is currently skipped because it takes too much effort?
- Which analysis would people perform if it were easier?
- What personalization is currently too expensive?
- Which preparation work is routinely avoided because time is limited?
- What information could be processed at a scale that was previously impractical?
- Which new activities would create real value rather than additional work?
- What newly practical work deserves experimentation?
Comparing Opportunities by Potential Value and Feasibility
- What value could each opportunity realistically create?
- How difficult would each opportunity be to implement in practice?
- What information or tool access does each opportunity require?
- How much user behavior would need to change?
- Which opportunities carry significant quality or risk concerns?
- Where do high value and high feasibility overlap?
- Which opportunities deserve closer consideration?
Identifying Opportunities That Could Demonstrate Value Early
- Which opportunities can be tested quickly?
- Where would improvements be visible to users?
- Which opportunities need little additional infrastructure?
- What use cases have clear before-and-after comparisons?
- Which opportunities matter enough to build credibility?
- What early opportunities could mislead us because they are unusually easy?
- Which candidates could demonstrate meaningful value without overpromising?
Creating an Organization-Wide View of GenAI Opportunity Areas
- Which major opportunity areas appear across the organization?
- Where do several use cases share the same underlying pattern?
- Which opportunities are role-specific and which are broadly transferable?
- What opportunity areas are currently underexplored?
- Where are we concentrating too heavily on obvious uses?
- How does the opportunity landscape connect to organizational priorities?
- What overall picture should leadership use when setting priorities?
Anticipating Adoption Barriers
Identifying Where Access Could Prevent Adoption
- Which users may lack access to suitable GenAI tools?
- Where are account or permission processes likely to create friction?
- Which roles need features they currently cannot access?
- What access differences could create unequal adoption?
- Where might access technically exist but remain impractical?
- What support will users need when access problems arise?
- Which access barriers must be addressed before expecting adoption?
Anticipating Uncertainty About What Is Allowed
- Which everyday GenAI situations are likely to create uncertainty?
- What information are employees most unsure about using?
- Which tools may cause confusion about approved use?
- Where could unclear rules discourage otherwise safe experimentation?
- Which questions will managers probably receive?
- What misunderstandings could lead to overly cautious behavior?
- What guidance must be clear before adoption expands?
Identifying Where People May Lack Relevant GenAI Skills
- Which basic skills are necessary for useful GenAI use?
- Which groups lack experience developing results through conversation?
- Where will people struggle to provide enough context?
- Who may have difficulty evaluating output quality?
- Which users need help recognizing appropriate GenAI uses?
- What skills can be learned naturally through real work?
- Which capability gaps could materially slow adoption?
Anticipating Difficulty Seeing Personal Relevance
- Which roles may struggle to see how GenAI applies to their work?
- Where are examples too generic to feel useful?
- Which groups may associate GenAI only with writing?
- What recurring situations could make GenAI more concrete?
- Where might people believe their work is too specialized?
- What evidence would make the relevance easier to see?
- Which groups need role-specific opportunity discovery most?
Identifying Trust and Quality Concerns
- Where are users most likely to distrust GenAI output?
- Which tasks require particularly high accuracy?
- What past experiences could reduce confidence?
- Where might people mistake fluent output for reliable output?
- Which review practices could address legitimate quality concerns?
- What concerns can be reduced through experience rather than explanation?
- Which trust issues could meaningfully block adoption?
Understanding Where People May Lack Time to Experiment
- Which teams are already operating at full capacity?
- Where is experimentation likely to be treated as extra work?
- Which managers may not protect time for learning?
- How much effort does useful experimentation actually require?
- What existing work could provide the experimentation opportunity?
- Where can learning happen inside normal tasks instead of separately?
- Which time constraints need explicit leadership action?
Anticipating Weak Managerial Support
- Which managers may not understand the value of GenAI?
- Who may be uncomfortable encouraging a tool they rarely use?
- Where could managers unintentionally discourage experimentation?
- Which management expectations leave little room for learning?
- What concerns could make managers resistant?
- What support would help managers lead adoption more effectively?
- Where is weak managerial support most likely to affect adoption?
Identifying Workflow Friction That Could Discourage Use
- Where does using GenAI require too many extra steps?
- Which workflows force users to copy information manually?
- Where is GenAI disconnected from the tools people already use?
- Which tasks require information users cannot easily provide to GenAI?
- Where could review requirements erase the time saved?
- What friction could make old methods feel easier?
- Which workflow barriers should be addressed early?
Anticipating Where Early Failure Could Reduce Confidence
- Which first-use situations are likely to produce weak results?
- Where could users choose tasks that are too difficult initially?
- What kinds of mistakes could create disproportionate distrust?
- Which users are least likely to try again after a poor experience?
- How can early experimentation include realistic expectations?
- What support could help users recover from weak results?
- Which early experiences should we deliberately design more carefully?
Assessing Which Barriers Could Have the Greatest Effect on Adoption
- Which barriers affect the largest number of people?
- Which barriers could completely prevent useful GenAI use?
- What problems are likely to disappear naturally with experience?
- Which barriers require organizational action rather than user learning?
- What barriers interact with each other?
- Where would solving one barrier unlock several others?
- Which barriers deserve the closest attention in the adoption approach?
Defining the Intended Adoption Outcome
Clarifying Which Adoption Problem Needs to Be Solved
- What is actually wrong with the current level of GenAI adoption?
- Is the main problem access, usage, capability, relevance, or integration?
- Which evidence shows that this is a real problem?
- Who is most affected by the current gap?
- What would happen if nothing changed?
- Which symptoms should not be mistaken for the core problem?
- What precise adoption problem should the organization address?
Defining Which People the Adoption Effort Should Reach
- Who needs to change behavior for the adoption effort to succeed?
- Which roles are most relevant to the intended outcome?
- Does the effort need to reach everyone at the same time?
- Which groups have the strongest opportunity to benefit?
- Who could reasonably remain outside the initial scope?
- What managers or support roles also need to be included?
- How broad should the intended reach actually be?
Defining How GenAI Use Should Change Everyday Work
- What should people do differently in their normal work?
- Which recurring situations should trigger consideration of GenAI?
- What kinds of work should become easier or better?
- Where should GenAI remain optional?
- What behaviors would indicate meaningful integration?
- Which existing working habits need to change?
- What should everyday work look like when adoption is successful?
Describing What Successful Adoption Should Look Like
- What would we observe if adoption were genuinely successful?
- How would regular users behave differently from today?
- What would managers notice in their teams?
- How would work outputs or processes change?
- What would employees say about using GenAI?
- Which signs would distinguish real adoption from superficial usage?
- What concise description should define success?
Identifying the Business Outcomes Adoption Should Support
- Which business outcomes should GenAI adoption contribute to?
- Is the main value expected in productivity, quality, speed, or capability?
- Which outcomes are realistic within the intended timeframe?
- How directly can adoption influence those outcomes?
- What business priorities should adoption support rather than distract from?
- Which outcomes would justify continued investment?
- What business value should remain central throughout the effort?
Defining the Desired Level of Independent GenAI Use
- How independently should employees recognize GenAI opportunities?
- How much should users rely on templates or predefined use cases?
- What should people be able to do without specialist support?
- How confidently should they review GenAI outputs?
- Where should expert help remain appropriate?
- What level of independence is realistic for different groups?
- What would independent GenAI use look like in practice?
Clarifying What Adoption Is Not Intended to Achieve
- What problems should this adoption effort not try to solve?
- Which broader AI transformation topics are outside its scope?
- What should remain a separate governance or technology initiative?
- Which expectations would make the adoption effort unrealistic?
- What work should not be pushed toward GenAI use?
- Where could excessive ambition weaken the core adoption goal?
- What boundaries should leadership state clearly?
Defining the Desired Breadth and Depth of GenAI Adoption
- How widely should GenAI be used across the organization?
- How many roles need meaningful adoption?
- Should success mean frequent use or broader situational awareness?
- How deeply should GenAI become integrated into recurring work?
- Which groups need deeper use than others?
- What level of breadth and depth is realistic?
- How will we know when adoption has moved beyond isolated pockets?
Defining Evidence That Would Indicate Meaningful Progress
- What evidence would show that more people are using GenAI?
- What would show that usage is becoming more independent?
- How could we see that work situations are changing?
- What evidence would indicate growing confidence?
- Which qualitative signs matter alongside usage data?
- What evidence would show that GenAI is creating value?
- What progress indicators are meaningful enough to guide decisions?
Creating a Clear Adoption Outcome for the Organization
- How can we express the adoption outcome in plain language?
- Does the outcome describe behavior rather than tool deployment?
- Does it connect adoption to real work?
- Is the outcome specific enough to guide priorities?
- Does it avoid promising benefits we cannot guarantee?
- Can leaders and employees understand the same intended direction?
- What final wording should anchor the adoption effort?
Setting Adoption Priorities
Identifying Which User Groups Should Receive Attention First
- Which groups have the strongest potential to benefit?
- Who currently faces the largest adoption gap?
- Which users could learn quickly with limited support?
- Where could early progress create useful examples for others?
- Which groups require support before broader adoption can work?
- What risks come from focusing too heavily on eager early adopters?
- Which groups should receive attention first?
Selecting Which Teams or Functions to Focus On
- Which teams have work especially suited to GenAI?
- Where is leadership support already strong?
- Which functions face obvious information or productivity problems?
- Where could early learning transfer elsewhere?
- Which teams have enough capacity to experiment?
- What important areas should not be ignored simply because adoption is harder?
- Which functions form the strongest initial focus?
Prioritizing the Most Valuable GenAI Opportunities
- Which opportunities could create the greatest practical value?
- Which opportunities affect important recurring work?
- Where is feasibility high enough for near-term action?
- Which opportunities require major dependencies first?
- What opportunities could create strong learning even if value is uncertain?
- Which opportunities are attractive but premature?
- What should sit at the top of the opportunity priorities?
Deciding Which Adoption Barriers Need Early Attention
- Which barriers could stop adoption before it starts?
- What barriers affect multiple user groups?
- Which barriers are easiest to remove quickly?
- What barriers require leadership decisions?
- Which barriers will become more important as adoption expands?
- What can reasonably wait until later?
- Which barriers must be addressed in the first phase?
Balancing Quick Wins With Longer-Term Capability Building
- Which quick wins can demonstrate value early?
- What capability investments will matter beyond initial experiments?
- Could too much focus on quick wins keep use shallow?
- Which early successes also build transferable skills?
- What long-term needs should begin before immediate results appear?
- How should resources be split between visible wins and capability?
- What balance best supports sustainable adoption?
Prioritizing Areas Where Leadership Support Is Most Needed
- Where does adoption depend on explicit leadership action?
- Which barriers cannot be solved by employees themselves?
- What teams need leaders to protect experimentation time?
- Where is manager skepticism limiting adoption?
- Which organizational decisions need senior sponsorship?
- Where could leadership involvement add little value?
- Which areas deserve the strongest visible leadership attention?
Deciding Where Limited Support Resources Should Be Used
- Which groups need hands-on support most?
- Where would additional support unlock the greatest adoption progress?
- What support activities are consuming resources without much effect?
- Which needs can be handled through peer support?
- Where is specialist expertise genuinely necessary?
- How should support be distributed across early and later adopters?
- What allocation makes the best use of limited capacity?
Sequencing Adoption Efforts Across the Organization
- What needs to happen before broader adoption can begin?
- Which teams can progress in parallel?
- What dependencies determine the sequence?
- When should capability building begin relative to experimentation?
- When should successful practices start spreading?
- What should wait until measurement or learning improves?
- What sequence creates a coherent path from start to scale?
Identifying Lower-Priority Activities That Can Wait
- Which activities sound useful but are not necessary yet?
- What work can be postponed without hurting adoption?
- Which initiatives depend on maturity we do not yet have?
- Where are we at risk of overengineering the adoption effort?
- What activities create complexity without immediate value?
- Which ideas can be revisited after early learning?
- What should deliberately stay out of the first phase?
Defining the First Adoption Priorities to Act On
- What are the first few priorities that need action?
- Why do these priorities matter now?
- What concrete outcome should each priority produce?
- Who needs to own the initial action?
- What dependencies must be addressed first?
- What can begin immediately with existing resources?
- What should leadership communicate as the initial adoption focus?
Developing the Adoption Approach
Choosing How Adoption Should Begin
- Should adoption begin with selected teams or broadly across the organization?
- What initial experience should employees have with GenAI?
- Which real work should anchor the first phase?
- How much structure do users need at the beginning?
- What risks come from launching too broadly?
- What risks come from starting too narrowly?
- What starting approach best fits our organization?
Deciding How Much Direction Should Come From the Center
- Which adoption elements need organization-wide consistency?
- What decisions should remain with central leadership?
- Where would central direction help users?
- Where could central control slow useful experimentation?
- What should teams be free to adapt?
- How can central support enable rather than dictate?
- What balance between central direction and local ownership makes sense?
Deciding How Much Freedom Teams Should Have to Experiment
- What kinds of experiments should teams be free to run?
- Which boundaries need to remain common?
- Where could local experimentation uncover valuable new methods?
- What risks increase if every team works differently?
- How should teams share what they learn?
- When should a local practice become organization-wide?
- What level of experimentation freedom supports adoption best?
Designing How Learning Should Happen Through Real Work
- Which real tasks can people use to learn GenAI?
- How can learning happen during normal work rather than separate exercises?
- What kinds of work are suitable for early learning?
- How should people reflect on what worked or failed?
- How can managers support learning without turning it into training administration?
- What knowledge should be shared after real experiments?
- How can everyday work become the main learning environment?
Determining How Support Should Be Provided
- What kinds of support will users need at different stages?
- When is self-service guidance enough?
- Where is peer support more useful than formal training?
- When do users need specialist help?
- How should support be easy to access during real work?
- What support models could become too expensive to sustain?
- What combination of support methods fits our users best?
Defining How Communication Should Support Adoption
- What does communication need to accomplish at each stage?
- Which messages should come from senior leadership?
- What should managers explain locally?
- How can communication remain practical rather than promotional?
- What concerns need direct acknowledgment?
- How often should messages be reinforced?
- What communication approach will keep adoption understandable and relevant?
Deciding How Managers Should Participate
- What should managers personally do to support adoption?
- How much GenAI experience do managers need themselves?
- What adoption conversations should managers have with their teams?
- How should they create space for experimentation?
- What should managers reinforce during normal work?
- Which responsibilities should not be pushed onto line managers?
- What realistic manager role best supports adoption?
Connecting the Adoption Approach to Existing Organizational Structures
- Which existing routines can support GenAI adoption?
- What current learning structures can be reused?
- Where can adoption fit into existing team meetings?
- Which communication channels already reach the right people?
- What current structures would create unnecessary friction?
- Where should adoption remain separate from existing processes?
- How can we use what already exists instead of building parallel machinery?
Sequencing the Main Elements of the Adoption Approach
- Which elements need to happen first?
- What can occur in parallel?
- When should access, communication, and experimentation begin?
- When should peer support and team methods develop?
- At what point should measurement become more formal?
- When is the organization ready to scale successful patterns?
- What sequence creates the most natural adoption progression?
Preparing the Adoption Approach to Evolve With Experience
- Which parts of the approach should remain flexible?
- What assumptions should we explicitly expect to revisit?
- How will early user feedback change the approach?
- What evidence should trigger adjustments?
- Which adoption activities should be treated as experiments themselves?
- How can we avoid locking into methods too early?
- What mechanism will keep the approach responsive over time?
Building Leadership Alignment
Understanding How Different Leaders Currently View GenAI
- How does each leader currently understand GenAI?
- Who sees GenAI primarily as a productivity tool?
- Who sees broader strategic implications?
- Which leaders remain skeptical?
- Where do views differ because of different business contexts?
- What misconceptions are shaping leadership opinions?
- What does the current leadership perspective landscape look like?
Identifying Differences in Leadership Expectations
- What outcomes does each leader expect from adoption?
- How quickly do different leaders expect results?
- Where do expectations differ about risk?
- Who expects centralized control versus local experimentation?
- What different assumptions exist about employee behavior?
- Which expectations are unrealistic?
- What expectation gaps need to be resolved before moving forward?
Building a Shared Understanding of Why Adoption Matters
- What reasons for adoption can all leaders support?
- Which organizational priorities does GenAI connect to?
- What evidence makes the case most credible?
- How should we distinguish real value from hype?
- What happens if leaders emphasize different reasons publicly?
- Which concerns need acknowledgment before alignment is possible?
- What shared rationale should anchor leadership discussion?
Aligning Leaders on the Intended Adoption Outcome
- Do leaders agree on what successful adoption looks like?
- How broad should adoption become?
- How deeply should GenAI enter everyday work?
- What level of independent use is expected?
- Which business outcomes matter most?
- What outcomes are outside the scope?
- What common adoption outcome can leadership support?
Aligning Leaders on Adoption Priorities
- Which priorities do leaders currently disagree on?
- What evidence supports each priority?
- Which teams or functions should come first?
- How should quick wins be balanced against long-term capability?
- What barriers deserve leadership attention?
- Which activities can wait?
- What priority set can leaders commit to consistently?
Resolving Differences About Speed and Risk
- Where do leaders disagree about how quickly to move?
- What risks concern the more cautious leaders?
- What opportunities worry faster-moving leaders about delay?
- Which risks can be reduced through controlled experimentation?
- Where would excessive caution create its own risk?
- What pace allows learning without unnecessary exposure?
- What compromise on speed and risk is practical?
Clarifying Leadership Roles in the Adoption Effort
- What should senior leadership own?
- What should functional leaders own?
- What should line managers own?
- Where are leadership responsibilities currently unclear?
- Which responsibilities overlap unnecessarily?
- What decisions require explicit executive ownership?
- How should leadership roles be communicated?
Aligning Leaders on Resources and Support
- What resources does the adoption effort actually require?
- Which support capacity already exists?
- Where are additional resources necessary?
- What does each leader expect other functions to provide?
- Which resource requests are premature?
- How should limited support be allocated?
- What commitments do leaders need to make now?
Agreeing on Consistent Messages for the Organization
- What core messages should all leaders reinforce?
- Which statements would create confusion if leaders phrase them differently?
- How should leaders explain both opportunity and responsibility?
- What should be said about job and role changes?
- How should uncertainty be communicated?
- Which messages need adaptation without changing their meaning?
- What common language should leadership use?
Resolving Remaining Leadership Disagreements Before Moving Forward
- What leadership disagreements remain unresolved?
- Which disagreements materially affect adoption?
- What evidence could help resolve them?
- Which issues can remain open without blocking action?
- What decisions need explicit escalation?
- Where can experimentation replace prolonged debate?
- What must leadership agree on before the next phase begins?
Demonstrating Leadership Commitment
Showing Visible Leadership Interest in GenAI Adoption
- How can leaders show that GenAI adoption genuinely matters?
- What visible behaviors would employees notice?
- How often should leaders discuss adoption?
- Where should leaders ask about GenAI during normal business discussions?
- What signals would make support feel performative rather than genuine?
- How can leaders stay visible without dominating the effort?
- What leadership behaviors would create credible commitment?
Using GenAI Personally in Relevant Leadership Work
- Which leadership tasks could benefit from GenAI?
- Where could a leader use GenAI during normal preparation?
- What decisions could GenAI help a leader think through?
- Which uses would demonstrate realistic rather than theatrical adoption?
- How should leaders discuss their own mistakes and learning?
- What leader use cases are appropriate to share?
- How can personal use strengthen leadership credibility?
Making Time for Teams to Experiment With GenAI
- Where can teams experiment within existing work?
- How much protected time is actually needed?
- Which current activities could be reduced to make room?
- How should managers signal that experimentation is legitimate work?
- What happens when delivery pressure competes with learning?
- How can experimentation avoid becoming an extra project?
- What practical leadership action would create enough space?
Removing Barriers That Leaders Can Directly Influence
- Which barriers require a leadership decision?
- What approval delays could leaders remove?
- Where can leaders simplify access?
- What organizational uncertainty can leadership clarify?
- Which resource constraints can be addressed directly?
- What barriers are outside leadership control?
- Which leadership actions would have the greatest immediate effect?
Asking Teams About GenAI Opportunities in Their Work
- What questions can leaders ask to surface GenAI opportunities?
- Which recurring tasks should teams examine?
- Where are employees already experimenting?
- What problems do teams wish were easier?
- Which suggestions are coming from people closest to the work?
- How can leaders listen without turning opportunity discovery into reporting?
- What should leaders do with the opportunities teams identify?
Following Up on Adoption Progress Regularly
- What should leaders ask about during follow-up?
- How often is follow-up useful without becoming intrusive?
- What evidence should leaders review?
- Which problems need escalation?
- What lessons from teams deserve broader attention?
- How can follow-up focus on learning rather than compliance?
- What should leaders do differently based on what they hear?
Supporting People When Early Experiments Do Not Work
- How should leaders react when an experiment fails?
- What can be learned from a poor result?
- Was the task unsuitable or was the approach weak?
- How can leaders prevent one failure from reducing confidence?
- When should an experiment be abandoned?
- What should be tried differently next time?
- How can leaders normalize responsible learning from failure?
Recognizing Useful GenAI-Supported Work
- What kinds of GenAI-supported work deserve recognition?
- Should recognition focus on usage or on business value?
- How can leaders avoid rewarding superficial AI activity?
- What examples demonstrate good human judgment?
- How can recognition encourage others to experiment?
- What recognition methods fit the organization's culture?
- How should leaders make valuable practices more visible?
Maintaining Attention After the Initial Launch
- What usually causes leadership attention to fade?
- Which adoption indicators should remain on the leadership agenda?
- How can leaders reinforce adoption during normal business reviews?
- What new examples can refresh interest?
- When does adoption need renewed leadership intervention?
- How can attention continue without creating another permanent initiative?
- What cadence would sustain leadership involvement?
Demonstrating Consistent Commitment Through Leadership Behavior
- Do leadership actions match leadership messages?
- Where are leaders unintentionally sending contradictory signals?
- Are managers rewarded for making time for adoption?
- Does leadership tolerate responsible experimentation?
- Are leaders changing their own working methods?
- What behaviors would employees interpret as lack of commitment?
- How can leadership commitment become visible through normal decisions?
Communicating Why GenAI Matters
Developing a Clear Explanation of Why GenAI Matters Now
- Why does GenAI matter to this organization now?
- What has changed enough to justify attention?
- Which business pressures make adoption relevant?
- What practical opportunities are already visible?
- What should we avoid claiming?
- How can the explanation remain understandable to non-specialists?
- What concise message should people remember?
Connecting GenAI Adoption to Organizational Priorities
- Which existing priorities can GenAI adoption support?
- Where does GenAI help work already considered important?
- What organizational goals should not be distorted to fit the AI narrative?
- How can teams see the connection to their own objectives?
- Which examples make the connection credible?
- Where would adoption compete with existing priorities?
- How should leaders explain GenAI as part of the broader strategy?
Explaining the Practical Value of GenAI Without Exaggeration
- What benefits can we credibly claim today?
- Which value claims require stronger evidence?
- How should we discuss productivity without promising dramatic savings?
- What limitations should be stated openly?
- Which examples show practical value best?
- How can we avoid both hype and excessive caution?
- What language creates realistic expectations?
Showing How GenAI Can Improve Everyday Work
- Which everyday problems can GenAI help with?
- What familiar tasks make good examples?
- How can examples show more than drafting and summarization?
- Which examples are relevant across many roles?
- How can we show the human role alongside GenAI?
- What examples would feel unrealistic to employees?
- Which practical stories best make the value tangible?
Addressing Common Misunderstandings About GenAI
- What do employees most commonly misunderstand?
- Which misconceptions about accuracy need correction?
- Where do people overestimate automation?
- Where do they underestimate GenAI's usefulness?
- What misunderstandings exist about privacy or approved tools?
- Which explanations are simple enough to remember?
- How can communication correct misconceptions without becoming technical training?
Explaining What GenAI Adoption Means for Employees
- What will employees actually be expected to do differently?
- Will everyone be expected to use GenAI in the same way?
- What learning will employees need?
- How will responsibility for work remain with people?
- What changes might employees notice first?
- Which future changes remain uncertain?
- How can we explain adoption without making unsupported promises about roles?
Clarifying What GenAI Adoption Does Not Mean
- Does adoption mean replacing every existing working method?
- Does it mean employees must use GenAI for every task?
- Does it mean GenAI will make decisions for people?
- Does adoption require immediate workflow redesign?
- What fears can be reduced by stating clear boundaries?
- Which expectations should leadership explicitly reject?
- What should employees understand adoption is not?
Adapting the Adoption Message for Different Audiences
- What matters most to senior leaders?
- What matters most to managers?
- What concerns frontline employees?
- Which examples fit different functions?
- What level of technical detail does each audience need?
- Which core messages should remain unchanged?
- How can the message adapt without becoming inconsistent?
Preparing Managers to Explain GenAI Adoption to Their Teams
- What do managers need to understand before talking to their teams?
- What questions are employees likely to ask?
- Which concerns should managers answer directly?
- What should managers avoid speculating about?
- Which examples can managers use for their own team's work?
- When should managers escalate questions they cannot answer?
- What simple structure can help managers lead the conversation?
Reinforcing the Adoption Message Over Time
- Which messages need repeating after the initial communication?
- How should communication change as adoption matures?
- What new examples can keep the message relevant?
- When should leaders acknowledge problems or setbacks?
- How can communication reflect what users are actually experiencing?
- What signs indicate the original message no longer fits?
- How should the adoption narrative evolve with experience?
Making GenAI Relevant to Individual Roles
Understanding the Work of a Specific Role
- What does this role spend most of its time doing?
- Which tasks require substantial reading or writing?
- Where does the role make recurring judgments?
- What preparation work does the role perform?
- Which responsibilities involve coordination with others?
- What recurring frustrations does the role experience?
- Which parts of the role are most promising for GenAI support?
Identifying Role-Specific GenAI Opportunities
- Which tasks in this role could GenAI help prepare?
- Where could GenAI improve analysis?
- What communication work could GenAI support?
- Which recurring decisions could benefit from additional thinking support?
- Where could GenAI reduce repetitive effort?
- Which opportunities require information the tool cannot access?
- What role-specific opportunities deserve experimentation?
Connecting GenAI to Recurring Responsibilities
- Which responsibilities occur often enough for habits to form?
- Where does the same type of work repeat?
- Which recurring responsibilities currently take disproportionate effort?
- Where could GenAI become a normal part of preparation?
- Which responsibilities require too much human judgment for routine GenAI use?
- What cues could remind people to consider GenAI?
- Which recurring responsibilities should anchor adoption in this role?
- What examples mirror the work this role performs every week?
- Which examples solve a recognizable frustration?
- What examples show more than generic writing support?
- Which examples are simple enough to try immediately?
- Where can examples use familiar documents or situations?
- What examples would seem artificial or unrealistic?
- Which examples are most likely to trigger personal experimentation?
Showing Where GenAI Can Reduce Low-Value Effort
- Which tasks consume time without using the role's strongest skills?
- What repetitive structuring work could GenAI handle?
- Where do people repeatedly rewrite similar material?
- Which preparation steps add little unique human value?
- What effort could be reduced without weakening quality?
- Where might reducing effort simply create more unnecessary work?
- Which low-value activities are strongest candidates?
Showing Where GenAI Can Improve the Quality of the Role's Contribution
- Where could GenAI help people consider more options?
- Which outputs would benefit from stronger review?
- Where could GenAI challenge assumptions?
- What work could benefit from better structure or clarity?
- Where could users prepare more thoroughly?
- Which quality improvements matter most to the role's stakeholders?
- How could GenAI strengthen contribution rather than merely save time?
Clarifying Where Human Judgment Remains Essential
- Which parts of this role require experience GenAI cannot replace?
- What decisions must remain with the role holder?
- Where does accountability sit regardless of GenAI involvement?
- Which situations require contextual knowledge the model may lack?
- What outputs need careful professional review?
- Where could overreliance create significant risk?
- How should users combine GenAI support with their own judgment?
Connecting Organizational Adoption Goals to Individual Work
- What do organizational adoption goals mean for this role?
- Which expected behaviors are actually relevant here?
- What work changes would support the broader adoption outcome?
- Which organizational goals do not translate directly to this role?
- How could this role contribute to wider learning?
- What would successful adoption look like for this individual?
- How can the connection be explained without making it abstract?
Preparing Role-Specific Conversations About GenAI
- What should a manager discuss with people in this role?
- Which opportunities should the conversation explore?
- What concerns are likely to arise?
- Which examples should be available?
- What questions can help employees identify their own opportunities?
- What should the conversation avoid prescribing?
- How can the discussion end with useful experimentation rather than theory?
Updating Role-Specific Opportunities as GenAI Capabilities Improve
- Which existing role opportunities have become easier?
- What new capabilities create new uses for this role?
- Which old limitations no longer apply?
- What current working methods may now be outdated?
- Which new opportunities need testing before broader use?
- What examples should be retired or replaced?
- How should the role's opportunity map evolve over time?
Managing Workforce Concerns and Uncertainty
Identifying the Main Concerns Employees Have About GenAI
- What are employees actually worried about?
- Which concerns appear most frequently?
- How do concerns differ across roles?
- Which concerns are based on real organizational uncertainty?
- Which concerns come from misunderstandings?
- What concerns are people reluctant to voice openly?
- What overall concern landscape should leadership understand?
Understanding Concerns About Job and Role Changes
- Which employees believe GenAI threatens their role?
- What tasks do they expect to disappear?
- Which role changes are genuinely plausible?
- What changes remain too uncertain to predict?
- How might GenAI shift rather than eliminate responsibilities?
- What information can leadership responsibly provide now?
- How should uncertainty about future roles be discussed?
Addressing Anxiety About New Skill Expectations
- What new GenAI skills do employees think they are expected to have?
- Which expectations are actually necessary?
- Who feels behind compared with colleagues?
- What skills can be learned gradually through work?
- How can leaders avoid making GenAI competence feel like a sudden test?
- What support would reduce anxiety?
- How should capability expectations be communicated?
Responding to Concerns About Quality and Reliability
- What kinds of GenAI errors concern employees most?
- Which concerns are justified by their work?
- Where does review already protect quality?
- What new review practices may be needed?
- How can users learn to recognize unreliable outputs?
- Which tasks should not rely heavily on GenAI?
- What would help employees trust GenAI appropriately rather than blindly?
Addressing Concerns About Monitoring or Evaluation
- Do employees believe their GenAI usage will be monitored?
- What data is actually being collected?
- How might usage data be interpreted?
- Could adoption metrics create pressure for performative use?
- What should never be inferred from raw usage counts?
- How can measurement remain transparent?
- What should leadership communicate about monitoring and evaluation?
Understanding Fear of Losing Professional Expertise
- Which employees worry that GenAI will weaken their skills?
- What expertise could decline if people rely too heavily on GenAI?
- Which expertise becomes more important with GenAI?
- How can GenAI support learning rather than replace thinking?
- Where should manual practice remain important?
- What new forms of professional judgment may emerge?
- How can leaders address this concern credibly?
Discussing How Workloads May Change With GenAI
- Where could GenAI reduce current workload?
- Where might faster production create more expected output?
- What new review or coordination work could appear?
- Could GenAI shift work rather than reduce it?
- How should saved time be used?
- What workload expectations would undermine adoption?
- How can leaders discuss workload effects realistically?
Addressing Concerns About Fairness and Unequal Access
- Do all relevant employees have comparable access?
- Which groups may receive better tools or support?
- How could unequal access affect performance expectations?
- Are some roles getting more opportunity to learn?
- What fairness concerns arise from different job contexts?
- How should temporary inequalities be explained?
- What actions would reduce avoidable disparities?
Communicating Honestly Where the Future Remains Uncertain
- What do we genuinely know about future work changes?
- What important questions remain unanswered?
- Which predictions should leadership avoid making?
- How can uncertainty be acknowledged without creating unnecessary fear?
- What decisions can still be made despite uncertainty?
- When should employees expect updated information?
- What wording communicates uncertainty with credibility?
Creating Ongoing Dialogue About Workforce Concerns
- Where can employees raise concerns safely?
- How often should leaders invite feedback?
- Which concerns need individual rather than broad discussion?
- How can recurring concerns be tracked without turning dialogue into surveillance?
- What should managers do when they cannot answer?
- How should leadership respond to uncomfortable feedback?
- How can ongoing dialogue shape the adoption approach?
Enabling Practical Access to GenAI
- Which approved GenAI tools are available today?
- Who has access to each tool?
- What capabilities differ between tools?
- Which tools are available only to certain groups?
- What access restrictions matter for everyday work?
- Where are users relying on unapproved alternatives?
- What clear tool-access map do employees need?
Finding Groups That Lack Practical Access
- Who currently lacks access entirely?
- Which users have access that does not fit their work?
- Are there device or location constraints?
- Which groups cannot use important features?
- Where is access slower or more difficult than for others?
- How does lack of access affect adoption expectations?
- Which gaps need resolution first?
Understanding Where Access Processes Create Friction
- How many steps does a user need to gain access?
- Where do approvals slow the process?
- What information do users need but struggle to find?
- Which support requests occur repeatedly?
- Where do people abandon the process?
- What controls are necessary and what friction is avoidable?
- Which access steps could be simplified?
Making Initial Access Easy to Understand
- Do employees know which tool they should use?
- Can they find the access instructions easily?
- Are account setup steps clear?
- What common mistakes occur during onboarding?
- Which technical language confuses users?
- What information is unnecessary at the first access stage?
- What simple guidance would get users started faster?
- What should a new user do first?
- Which simple real tasks make good starting points?
- Do users know where to enter their first request?
- What do they need to know before uploading information?
- Where can they get help if the first interaction goes poorly?
- What should users avoid overthinking at the beginning?
- What minimum orientation is enough to start experimenting?
Identifying Technical or Device Constraints That Limit Use
- Which devices support the approved GenAI tools?
- Where does performance make use frustrating?
- Are browser or network restrictions creating barriers?
- Which features are unavailable on common devices?
- Do mobile and desktop experiences differ materially?
- Which technical limitations are temporary?
- What constraints must be solved before wider adoption?
Understanding Permission or Account Problems
- What permission issues are users encountering?
- Which account types lack required features?
- Where do identity or authentication problems occur?
- Which users need access to shared organizational resources?
- What problems require central support?
- Which permission requests are taking too long?
- How can recurring account issues be reduced?
- What internal information do users need for valuable GenAI use?
- Which information cannot currently be used with approved tools?
- Where does lack of access force users to provide context manually?
- What knowledge is trapped in separate systems?
- Which information access gaps most limit practical value?
- What access should not be expanded for security reasons?
- Which information gaps can be worked around, and which require a change in access?
Providing Clear Support When Access Problems Occur
- Where should users go when access fails?
- Can support distinguish tool problems from account problems?
- What common issues can users solve themselves?
- How quickly do access problems need resolution?
- Which recurring problems should be documented?
- How can support avoid sending users between teams?
- What support experience will prevent frustration from becoming disengagement?
Confirming That People Can Actually Use GenAI Before Expecting Adoption
- Does everyone in the target group have working access?
- Can they complete a basic interaction?
- Do they know which tool to use?
- Can they access the information needed for their work?
- What unresolved issues still block practical use?
- Are adoption expectations ahead of actual access?
- What must be confirmed before leadership evaluates usage?
Clarifying Safe and Practical GenAI Usage Boundaries
- Which GenAI tools are approved for organizational work?
- Are different tools approved for different purposes?
- What should employees do when a useful tool is not approved?
- How can they tell whether a new tool is permitted?
- Which personal accounts should not be used?
- Where do current tool rules remain ambiguous?
- What simple guidance would make tool choice clear?
- What information can safely be entered into approved GenAI tools?
- Which information categories require additional caution?
- What data should never be provided?
- How should employees handle mixed-sensitivity documents?
- Are there differences between internal and external tools?
- What examples would make the rules easier to apply?
- Where do employees still need clarification?
- What information is routine enough for normal GenAI use?
- What makes information sensitive in our organization?
- Which categories are easy to misclassify?
- How should employees handle uncertainty about sensitivity?
- What contextual factors change the classification?
- Which examples can illustrate the boundary?
- What decision rule would help users act consistently?
- What work must stay inside approved organizational tools?
- Which information creates the clearest restriction?
- Are there tasks where external tools are acceptable?
- How should users handle public information?
- What should happen when an external tool offers a useful capability we lack?
- Which risks are most important to explain?
- How can this boundary stay practical rather than overly broad?
Clarifying When GenAI Outputs Require Verification
- Which outputs should always be verified?
- When is light review enough?
- What kinds of facts require external confirmation?
- Which tasks carry higher consequences if GenAI is wrong?
- How can users recognize unsupported claims?
- What verification burden would make a use case impractical?
- What simple review expectations should users follow?
Clarifying Which Decisions Must Remain With People
- Which decisions require explicit human judgment?
- Where does accountability legally or organizationally remain with a person?
- What decisions can GenAI help prepare but not make?
- Which low-stakes choices could be more automated?
- How should users distinguish advice from a decision?
- What examples make the boundary clear?
- Where should human ownership be explicitly documented?
Understanding Additional Boundaries in Regulated or Sensitive Work
- What additional rules apply to this function?
- Which GenAI uses require specialist review?
- What information restrictions are unique to the work?
- Which decisions have formal regulatory requirements?
- Are there documentation obligations for GenAI-assisted work?
- Where do general organizational guidelines remain insufficient?
- What extra guidance does this group need?
Clarifying Intellectual Property and Confidentiality Considerations
- What copyrighted or proprietary material can be used?
- What confidential information needs special handling?
- Who owns outputs created with GenAI?
- What risks arise when using third-party content?
- How should users handle client or partner information?
- Which situations require legal guidance?
- What practical rules can employees apply without becoming legal experts?
Identifying Where Employees Need Further Guidance
- Which GenAI situations create the most uncertainty?
- What questions are repeatedly being asked?
- Which rules are interpreted differently across teams?
- Where are employees avoiding useful work because guidance is unclear?
- Which edge cases genuinely need additional policy clarification?
- What questions can managers answer themselves?
- Where should the organization provide clearer guidance next?
Making Usage Boundaries Easy to Apply in Everyday Work
- Can employees understand the boundaries without reading lengthy policy?
- What quick questions could guide everyday decisions?
- Which examples should be included in practical guidance?
- Where are current rules too abstract?
- How can guidance fit naturally into existing workflows?
- What should users do when they remain uncertain?
- How can safe use become easy enough to support adoption?
Helping People Recognize Useful GenAI Opportunities in Their Work
Helping People See Beyond the Most Obvious GenAI Uses
- What do people currently think GenAI is mainly for?
- Which useful capabilities are they overlooking?
- How can we show uses beyond drafting and summarizing?
- What examples reveal GenAI as a thinking partner?
- Where can GenAI help with preparation, comparison, or critique?
- Which examples would feel relevant rather than impressive?
- How can we expand people's mental model of GenAI use?
Connecting GenAI Opportunities to Recurring Work Situations
- What situations occur repeatedly in this role?
- Which situations involve information, uncertainty, or preparation?
- Where does the same problem appear week after week?
- What recurring moments could become GenAI cues?
- Which situations are frequent enough to build habits?
- How can users describe these situations naturally?
- What opportunity map would help GenAI come to mind more often?
- Where do people spend time reading large amounts of information?
- When do they need to summarize several sources?
- Where do they compare documents or versions?
- Which tasks require extracting specific details?
- Where do people struggle to see the main point?
- Which of these tasks are appropriate for GenAI?
- What cues could help users recognize them in the moment?
Helping People Recognize Moments of Uncertainty or Decision
- When do people feel unsure what to do next?
- Which decisions require options or tradeoff analysis?
- Where do assumptions need to be challenged?
- What moments trigger a need for additional perspective?
- When could GenAI help prepare rather than decide?
- Which uncertainties are too consequential for GenAI alone?
- How can uncertainty itself become a cue to consider GenAI?
Identifying Opportunities During Writing and Communication Work
- When do people struggle to start writing?
- Where do they need to adapt content for an audience?
- Which recurring messages take too long to prepare?
- When does feedback need to be incorporated?
- Where could GenAI improve clarity or structure?
- Which communications require strong human authorship?
- What writing situations should naturally trigger GenAI consideration?
Identifying Opportunities During Planning and Preparation
- What work requires repeated preparation?
- Where do people need agendas, checklists, or plans?
- Which tasks benefit from anticipating questions or risks?
- Where could GenAI help break work into parts?
- When could GenAI improve preparation quality?
- Which plans depend too heavily on unavailable context?
- What planning moments are useful GenAI triggers?
Identifying Opportunities During Meetings and Collaboration
- What could GenAI help prepare before a meeting?
- Where could it organize information after a discussion?
- When could it help clarify different viewpoints?
- How could it support decision preparation?
- Where could it help capture actions and responsibilities?
- What collaboration situations need confidentiality caution?
- Which meeting moments should make GenAI come to mind?
Helping People Recognize GenAI Opportunities in Problems They Already Face
- What problems frustrate people repeatedly?
- Which problems involve information they already have?
- Where are people stuck because they need another perspective?
- Which problems involve too many possible options?
- Where could GenAI help structure the problem before solving it?
- Which problems are actually outside GenAI's strengths?
- How can users turn existing frustrations into GenAI experiments?
Using Real Work Examples to Expand Opportunity Recognition
- Which real examples are most relatable to this audience?
- What situation triggered GenAI use in each example?
- What did the user ask GenAI to help with?
- Why was GenAI useful in that situation?
- What could another role learn from the underlying pattern?
- How can examples avoid becoming rigid templates?
- Which examples best encourage people to recognize their own opportunities?
Helping People Begin Recognizing New GenAI Opportunities Independently
- Are users beginning to think of GenAI without being prompted?
- What kinds of situations now trigger that thought?
- Where do users still depend on examples?
- Can they transfer a successful pattern to a new task?
- How can managers encourage independent opportunity recognition?
- What would indicate that the AI reflex is forming?
- How can support gradually decrease as recognition becomes habitual?
Supporting Early Experimentation
Choosing Real Work That Is Safe to Experiment With
- Which real tasks are low enough risk for early experimentation?
- What work is meaningful enough to motivate users?
- Which tasks can be easily reviewed afterward?
- Where is sensitive information unlikely to create problems?
- What tasks are too consequential for a first experiment?
- Which recurring work would allow repeated practice?
- What is the best kind of real work to begin with?
Selecting an Initial Task That Can Produce Visible Value
- Which task currently takes noticeable effort?
- Can improvement be seen quickly?
- Is the task simple enough for a new user?
- Does the task matter enough to feel worthwhile?
- Can the result be compared with the current method?
- What dependencies could make the experiment harder than necessary?
- Which initial task offers the clearest chance of a useful result?
Trying GenAI on Work That Would Otherwise Be Done Manually
- How is this task performed today?
- Which parts could GenAI help prepare?
- What information does GenAI need?
- How much human effort is still required?
- What difference does GenAI make to the process?
- Does the result remain acceptable after review?
- Is this use worth repeating?
Comparing GenAI-Assisted Work With the Existing Approach
- How long does the task take with and without GenAI?
- Does the GenAI-assisted version improve quality?
- What new review effort is required?
- Which parts become easier?
- Which parts become more complicated?
- What does the comparison reveal about where GenAI adds value?
- Should the GenAI-assisted method be tried again?
Improving Weak Initial Results Through Iteration
- What is weak about the first result?
- Did GenAI have enough context?
- Was the intended outcome clear?
- What follow-up question would improve the result most?
- Should the task be broken into smaller parts?
- At what point is further iteration no longer worthwhile?
- What does the improved interaction teach the user?
Trying Different Ways to Use GenAI on the Same Task
- What alternative approach could we try?
- Would more context improve the result?
- Would breaking the task into stages work better?
- Could GenAI critique rather than create the output?
- Could it compare alternatives instead of recommending one?
- Which interaction produces the most useful support?
- What general working pattern can we learn from the comparison?
Capturing What Was Learned From an Experiment
- What did we try?
- What worked better than expected?
- What failed or created extra work?
- What conditions mattered to the result?
- What should be done differently next time?
- Is the lesson specific to this task or more broadly useful?
- What is worth sharing with others?
Sharing Early Experiments With Colleagues
- Which experiment is worth showing others?
- What context do colleagues need to understand it?
- What was the original work problem?
- What did GenAI contribute?
- What limitations should be explained?
- How could colleagues adapt the approach?
- What feedback should we ask for after sharing it?
Deciding Which Experiments Are Worth Repeating
- Did the experiment create enough value?
- Was the result reliable enough?
- How much effort was required to get there?
- Would repeated use make the interaction easier?
- Does the task occur frequently enough?
- What unresolved issues could prevent reuse?
- Which experiments deserve another cycle?
Turning Successful Experiments Into Continued Use
- What made the experiment successful?
- How often does this situation recur?
- What parts of the interaction can be reused?
- What still requires judgment each time?
- How can the user remember to apply the method again?
- Should the approach be shared with the team?
- What would turn this isolated success into a working habit?
Building Initial GenAI Confidence
Helping Someone Complete Their First Useful GenAI Interaction
- What real task could give this person a useful first experience?
- What minimum context should they provide?
- What result would feel immediately valuable?
- How can the task remain simple enough to succeed?
- What should the user review before relying on the output?
- What can they learn from the first interaction?
- What natural next use could build on the success?
Recovering From a Weak First GenAI Result
- What exactly was disappointing about the result?
- Was the task suitable for GenAI?
- Was important context missing?
- Could a follow-up improve the result?
- Did expectations exceed current capability?
- What smaller task could restore confidence?
- How can one weak result become a learning experience rather than a stopping point?
Understanding Why a GenAI Interaction Did Not Work
- Was the request unclear?
- Did GenAI lack essential information?
- Was the task too broad?
- Did the user expect information GenAI could not know?
- Was the result weak because the model was unsuitable?
- What could be changed before trying again?
- What lesson should the user carry into future interactions?
Learning to Improve Results Through Follow-Up
- What part of the result should be improved first?
- What additional context would help?
- What should the user ask GenAI to change?
- How can the user challenge an unsatisfactory answer?
- When is a new conversation better than another follow-up?
- How much improvement is possible before diminishing returns?
- What follow-up pattern can the user reuse elsewhere?
Building Confidence in Reviewing GenAI Outputs
- What should the user check first?
- Which parts of the output are most likely to be wrong?
- Does the result fit the user's actual situation?
- What assumptions did GenAI make?
- Which facts require verification?
- What professional judgment should override the output?
- How can repeated review build appropriate confidence?
- Which claims matter enough to verify?
- What information could GenAI plausibly invent?
- When are current sources necessary?
- Which trusted sources can confirm the answer?
- How much verification is appropriate for this task?
- When is uncertainty acceptable?
- How can users make verification proportionate to consequence?
Expanding From Simple to More Difficult Tasks
- Which simple uses does the user already handle comfortably?
- What slightly more complex task is a natural next step?
- Which new skill does that task require?
- How can the work be broken into manageable parts?
- What additional review is needed?
- When is the task too complex for independent use?
- What progression will build capability without overwhelming the user?
Learning How to Respond When GenAI Makes a Mistake
- What kind of mistake occurred?
- How consequential is it?
- Can the error be corrected through follow-up?
- Does the mistake reveal missing context?
- Should the answer be independently verified?
- When should the user stop relying on GenAI for the task?
- How can mistakes improve judgment rather than destroy confidence?
Repeating Successful Uses Until They Feel Familiar
- Which successful use should the user repeat?
- How often does the situation occur?
- What part of the method can remain consistent?
- What context changes each time?
- Is the user becoming faster at starting the interaction?
- Does the user still need reminders or examples?
- When has the use become familiar enough to feel natural?
- Can the user start a useful conversation independently?
- Can they recognize when the result needs improvement?
- Can they decide what information to provide?
- Can they review the output critically?
- Can they try another approach when the first one fails?
- Do they know when to ask for help?
- What would show that independent confidence has been reached?
Developing Practical GenAI Capability
Describing Work Situations Clearly to GenAI
- What does GenAI need to know about this situation?
- Which background details materially affect the answer?
- What constraints should be stated?
- What information is irrelevant?
- How can the situation be explained in natural language?
- What assumptions should GenAI not have to guess?
- What description would give GenAI enough context to help?
Providing Enough Context for Useful Results
- What context is currently missing?
- Which documents or examples would improve the response?
- What audience information matters?
- What prior decisions should GenAI know?
- Which constraints change what a useful answer looks like?
- When does additional context become unnecessary?
- What is the smallest useful context package for the task?
Defining the Intended Outcome of a GenAI Interaction
- What do I actually want to achieve?
- What should the result help me do next?
- What would make the output useful?
- Which format best supports the intended use?
- What quality matters most?
- What should GenAI avoid optimizing for?
- How can the outcome be stated clearly before asking for help?
Developing Results Through Conversation
- What should be established before asking for a final result?
- Which parts of the work should be developed separately?
- What feedback should be given after each response?
- Where should assumptions be challenged?
- When should the user ask for alternatives?
- What should be reviewed before moving to the next stage?
- How can conversation produce a stronger result than a single prompt?
Working With Existing Documents and Source Material
- Which source material should GenAI rely on?
- What information is authoritative?
- What should GenAI ignore?
- What parts of the material need interpretation rather than summarization?
- How can source meaning be preserved?
- Where should unsupported additions be avoided?
- What instructions will keep the work grounded in the provided material?
Breaking Complex Work Into Manageable Interactions
- What are the major parts of the task?
- Which part should be handled first?
- What information must carry between stages?
- Which stages need separate review?
- Where could one large request produce an unreliable result?
- What stopping points would preserve user control?
- What sequence of interactions best supports the complex task?
Comparing and Challenging GenAI Outputs
- What assumptions does this output depend on?
- What alternative answer could also be reasonable?
- Where is the reasoning weakest?
- What evidence supports the conclusion?
- What important perspective may be missing?
- How would a knowledgeable critic challenge this?
- What should change before the output is trusted?
Using GenAI to Support Analysis and Decision Preparation
- What decision or analysis are we trying to prepare?
- Which facts should be organized first?
- What options need comparison?
- Which criteria matter?
- What risks or tradeoffs should be surfaced?
- What uncertainty could change the decision?
- How can GenAI strengthen preparation while leaving the decision with people?
Reusing Successful GenAI Working Patterns
- Which previous interaction worked particularly well?
- What underlying pattern made it successful?
- Which parts can be reused?
- What details need to change for the new situation?
- Where would reuse become too rigid?
- How can the pattern be made easier to remember?
- What other tasks could benefit from the same approach?
Adapting GenAI Methods Independently to New Work
- What familiar GenAI pattern resembles this new situation?
- Which parts of the old method still apply?
- What new context changes the interaction?
- What additional constraints matter?
- How should the user test whether the adapted method works?
- What can be learned from the first attempt?
- Can the user now design useful GenAI support without predefined examples?
Learning From Early Users
Identifying People Who Are Already Using GenAI Regularly
- Who uses GenAI frequently enough to have meaningful experience?
- Which users go beyond basic drafting?
- Who applies GenAI across several types of work?
- Which users can explain both successes and failures?
- Are regular users concentrated in particular teams?
- Who is willing to share practical experience?
- Which early users are most useful to learn from?
Collecting Examples of Useful Early GenAI Use
- What real tasks are early users using GenAI for?
- Which examples created noticeable value?
- What was the original work problem?
- How did the user interact with GenAI?
- What review was required?
- What limitations appeared?
- Which examples are worth documenting for others?
Understanding Why Certain Uses Became Successful
- What problem made the use valuable?
- How frequently did the situation occur?
- What user skill contributed to success?
- What information or tool access was available?
- Did managerial support matter?
- Which parts of the success could transfer elsewhere?
- What conditions should be replicated?
Identifying Problems Early Users Encounter
- What recurring problems are early users reporting?
- Which problems come from tool limitations?
- Where do users struggle with context or prompting?
- What access or information barriers appear?
- Which quality issues are common?
- What problems reduce continued use?
- Which issues need organizational attention?
Understanding Workarounds Early Users Have Developed
- What workarounds are users creating?
- What problem does each workaround solve?
- Are workarounds compensating for missing features?
- Which workarounds create unnecessary risk?
- What workarounds reveal unmet support needs?
- Which informal methods are effective enough to reuse?
- What should the organization learn from them?
Identifying Quality Problems in Early GenAI Use
- What quality failures occur most often?
- Are users reviewing outputs consistently?
- Which tasks produce unreliable results?
- Where are users accepting plausible answers too quickly?
- What context is commonly missing?
- Which quality problems can be addressed through better working methods?
- What needs to change before broader reuse?
Understanding Where Early Users Are Creating Measurable Value
- Which early uses save meaningful time?
- Where has output quality improved?
- What work is being completed faster?
- Which users are enabling work that was previously skipped?
- What value claims can actually be supported?
- Which effects are difficult to measure?
- Where is the strongest evidence of practical value?
Identifying Working Patterns That Others Could Reuse
- What interaction patterns recur across successful users?
- Which methods are simple enough for others to adopt?
- What context does reuse require?
- Which patterns depend heavily on specialist expertise?
- How should a pattern be explained without turning it into a rigid prompt?
- Where could the same pattern apply in another function?
- Which working patterns deserve wider sharing?
Using Early User Feedback to Improve Adoption Support
- What support are early users asking for?
- Which existing materials are not useful?
- What guidance is missing?
- Where do users need more role-specific examples?
- What support can be removed because users no longer need it?
- Which problems should be solved centrally?
- How should early feedback change the support model?
Identifying What the Organization Should Change Based on Early Experience
- What organizational friction is early use revealing?
- Which assumptions about adoption were wrong?
- What access arrangements need improvement?
- Which communication messages need adjustment?
- What learning methods are working poorly?
- Which successful practices should be reinforced?
- What changes should leadership consider before the next phase?
Developing Everyday GenAI Habits
Moving From Occasional GenAI Use to Regular Use
- What prevents users from returning to GenAI?
- Which recurring tasks could create regular use?
- What value makes users want to return?
- How can people avoid needing to remember GenAI deliberately?
- What simple routines could encourage repetition?
- When does regular use become unnecessary or forced?
- What conditions help occasional use become natural?
Identifying Recurring Moments When GenAI Should Come to Mind
- What recurring situations involve uncertainty?
- When do people need to understand information quickly?
- What tasks repeatedly require drafting or restructuring?
- Where do people compare options?
- Which moments involve preparation before action?
- What cues could make GenAI easier to remember?
- Which situations are frequent enough to build the AI reflex?
Using GenAI During Normal Preparation Work
- What does this person regularly prepare for?
- Which preparation tasks are repetitive?
- Where could GenAI generate questions or check gaps?
- What information can be organized before a meeting or task?
- Which preparation work benefits from challenge rather than drafting?
- What should remain under the user's own judgment?
- How can GenAI become a normal preparation option?
Using GenAI During Recurring Writing and Communication
- What communications are produced repeatedly?
- Where does starting from scratch consume unnecessary time?
- Which messages need frequent audience adaptation?
- Where could GenAI improve clarity?
- What communication still requires strong personal authorship?
- Which recurring writing situations are easiest to recognize?
- How can GenAI become part of the normal communication workflow?
Using GenAI During Analysis and Decision Preparation
- Which recurring analyses could GenAI help structure?
- Where could assumptions be challenged?
- What options need regular comparison?
- Which decisions benefit from better preparation?
- What evidence should remain independently verified?
- Where does GenAI support judgment without replacing it?
- How can this pattern become routine when similar situations arise?
Using GenAI Before and After Meetings
- What meeting preparation could GenAI improve?
- What questions should be considered beforehand?
- How can GenAI help organize notes afterward?
- Where can it clarify decisions and actions?
- What information should not be shared with GenAI?
- Which meeting types benefit most?
- How can meeting-related use become a recurring habit?
Continuing Useful GenAI Conversations Instead of Starting Over
- Which recurring topics benefit from retained context?
- When does an existing conversation contain useful background?
- What decisions or assumptions should remain visible?
- When has a conversation become too cluttered to continue?
- How can users keep related work together?
- What benefits come from building on prior interactions?
- When should users deliberately start fresh?
Reusing Successful GenAI Approaches in Similar Work
- Which approach worked well before?
- What new task resembles the earlier situation?
- Which parts can remain unchanged?
- What context needs updating?
- How can reuse reduce unnecessary experimentation?
- When would reuse create a poor fit?
- How can successful approaches become habitual without becoming rigid?
Building Team Cues That Encourage Regular GenAI Consideration
- What team routines could prompt people to consider GenAI?
- Could meetings include discussion of useful GenAI opportunities?
- What questions can managers ask during normal work?
- How can teams share examples without creating extra meetings?
- Which cues would feel forced or performative?
- How can cues fade as habits strengthen?
- What simple team practices could reinforce regular consideration?
Recognizing When GenAI Habits Are Beginning to Fade
- Is regular use declining?
- Which users have stopped returning to GenAI?
- What situations no longer trigger GenAI consideration?
- Has the perceived value changed?
- Are old barriers reappearing?
- What new examples or capabilities could refresh relevance?
- What should be reinforced before habits disappear completely?
Sharing Useful Practices Across Teams
Identifying GenAI Practices Worth Sharing
- Which practices consistently produce useful results?
- What value do they create?
- Are they repeatable by others?
- Do they depend heavily on local context?
- What review requirements make them safe?
- Which practices are too immature to share broadly?
- What makes a practice worth spreading?
Capturing Enough Context to Make an Example Understandable
- What problem was the user trying to solve?
- What work context shaped the approach?
- What information did GenAI receive?
- What did the user do after the first response?
- What limitations appeared?
- What result was ultimately useful?
- What context does another team need to judge whether the example applies?
Explaining Why a GenAI Practice Was Useful
- What changed because of the practice?
- Did it save time, improve quality, or enable new work?
- What would have happened without GenAI?
- Which part of the value came from the user rather than the tool?
- How repeatable is the benefit?
- What tradeoffs did the practice introduce?
- Why should another team care about it?
Showing How the Practice Worked in Real Work
- What was the actual work situation?
- How did the user begin the GenAI interaction?
- What information was provided?
- How was the result improved?
- What human review was applied?
- How did the output enter the real workflow?
- What should another user understand before trying it?
Selecting the Right Channels for Sharing Practices
- Who needs to see this practice?
- Where do those people already exchange work methods?
- Does the example belong in a team discussion or shared resource?
- How often should practices be surfaced?
- What channels risk overwhelming users?
- How can useful examples remain easy to find later?
- What sharing channel fits the practice best?
Avoiding Presenting Local Examples as Universal Solutions
- What parts of the example depend on local context?
- Which assumptions may not hold elsewhere?
- What tool or information access does the example require?
- Which roles would find the practice irrelevant?
- How should limitations be stated?
- What should other teams adapt rather than copy?
- How can sharing encourage experimentation instead of standardization?
Helping Other Teams Adapt a Practice to Their Own Context
- What problem does the receiving team actually have?
- Which parts of the shared practice match their work?
- What context needs to change?
- What constraints differ?
- How should the team test the adapted method?
- What should they preserve from the original pattern?
- What would make the adaptation genuinely useful?
Collecting Feedback From Teams That Reuse a Practice
- Did the practice work in the new context?
- What had to be changed?
- What problems appeared?
- Did the expected value materialize?
- What did the receiving team improve?
- Which lessons should update the shared example?
- What does reuse teach us about transferability?
Updating Shared Practices When Better Approaches Emerge
- What has changed since the practice was documented?
- Are new GenAI capabilities available?
- Have users found a simpler method?
- Which instructions are now outdated?
- Has the organizational context changed?
- What should be revised rather than preserved?
- How can the shared practice remain current?
Removing Examples That Are No Longer Useful
- Which shared examples are rarely used?
- What examples rely on outdated capabilities?
- Which practices no longer fit approved tools or guidance?
- Where have better methods replaced older ones?
- Could obsolete examples confuse new users?
- What historical examples are still useful for learning?
- Which material should be retired to keep the collection useful?
Building Peer Support for GenAI Adoption
Identifying People Willing to Help Colleagues With GenAI
- Who already helps colleagues informally?
- Who has practical GenAI experience?
- Which people enjoy explaining working methods?
- Who has credibility within their team?
- Where are willing helpers currently concentrated?
- What expectations should not be placed on them?
- How can willing peers support adoption without becoming a formal program?
- Where do users currently ask GenAI questions?
- Is there an obvious place to seek help?
- What prevents people from asking colleagues?
- How can requests remain lightweight?
- Which questions can peers answer quickly?
- What questions need specialist support instead?
- How can help be available without creating bureaucracy?
Connecting Less Experienced Users With More Experienced Peers
- Who needs practical help?
- Which experienced users understand similar work?
- What kind of matching matters most?
- Should support happen one-to-one or in groups?
- How much time should peers reasonably spend?
- What could make the relationship feel uncomfortable?
- How can peer connections accelerate confidence without creating dependency?
Creating Opportunities for Colleagues to Solve GenAI Problems Together
- What recurring GenAI problems are users facing?
- Which problems benefit from several perspectives?
- Where can colleagues compare different approaches?
- What real work can they examine together?
- How can collaborative problem-solving stay practical?
- What should be captured afterward?
- How can these sessions strengthen peer learning?
Using Team Discussions to Answer Common GenAI Questions
- What questions recur across the team?
- Which questions are suitable for normal team meetings?
- What answers need confirmation before sharing?
- How can a short discussion replace repeated individual support?
- Which questions reveal broader adoption barriers?
- What should be documented for later reference?
- How can team discussion normalize learning without dominating the agenda?
Encouraging People to Share Unfinished GenAI Work
- Why are users reluctant to show work in progress?
- What can colleagues learn from an unfinished interaction?
- Which mistakes are particularly useful to discuss?
- How can teams avoid turning sharing into evaluation?
- What feedback would help improve the work?
- What information should remain private?
- How can unfinished work make learning more realistic?
Connecting People Across Teams Who Face Similar GenAI Challenges
- Which teams are solving similar problems independently?
- What shared challenges are appearing?
- Who has already found useful approaches?
- What context differences matter?
- How can people exchange learning without creating another community structure?
- What problems deserve cross-team discussion?
- How can these connections reduce duplicate experimentation?
Knowing When a Peer Question Needs Specialist Support
- What questions can peers safely answer?
- Which issues involve policy or legal interpretation?
- When is technical support required?
- What problems indicate a broader organizational issue?
- How should a peer recognize the boundary?
- Where should the question be escalated?
- How can escalation remain easy for users?
Avoiding Dependence on a Small Number of Helpful Individuals
- Who is receiving most GenAI help requests?
- Are a few people becoming bottlenecks?
- What knowledge can be distributed more broadly?
- Which common questions should become self-service?
- How can more peers build enough confidence to help?
- What support load is reasonable?
- How can peer support remain resilient as adoption grows?
Sustaining Peer Support as General GenAI Capability Improves
- How should peer support change as users become more capable?
- Which basic questions should disappear over time?
- What more advanced problems may emerge?
- Do users still know where to ask for help?
- Which informal connections remain valuable?
- What support activities can be reduced?
- How can peer support evolve without becoming a permanent dependency?
Building Team-Level GenAI Working Methods
Identifying Recurring Team Work That GenAI Can Support
- What work does the team repeat regularly?
- Which tasks involve substantial information processing?
- Where does the team repeatedly prepare similar material?
- What decisions need recurring analysis?
- Which activities could GenAI support across several team members?
- What work should remain individual rather than standardized?
- Which recurring team activities are strongest candidates?
Comparing How Team Members Currently Use GenAI
- Who is already using GenAI?
- What tasks are they using it for?
- Which approaches differ significantly?
- Where do some users get better results?
- What useful practices are hidden in individual workflows?
- Which differences reflect personal preference rather than quality?
- What can the team learn from comparing current use?
Identifying GenAI Approaches That Consistently Work Well
- Which approaches repeatedly produce useful results?
- What conditions make them work?
- Are several team members getting similar benefits?
- Which methods require strong individual expertise?
- What quality controls are important?
- Which approaches are too inconsistent to standardize?
- What patterns are reliable enough to become shared methods?
Agreeing on Shared GenAI Working Patterns
- Which parts of the work benefit from a common approach?
- What should the shared pattern actually specify?
- Where should team members retain flexibility?
- What minimum review should be consistent?
- Which tools should the team use?
- How can a shared method remain simple?
- What agreement would improve consistency without limiting experimentation?
Developing Reusable Team-Level GenAI Resources
- What resources would genuinely save the team time?
- Do users need prompts, examples, checklists, or source material?
- Which information should be reusable?
- What context changes from task to task?
- How should resources be maintained?
- What resources risk becoming outdated quickly?
- How should team members know which resource to use for which situation?
Integrating GenAI Into Team Preparation and Coordination
- What team preparation happens repeatedly?
- Where could GenAI organize information before discussions?
- How could it help identify open questions?
- Which coordination work could be simplified?
- What inputs need to remain current?
- Where would GenAI add unnecessary complexity?
- How can GenAI support team coordination without creating a parallel process?
Defining How Important GenAI-Assisted Work Should Be Reviewed
- Which outputs require peer review?
- What level of review fits the consequence of the work?
- Who should verify important facts?
- How should assumptions be checked?
- What evidence should reviewers expect?
- Where would review erase the benefit of GenAI?
- What simple team review standards make sense?
Building GenAI Into Team Handoffs and Shared Work
- What information is lost during team handoffs?
- Could GenAI help structure handover material?
- Which shared documents could be improved?
- How can GenAI help summarize status and open issues?
- What context should transfer between people?
- Where are confidentiality boundaries important?
- How can GenAI make shared work more continuous?
Updating Team Methods as Experience Grows
- Which team methods are no longer working well?
- What have users learned since the method was introduced?
- Have new GenAI capabilities changed the best approach?
- Which parts should be simplified?
- Where should more flexibility be introduced?
- What new quality risks have appeared?
- How should the team update methods without constant disruption?
Avoiding Unnecessary Standardization of GenAI Use
- Which GenAI uses benefit from consistency?
- Where is personal flexibility more valuable?
- Are we standardizing because of real need or administrative preference?
- Could standard methods discourage better approaches?
- Which boundaries actually need to be common?
- How can teams share patterns without requiring everyone to use them?
- What level of standardization is genuinely useful?
Supporting Managers in Leading Adoption
Helping Managers Understand Their Role in GenAI Adoption
- What can managers influence directly?
- What adoption responsibilities belong with managers?
- What should remain with central leadership or support teams?
- How can managers create relevance for their teams?
- What behaviors from managers encourage experimentation?
- What behaviors discourage adoption?
- What simple description of the manager role is most useful?
Helping Managers Build Their Own GenAI Experience
- What manager tasks are good starting points?
- How can managers use GenAI in real leadership work?
- What experiences will help them understand employee challenges?
- Which use cases demonstrate both value and limitations?
- How can managers learn without needing advanced prompting?
- What should they verify carefully?
- How much personal experience is enough to lead credibly?
Preparing Managers to Discuss GenAI With Their Teams
- What should managers explain first?
- What questions are likely to come up?
- Which concerns should be acknowledged directly?
- What role-specific opportunities can managers discuss?
- What should managers avoid promising?
- When should they escalate questions?
- How can the conversation encourage real experimentation?
Helping Managers Identify Opportunities in Team Work
- Which recurring team tasks could GenAI support?
- Where does the team spend excessive preparation time?
- What work involves repeated analysis or communication?
- Which frustrations do team members mention regularly?
- How can managers ask employees for opportunity ideas?
- Which opportunities are too risky for early experimentation?
- What opportunities should the manager explore first?
Helping Managers Create Time for Experimentation
- Where can experimentation fit into normal work?
- What low-value activity could be reduced?
- How much time does the team actually need?
- Which deadlines make experimentation unrealistic right now?
- How can managers signal that experimentation is legitimate?
- What experiments should not consume team capacity?
- How can learning continue without becoming an extra burden?
Supporting Managers in Coaching Less Confident Users
- What is preventing the user from feeling confident?
- Does the employee need an example, practice, or reassurance?
- What simple task could create a successful experience?
- How can the manager encourage follow-up rather than provide the answer?
- When should a peer help instead?
- What mistakes should managers normalize?
- How can coaching gradually increase user independence?
Helping Managers Respond to Concerns and Resistance
- What concern is the employee actually expressing?
- Is the concern about jobs, quality, rules, or workload?
- Which concerns can the manager answer directly?
- What should the manager avoid dismissing?
- When is resistance based on a real organizational problem?
- What action could address the concern?
- How can managers distinguish healthy skepticism from disengagement?
Helping Managers Remove Team-Level Adoption Barriers
- What barriers can the manager solve directly?
- Is access limiting the team?
- Are workflows making GenAI inconvenient?
- Do employees lack time or permission to experiment?
- Are unclear expectations causing hesitation?
- What barriers require escalation?
- Which manager action would remove the most friction?
Helping Managers Reinforce Useful GenAI Behaviors
- What behaviors should managers notice?
- How can they recognize useful experimentation?
- When should managers ask whether GenAI could help?
- How can they encourage critical review of outputs?
- What examples should they share with the team?
- How can reinforcement avoid becoming pressure to use GenAI?
- What routines help useful behavior persist?
Helping Managers Adapt Their Approach as Team Adoption Matures
- What support does the team still need?
- Which basic interventions are no longer necessary?
- Who is ready for deeper experimentation?
- What team methods should become more sophisticated?
- Where should managers step back to allow independence?
- What new adoption problems appear at higher maturity?
- How should the manager's role evolve over time?
Coordinating Adoption Across the Organization
Mapping GenAI Adoption Activities Across Teams
- What adoption activities are currently happening?
- Which teams are running their own experiments?
- Where are learning efforts underway?
- Which functions are developing resources independently?
- What activities are invisible outside the local team?
- Where are major gaps?
- What organization-wide map would help coordination?
Clarifying Ownership for Different Adoption Activities
- Who owns tool access?
- Who owns practical adoption support?
- Who provides usage guidance?
- What responsibilities belong with managers?
- Where is ownership currently ambiguous?
- Which responsibilities are duplicated?
- What ownership model keeps coordination simple?
Identifying Duplicate Adoption Efforts
- Which teams are creating similar materials?
- Where are multiple groups solving the same support problem?
- What experiments are being repeated unnecessarily?
- Which duplication is useful because contexts differ?
- What work could be shared?
- Where would centralization create more overhead than value?
- Which duplicate efforts should be combined or connected?
Identifying Dependencies Between Adoption Initiatives
- What initiatives depend on tool access?
- Which learning activities depend on usage guidance?
- What scaling efforts depend on proven examples?
- Which initiatives need manager support?
- What dependencies could delay progress?
- Which activities can move independently?
- What sequence follows from the dependency structure?
Connecting Central Support With Local Team Needs
- What support is being provided centrally?
- What do teams actually need?
- Where is central support too generic?
- Which local problems are recurring across teams?
- What can local teams solve themselves?
- How should feedback reach central support?
- What connection model keeps support relevant?
Balancing Organization-Wide Consistency With Local Flexibility
- Which adoption elements must be consistent everywhere?
- What should teams be able to adapt?
- Where could local freedom create risk?
- Where could central standards reduce useful experimentation?
- Which common principles are enough?
- What local differences genuinely matter?
- What balance supports both coherence and relevance?
Sharing Adoption Learning Across Functions
- What lessons should other functions hear about?
- Which experiences are transferable?
- How can teams share failures as well as successes?
- What context is necessary for another function to interpret the lesson?
- Which learning should remain local?
- How can sharing avoid creating excessive meetings?
- What mechanisms help useful learning move across the organization?
Coordinating Limited Adoption Resources
- What resources are currently constrained?
- Which teams need support most?
- Where can resources create the greatest leverage?
- What can be handled through peer support?
- Which activities should be delayed?
- Where are scarce specialists becoming bottlenecks?
- How should support capacity be allocated transparently?
Resolving Conflicts Between Different Adoption Approaches
- Where are teams pursuing incompatible approaches?
- What underlying needs explain the difference?
- Which differences are harmless?
- Which differences create organizational risk or confusion?
- What evidence supports each approach?
- Can a common principle allow local variation?
- What should be resolved centrally?
Maintaining a Coherent Organization-Wide Adoption Direction
- Do current initiatives still support the intended outcome?
- Are teams interpreting adoption priorities consistently?
- Where has the effort become fragmented?
- Which local innovations should influence the broader direction?
- What common messages need reinforcement?
- Which activities no longer fit?
- How can the organization remain coherent without becoming rigid?
Measuring Ongoing GenAI Use
Defining What Meaningful GenAI Use Should Be Measured
- What does meaningful GenAI use actually mean?
- Should we measure frequency, breadth, depth, or something else?
- Which uses count as substantive?
- What metrics could encourage superficial behavior?
- Which measures align with the intended adoption outcome?
- What qualitative evidence is also needed?
- What measurement definition will be useful for decisions?
- How many people have access?
- How many actually use the tool?
- How large is the gap between access and use?
- Where is the gap largest?
- What reasons explain unused access?
- Does access data create a misleading impression of adoption?
- What should leaders look at instead of licensing numbers alone?
Measuring How Frequently People Use GenAI
- How often do active users engage with GenAI?
- How many use it daily, weekly, or rarely?
- Does frequency differ by role?
- What tasks drive the highest frequency?
- Is frequent use always a sign of value?
- Where is frequency increasing or declining?
- What does usage frequency tell us about habit formation?
Understanding How Many People Use GenAI Regularly
- What threshold should count as regular use?
- How many users meet that threshold?
- How does the proportion vary across teams?
- Are regular users expanding beyond early adopters?
- What distinguishes regular users from occasional users?
- Are some users active but only for low-value tasks?
- What does the regular-user population tell us about adoption depth?
Measuring the Breadth of Work Supported by GenAI
- How many different work situations involve GenAI?
- Are users limited to a few obvious tasks?
- Which work categories are becoming common?
- Where is GenAI use expanding into analysis or preparation?
- Which important work remains untouched?
- How much breadth is appropriate for different roles?
- What does the range of uses reveal about maturity?
Understanding Whether People Return to GenAI After Initial Use
- How many first-time users return?
- How quickly do they return?
- Where does usage drop off?
- What experiences make users come back?
- What experiences cause disengagement?
- Which groups show the strongest retention?
- What does repeat use tell us about perceived value?
Comparing GenAI Use Across Teams
- Which teams use GenAI most regularly?
- Which teams use it across the broadest range of work?
- Are differences explained by the nature of the work?
- How much does manager support matter?
- What local practices explain higher adoption?
- Which comparisons would be unfair or misleading?
- What useful lessons emerge from team differences?
Tracking How GenAI Use Changes Over Time
- Is overall use increasing?
- Is growth driven by new users or deeper use by existing users?
- Which teams are accelerating?
- Where has usage plateaued?
- Are people expanding into new work situations?
- What external events explain changes?
- What trend matters most for the next adoption decision?
Combining Usage Data With Qualitative Evidence
- What does the usage data fail to explain?
- What are users saying about their experience?
- Which work situations produce the most value?
- Why are some teams using GenAI more than others?
- What problems are hidden behind aggregate metrics?
- Where do qualitative findings contradict the numbers?
- What combined view gives the most accurate picture?
Measuring How Deeply GenAI Is Integrated Into Recurring Work
- Is GenAI used occasionally or as part of established work?
- Which recurring tasks now routinely involve GenAI?
- Do users remember to use GenAI without prompting?
- Has the workflow itself changed?
- How dependent is the use on individual enthusiasts?
- What would deeper integration look like?
- How can we measure integration without encouraging mandatory use?
Assessing Whether GenAI Is Creating Value
Defining What Value From GenAI Should Mean
- What kinds of value matter to the organization?
- Is the main goal time, quality, speed, capacity, or something else?
- What value matters to users themselves?
- Which benefits are measurable?
- What benefits are important but difficult to quantify?
- What outcomes should not be treated as value?
- What definition of value will guide the assessment?
Establishing a Baseline for Comparison
- What does the work look like without GenAI?
- How much effort does it currently require?
- What quality level is typical?
- How long does the work usually take?
- What baseline data already exists?
- Where do we need estimates rather than precise measures?
- What comparison point is credible enough to assess improvement?
Assessing Time and Effort Saved Through GenAI
- How much time does the task take with GenAI?
- How much did it take before?
- What additional review time is required?
- Does saved time remain consistent over repeated use?
- What new work is created by the GenAI-assisted process?
- Where does saved effort actually go?
- Is the net reduction meaningful enough to matter?
Assessing Whether GenAI Improves Quality
- What quality dimensions matter for this work?
- Does GenAI reduce common errors?
- Does it improve completeness or structure?
- Where does GenAI introduce new quality problems?
- How do reviewers rate GenAI-assisted work?
- Does quality improve consistently across users?
- Is the improvement strong enough to justify continued use?
Assessing Whether GenAI Improves Speed
- How much faster is the work completed?
- Which stages become faster?
- Where do review requirements add time back?
- Does faster output shorten the full workflow?
- Are downstream teams receiving work sooner?
- Could greater speed create more volume without more value?
- What speed improvement actually matters operationally?
Assessing Whether GenAI Improves Decision Preparation
- Does GenAI help identify more relevant options?
- Are assumptions surfaced more clearly?
- Is evidence easier to organize?
- Are tradeoffs more visible?
- Does preparation take less time?
- Are decision-makers receiving better material?
- What evidence shows that decision preparation has genuinely improved?
Assessing Whether GenAI Enables Work That Was Previously Impractical
- What useful work is now being performed that was previously skipped?
- Why was that work impractical before?
- How much effort does GenAI remove?
- Does the new work create measurable value?
- Is it repeatable?
- What additional review or resources does it require?
- Does the benefit justify making the work part of normal operations?
Comparing GenAI Benefits With the Effort and Cost Required
- What benefits are being realized?
- What licensing or infrastructure costs are involved?
- How much user time is required?
- What support effort does the use require?
- What review burden exists?
- Are benefits increasing as users gain experience?
- Is the overall value proposition strong enough to continue or scale?
Distinguishing GenAI's Contribution From Other Changes
- What else changed at the same time?
- Could process improvements explain part of the result?
- Did staffing or workload change?
- Which improvement is directly attributable to GenAI?
- Where is attribution uncertain?
- What evidence would strengthen the assessment?
- How should value be communicated without overstating causality?
Building an Overall View of Value Created Through GenAI
- Where is GenAI creating the strongest value?
- Which value dimensions appear most consistently?
- What uses create little measurable benefit?
- How does value differ across teams?
- Which benefits are growing over time?
- Where is evidence still weak?
- What overall conclusion should leadership draw about GenAI value?
Diagnosing Resistance and Low Adoption
Identifying Where Adoption Is Lower Than Expected
- Which teams or groups are below the expected level of adoption?
- How large is the gap?
- Is the issue low usage, shallow usage, or both?
- Has adoption always been low or recently declined?
- What expectations are we comparing against?
- Could the target itself be unrealistic?
- Where does low adoption deserve deeper investigation?
Distinguishing Lack of Access From Lack of Use
- Do people actually have working access?
- Can they use the features relevant to their work?
- Are account problems creating hidden barriers?
- Who has access but never uses it?
- Why are those users not engaging?
- Are we incorrectly treating access provision as adoption?
- What part of the gap is truly behavioral?
Understanding Whether People See Enough Personal Relevance
- Do employees know how GenAI applies to their work?
- Which roles struggle most to see useful applications?
- Are current examples too generic?
- Do users associate GenAI with only a narrow set of tasks?
- Have people tried role-relevant uses?
- What recurring work situations could make the value clearer?
- Is lack of relevance the main reason adoption remains low?
Identifying Skill or Confidence Problems
- Do users know how to start a useful interaction?
- Can they improve weak results?
- Are they comfortable reviewing outputs?
- Do mistakes discourage them from trying again?
- Which capability gaps appear most often?
- Is the issue skill, confidence, or both?
- What support would address the actual problem?
Understanding Whether Usage Boundaries Are Discouraging Use
- Do employees understand what is allowed?
- Are rules perceived as more restrictive than they are?
- Which situations create uncertainty?
- Are users avoiding GenAI because asking for clarification is difficult?
- What boundaries genuinely prevent useful work?
- Can guidance be made more practical?
- How much of low adoption comes from policy uncertainty?
Identifying Poor Early Experiences With GenAI
- What happened during users' first experiments?
- Were initial tasks too difficult?
- Did results contain obvious mistakes?
- Were expectations unrealistic?
- Did users receive help after a poor result?
- Which early experiences became lasting negative impressions?
- How can those experiences be corrected?
Understanding How Managers Influence Low Adoption
- Do managers encourage experimentation?
- Are they using GenAI themselves?
- Do employees feel they have permission to try?
- Are managers creating enough time?
- Do performance expectations discourage learning?
- Which managers are actively skeptical?
- How much would stronger manager behavior change adoption?
Identifying Workflow Friction That Makes GenAI Inconvenient
- Does GenAI require extra copying or reformatting?
- Is relevant information difficult to provide?
- Are approved tools disconnected from everyday systems?
- Does review take too much time?
- Is the existing manual method simply easier?
- Which friction appears most often?
- What changes would make GenAI practical enough to use regularly?
Identifying Organizational Policies or Structures That Discourage Adoption
- Which policies unintentionally discourage experimentation?
- Where do approval processes create delay?
- Do organizational structures make support difficult to access?
- Are teams rewarded for short-term output at the expense of learning?
- What management practices reduce adoption?
- Which barriers are systemic rather than individual?
- What organizational changes deserve consideration?
Determining the Main Causes Behind Resistance or Low Adoption
- Which causes appear repeatedly across the evidence?
- What is a symptom rather than a root cause?
- Which causes affect the largest number of users?
- What causes differ by group?
- Which problems reinforce each other?
- What evidence supports the diagnosis?
- What causes should the response address first?
Responding to Adoption Problems
Matching the Response to the Actual Cause of the Problem
- What problem are we actually trying to solve?
- What evidence supports the diagnosis?
- Which intervention addresses that specific cause?
- What response would only treat the symptom?
- Does the response fit the affected user group?
- What unintended effects could the intervention create?
- How will we know whether the response worked?
Fixing Practical Access Problems
- What access problem is occurring?
- Who is affected?
- Is the issue technical, procedural, or permission-related?
- What can be fixed immediately?
- What requires another function's involvement?
- How should affected users be informed?
- What change would prevent the problem from recurring?
Clarifying Confusing Usage Boundaries
- What rule are users misunderstanding?
- Which situations create ambiguity?
- Is the guidance itself unclear?
- What practical examples could resolve the confusion?
- What questions require policy-owner input?
- How can clarification reach all affected users?
- What wording would make the boundary easier to apply?
Providing More Relevant Role-Specific Examples
- Which roles are not connecting current examples to their work?
- What recurring tasks matter to them?
- Which real situations could demonstrate GenAI value?
- What examples are simple enough to try?
- What limitations should be shown?
- Who can provide credible examples from similar work?
- How can examples trigger experimentation rather than passive learning?
Providing Targeted Support Where Capability Is Weak
- What specific capability is missing?
- Who needs the support?
- Can the skill be learned through real work?
- Would peer support be enough?
- What support is too broad for the actual gap?
- How can progress be observed?
- When can the additional support be reduced?
Addressing Managerial Barriers to Adoption
- What manager behavior is limiting adoption?
- Why is the manager acting this way?
- Is the issue confidence, workload, skepticism, or unclear expectations?
- What support would help?
- What leadership expectation needs clarification?
- When does the issue require escalation?
- How can managerial behavior change without creating compliance theater?
Changing Learning Approaches That Are Not Working
- What learning activity is failing?
- Are employees unable to apply it to real work?
- Is the material too generic?
- Is there too much explanation and too little experimentation?
- What evidence shows users are not learning?
- What alternative approach fits better?
- How can the revised method be tested quickly?
Removing Workflow Friction That Discourages GenAI Use
- What step makes GenAI inconvenient?
- Can the friction be removed without technical integration?
- What information is repeatedly copied manually?
- Which reviews could be simplified?
- Does the workflow need redesign rather than another GenAI tip?
- What change would make the use easier than the old method?
- How should the improvement be tested?
Responding to Trust or Quality Concerns
- What quality issue is reducing trust?
- How often does the problem occur?
- Is it caused by unsuitable use, weak inputs, or model limits?
- What review practice could reduce the risk?
- Should the use case be narrowed?
- What evidence would rebuild appropriate confidence?
- When is the right response to stop using GenAI for that task?
Checking Whether the Intervention Actually Improves Adoption
- What change did we expect from the intervention?
- Did usage or behavior change?
- Did the affected users report improvement?
- Did the original barrier actually decline?
- What unintended effects appeared?
- Is more time needed before drawing a conclusion?
- Should the intervention continue, change, or stop?
Reinforcing Successful Adoption Behaviors
Identifying the GenAI Behaviors Worth Reinforcing
- Which behaviors lead to useful GenAI outcomes?
- Do users recognize opportunities independently?
- Are they reviewing outputs critically?
- Are successful methods being reused?
- Which behaviors create value rather than just usage?
- What behaviors should not be encouraged?
- Which few behaviors matter most for sustainable adoption?
Recognizing People Who Apply GenAI Usefully
- What should recognition reward?
- Is the person creating real value?
- Are they using sound judgment?
- Did the work help others learn?
- How can recognition avoid rewarding raw usage volume?
- What form of recognition fits the culture?
- How can useful behavior become visible without creating competition?
Reinforcing Opportunity Recognition During Everyday Work
- What recurring situations should make GenAI come to mind?
- Are managers asking useful opportunity questions?
- Do teams discuss overlooked GenAI uses?
- What cues can be built into normal work?
- Are users transferring patterns to new situations?
- When do reminders become unnecessary?
- How can recognition become increasingly automatic?
Encouraging Managers to Discuss Successful GenAI Use
- Which successful examples should managers bring into team discussions?
- What made each use successful?
- What can others learn from the pattern?
- How often should these examples be discussed?
- How can managers avoid turning discussion into promotion?
- Which failures are also worth discussing?
- How can normal team conversation reinforce adoption?
Building GenAI Into Existing Team Routines
- Which team routines already occur regularly?
- Where can GenAI naturally support those routines?
- Could preparation or follow-up include GenAI?
- What prompts regular consideration without extra meetings?
- Which routines should remain unchanged?
- How can team practices stay flexible?
- What small integrations would make useful behavior easier to sustain?
Sharing New Examples That Keep GenAI Relevant
- What new uses are emerging?
- Which examples reflect recent capabilities?
- What roles need fresh examples?
- Are existing examples becoming repetitive?
- What practical problems do new examples solve?
- Which examples are still too experimental?
- How can new examples renew curiosity without creating hype?
Refreshing Support Material as Users Become More Capable
- Which basic guidance is no longer necessary?
- What more advanced questions are users asking?
- Which examples should be replaced?
- What working methods have matured?
- Where do users need less prescription?
- What support remains valuable for new users?
- How should materials evolve with capability levels?
Making Successful GenAI Behaviors More Visible in Everyday Work
- Where are useful behaviors happening unnoticed?
- How can teams see how colleagues work with GenAI?
- What examples can be surfaced during normal work?
- Which behaviors are more important than the final output?
- How can visibility remain voluntary and practical?
- What could make sharing feel performative?
- How can useful behavior spread through observation?
Sustaining GenAI Use After Initial Enthusiasm Fades
- Why is initial enthusiasm declining?
- Are users still experiencing real value?
- Have examples become stale?
- Are new barriers appearing?
- Which recurring work situations can sustain use?
- What support can be reduced or refreshed?
- How can adoption move from novelty to routine usefulness?
Keeping Successful Adoption Behaviors Active Over Time
- Which behaviors are becoming stable?
- Where are old habits returning?
- What organizational routines reinforce useful GenAI use?
- How should managers continue supporting adoption?
- Which practices need periodic refresh?
- How do new capabilities affect established behaviors?
- What will keep adoption useful rather than merely persistent?
Scaling Successful Adoption Patterns
Identifying Adoption Patterns That Are Ready to Scale
- Which adoption patterns have worked repeatedly?
- What evidence shows that the pattern is stable?
- Has it worked for more than one person?
- What conditions does it depend on?
- Are support requirements manageable?
- What unresolved risks remain?
- Which patterns are mature enough to test elsewhere?
Confirming That a Successful Pattern Produces Real Value
- What value does the pattern create?
- Is the benefit repeatable?
- How much effort is required?
- Does the pattern improve quality, speed, or capacity?
- What evidence supports the value claim?
- Are there hidden costs?
- Is the value strong enough to justify scaling?
Distinguishing the Transferable Core From Local Details
- What part of the pattern makes it successful?
- Which details are specific to the original team?
- What information or tools are required?
- Which role characteristics matter?
- What can another team change safely?
- What must remain consistent?
- What is the smallest transferable version of the pattern?
Identifying Other Teams That Could Benefit From the Pattern
- Which teams perform similar work?
- Who faces the same underlying problem?
- Which teams have the required tools and information?
- Where is manager support sufficient?
- What differences could prevent transfer?
- Which teams are ready to experiment?
- Where is the strongest next environment for replication?
Adapting the Pattern to a New Team or Function
- What is different about the new context?
- Which parts of the pattern still apply?
- What examples need to change?
- What constraints are new?
- Which support should be added?
- How should the team test the adapted method?
- What would count as successful adaptation?
Preparing Support for the First New Adopters
- What do new adopters need to understand?
- Which examples should they see?
- What access or information is required?
- Who can answer questions?
- What common mistakes should they expect?
- How much support is enough?
- What will help them become independent quickly?
Testing the Pattern in a New Environment
- What assumptions are we testing?
- How closely should the new team follow the original pattern?
- What differences should be documented?
- What outcomes should be measured?
- What problems would indicate poor transferability?
- How long should the test run?
- What should determine whether scaling continues?
Comparing Results Across Different Teams
- Did the pattern create similar value in each team?
- What differences appeared?
- Which contextual factors explain those differences?
- Did support requirements vary?
- What parts of the pattern were consistently useful?
- Where did adaptation improve the method?
- What does the comparison tell us about broader scalability?
Documenting What Is Needed for Reliable Reuse
- What prerequisites should future teams know?
- What steps are essential?
- What parts are optional?
- What review or quality controls matter?
- What common failure modes should be noted?
- What examples make the method easier to understand?
- What documentation is enough without becoming a manual?
Expanding the Pattern After Successful Replication
- What evidence supports broader expansion?
- Which teams should come next?
- What support capacity is required?
- What parts can be self-service?
- Which risks increase with scale?
- How should learning continue during expansion?
- What would indicate that scaling should pause?
Identifying Workflows That Should Change
Identifying Workflows Where GenAI Is Only Being Added on Top
- Where has GenAI become an extra step rather than an improvement?
- What existing steps remain unchanged unnecessarily?
- Are users duplicating work before and after GenAI?
- Which reviews exist only because the workflow was not redesigned?
- Does GenAI save time at one point but add work elsewhere?
- What would the workflow look like if designed with GenAI from the start?
- Which workflows deserve deeper examination?
- Where do people repeatedly read large volumes of information?
- Which information is manually summarized?
- Where is data copied between systems?
- Which tasks involve repeated classification or extraction?
- What information processing genuinely requires human judgment?
- Where could GenAI reduce effort safely?
- Which workflows have the strongest redesign potential?
Identifying Repeated Handoffs and Rework
- Where does work move between too many people?
- What information is lost during handoffs?
- Which outputs are repeatedly rewritten?
- Why does rework occur?
- Could GenAI improve preparation before handoff?
- Which handoffs exist only because of old process design?
- What should be reconsidered in a redesigned workflow?
Identifying Workflows With Slow Decision Preparation
- Which decisions take too long to prepare?
- Where is information gathering the bottleneck?
- What analysis is repeated manually?
- Which inputs arrive too late?
- Where could GenAI prepare material faster?
- What decision responsibilities must remain unchanged?
- Which decision workflows warrant redesign?
Finding Communication Work That Could Be Simplified
- Where does communication consume excessive time?
- Which messages are repeatedly recreated?
- Where is the same information adapted for several audiences?
- Which coordination messages add little unique value?
- Could GenAI prepare or transform the content?
- What communication requires strong human ownership?
- Which workflows could become materially simpler?
Identifying Quality Bottlenecks That GenAI Could Help Address
- Where does poor quality create repeated rework?
- Which checks happen too late?
- What errors appear repeatedly?
- Where is review dependent on scarce specialists?
- Could GenAI provide an additional first-pass review?
- Which quality judgments remain human?
- Where could workflow redesign improve quality rather than just speed?
Understanding Where Human Judgment Adds the Most Value
- Which workflow steps require real expertise?
- Where are people making consequential decisions?
- What work depends on context GenAI cannot fully understand?
- Which current human tasks are mostly mechanical?
- Where should GenAI prepare rather than decide?
- What human contribution becomes more important after automation?
- How should the workflow concentrate judgment where it matters most?
Mapping How GenAI Currently Fits Into the Workflow
- At what points is GenAI currently used?
- Who uses it?
- What inputs does it receive?
- What outputs does it produce?
- What happens before and after GenAI use?
- Where are users compensating for weak integration?
- What does the current workflow reveal about redesign opportunities?
Identifying Parts of the Workflow That No Longer Make Sense
- Which steps exist because of old tool limitations?
- What manual work has become unnecessary?
- Which approvals no longer add meaningful control?
- Where are outputs produced that nobody uses?
- What duplication could be eliminated?
- Which steps should remain for quality or accountability?
- What should disappear from a redesigned workflow?
Prioritizing Workflows for Deeper Redesign
- Which workflows have the largest improvement potential?
- Where is GenAI already proving useful?
- Which workflows affect many people?
- What redesigns are feasible with current tools?
- Which workflows carry high implementation risk?
- Where would redesign create visible business value?
- Which workflows should be examined first?
Redesigning Work Around GenAI
Defining the Outcome the Redesigned Work Should Produce
- What should the redesigned workflow accomplish?
- What problems in the current workflow must disappear?
- Which outcomes matter most?
- What quality level should be preserved or improved?
- How much speed or effort reduction is realistic?
- What should not change?
- What clear outcome should guide redesign decisions?
Separating Work Best Done by People From Work GenAI Can Support
- Which activities require strong human judgment?
- What work is repetitive enough for GenAI support?
- Which activities depend on interpersonal understanding?
- Where can GenAI prepare material for human review?
- What work should remain fully human?
- Which activities could become more automated over time?
- What division of work creates the strongest overall workflow?
Redesigning the Sequence of Human and GenAI Activities
- Which activity should happen first?
- Where should GenAI enter the workflow?
- What should people review before the next stage?
- Which steps can run in parallel?
- Where should human decisions interrupt the flow?
- What sequence minimizes unnecessary handoffs?
- What new sequence best combines human and GenAI strengths?
- What information does GenAI need at each stage?
- Which sources are authoritative?
- How current must the information be?
- What information cannot be shared?
- Where does context need to be added manually?
- What information gaps could weaken the workflow?
- What minimum information set makes the redesigned process viable?
Designing Human Review Into the Workflow
- Which outputs need human review?
- At what point should review occur?
- Who is qualified to review?
- What should reviewers check?
- How can review be proportionate to risk?
- Where could duplicate reviews be removed?
- What review design preserves both speed and accountability?
Planning How Exceptions and Difficult Cases Should Be Handled
- What cases will not fit the normal workflow?
- How can users recognize an exception?
- When should work leave the GenAI-supported path?
- Who should handle difficult cases?
- What information needs to accompany the escalation?
- Could GenAI help identify exceptions?
- How can the workflow remain robust without overcomplicating the normal path?
- Which GenAI capabilities are required?
- What other tools does the workflow depend on?
- Which information sources are essential?
- What access is currently missing?
- Which dependencies are fragile?
- What can be redesigned without technical integration?
- What prerequisites must be understood before implementation?
Redesigning Handoffs Between People and GenAI-Assisted Work
- What should transfer at each handoff?
- How can context be preserved?
- Who remains responsible for the work?
- What output format makes the next step easier?
- Where are handoffs currently creating rework?
- How can GenAI prepare clearer handover material?
- What handoff design reduces friction without obscuring accountability?
Testing the Redesigned Workflow in Real Work
- What part of the redesigned workflow should be tested first?
- Which real case is suitable?
- What baseline should we compare against?
- What user feedback should we collect?
- Which quality problems need attention?
- What unexpected friction appears?
- What evidence would justify continuing the redesign?
Refining the Workflow Based on Practical Experience
- What worked as intended?
- Where did users deviate from the design?
- What steps created unnecessary effort?
- Which GenAI interactions were unreliable?
- What review could be simplified?
- What new needs became visible?
- How should the workflow change before broader use?
Adjusting Roles and Responsibilities
Identifying Tasks That Change Because of GenAI
- Which tasks are performed differently after GenAI adoption?
- What parts of those tasks become easier?
- Which steps disappear?
- What new review work appears?
- How does the role's contribution change?
- Which changes are temporary during transition?
- What task changes need to be reflected in role expectations?
Identifying Tasks That Require Less Human Effort
- Which tasks now require substantially less manual work?
- How much effort has actually been reduced?
- What human input remains necessary?
- Does reduced effort affect staffing assumptions?
- Which parts of the task still consume most of the remaining human effort?
- Are savings consistent across users?
- Which reductions are durable enough to plan around?
Identifying New Tasks Created by GenAI-Enabled Work
- What new review tasks appear?
- Who maintains reusable GenAI resources?
- What new coordination is required?
- Are new quality or monitoring tasks necessary?
- Which new tasks add real value?
- What temporary tasks will disappear as adoption matures?
- Who should own the genuinely new responsibilities?
Clarifying Where Human Judgment and Accountability Remain
- Which decisions remain human?
- Who is accountable for GenAI-assisted outputs?
- Where does professional expertise remain essential?
- What review responsibilities need to be explicit?
- Can accountability become unclear after workflow redesign?
- What should role descriptions make clear?
- How can responsibility remain visible even when GenAI does more of the preparation?
Reassigning Responsibilities in Redesigned Work
- Which responsibilities no longer fit the old role boundaries?
- Where has work shifted between people?
- What tasks could move to a different role?
- Who should own GenAI-supported preparation?
- What handoffs can be removed?
- What changes require formal agreement?
- How should new ownership be communicated?
Adjusting Collaboration Between Different Roles
- Which roles now interact differently?
- What information needs to move between them?
- Where can GenAI reduce coordination effort?
- What new dependencies appear?
- Which old boundaries are becoming less useful?
- Where could role overlap create confusion?
- What collaboration model best fits the redesigned work?
Updating Managerial Expectations for GenAI-Enabled Work
- What should managers now expect from employees?
- Should output speed expectations change?
- How should quality expectations change?
- What GenAI skills are reasonable to expect?
- How should managers judge work without rewarding raw usage?
- What expectations could create unhealthy pressure?
- What new expectations should be communicated clearly?
Identifying New Capability Requirements for Roles
- What new GenAI skills does the role need?
- Which judgment skills become more important?
- What ability to review outputs is required?
- Does the role need stronger information literacy?
- What capabilities can be learned through everyday work?
- Which capabilities require structured development?
- How should role development priorities change?
Deciding How Freed Capacity Should Be Used
- How much capacity has actually been freed?
- What valuable work is currently underdone?
- Could people spend more time on judgment or relationships?
- What work should not simply expand because time is available?
- How should managers allocate the capacity?
- What employee input should shape the decision?
- Where can freed capacity create the most organizational value?
Supporting People Through Changes in Roles and Responsibilities
- What changes are employees experiencing?
- Which parts are causing uncertainty?
- What new expectations need explanation?
- What training or practice do people need?
- Which responsibilities are being removed?
- How can managers involve employees in shaping the transition?
- What support will help the role change without unnecessary disruption?
Building GenAI Into Organizational Practices
Integrating GenAI Into Employee Onboarding
- What should new employees learn about GenAI?
- Which approved tools should they access early?
- What usage boundaries matter from the start?
- Which practical tasks can introduce GenAI naturally?
- How much GenAI content belongs in onboarding?
- What should new hires learn through real work instead?
- How can onboarding normalize appropriate GenAI use without overwhelming people?
Integrating GenAI Into Ongoing Professional Development
- Which existing development programs should include GenAI?
- What role-specific GenAI capability matters?
- How can development focus on real work?
- Which skills need regular refresh?
- How should advanced users continue learning?
- What learning should remain optional?
- How can professional development evolve with GenAI capabilities?
Building GenAI Into Recurring Planning Activities
- Which planning activities occur regularly?
- Where could GenAI help clarify objectives?
- How could it support option generation?
- Where could it identify assumptions or risks?
- What planning decisions remain fully human?
- Which inputs need verification?
- How can GenAI become a normal planning aid without adding bureaucracy?
Building GenAI Into Meeting and Decision Preparation
- What preparation happens before recurring meetings?
- Could GenAI summarize relevant context?
- How can it help identify questions or objections?
- Where could it compare options?
- What information should not be provided?
- How should outputs be reviewed?
- What recurring meeting practices are suitable for GenAI support?
Integrating GenAI Into Standard Work Templates and Resources
- Which templates could include optional GenAI guidance?
- Where would GenAI reduce repetitive preparation?
- What reusable context should be included?
- Which templates should remain tool-neutral?
- How can resources avoid becoming outdated prompts?
- What users actually need when performing the work?
- Which standard resources should evolve to reflect GenAI?
Building GenAI Into Review and Quality Practices
- Where could GenAI provide an additional review pass?
- Which quality checks can be supported?
- What review remains human?
- How can GenAI help identify missing information?
- Where could GenAI create false confidence?
- What evidence is required before accepting its critique?
- How can GenAI strengthen quality without multiplying review steps?
Integrating GenAI Into Knowledge-Sharing Practices
- What knowledge is currently difficult to find?
- Where could GenAI help summarize or organize shared learning?
- Which recurring knowledge products could improve?
- How should source provenance be preserved?
- What information should not be included?
- How can GenAI help people reuse previous work?
- What knowledge-sharing practices would become more effective?
Updating Established Working Methods to Reflect GenAI
- Which established methods assume GenAI does not exist?
- What parts of those methods are now unnecessary?
- Where can GenAI strengthen the existing approach?
- What human steps still matter?
- Which methods should remain unchanged?
- How should updates be tested?
- What established practices need revision first?
Embedding GenAI Learning Into Existing Organizational Routines
- Where do people already reflect on work?
- Could team reviews include GenAI lessons?
- How can project closeouts capture useful GenAI methods?
- What recurring meetings can surface adoption learning?
- How can learning stay lightweight?
- Which routines would become overloaded?
- Where can GenAI learning fit naturally without creating new structures?
Making GenAI-Enabled Work Part of Normal Organizational Practice
- Which GenAI-supported methods are mature enough to become normal?
- Are users applying them independently?
- What organizational routines already reinforce them?
- Which methods still depend on individual enthusiasts?
- What documentation or support is still necessary?
- How should normal practice remain adaptable?
- What shows that GenAI-enabled work is no longer a special initiative?
Reviewing the Adoption Approach
Comparing Adoption Progress With the Intended Outcome
- What outcome did we originally define?
- How much progress has been made?
- Which behaviors have actually changed?
- Where is adoption broader or deeper than expected?
- Where are we behind?
- Has the intended outcome itself become outdated?
- What does the comparison imply for the next phase?
Reviewing Which Parts of the Adoption Approach Worked Well
- Which activities produced the strongest adoption progress?
- What did users find most helpful?
- Which support methods led to independent use?
- What role did managers play?
- Which communication approaches worked?
- What successful elements are transferable?
- What should clearly be continued?
Identifying Adoption Activities That Produced Little Value
- Which activities consumed effort without changing behavior?
- What training was rarely applied?
- Which resources were hardly used?
- What communication had little effect?
- Why did these activities underperform?
- Should they be improved or stopped?
- What resources could be redirected elsewhere?
Comparing Results Across Different User Groups
- Which groups made the strongest progress?
- Who continues to struggle?
- What support did each group receive?
- Which differences reflect the nature of the work?
- What adoption methods worked only for certain groups?
- Where is unequal progress a real concern?
- What should change for the next cycle?
Reviewing Whether Support Matches Current User Needs
- What support do users currently rely on?
- Which support needs have declined?
- What new needs are appearing?
- Are advanced users underserved?
- Are new users still receiving enough basic help?
- What support is too generic?
- How should the support model evolve?
Reviewing the Effectiveness of Adoption Communication
- Do employees understand why GenAI matters?
- Are expectations clear?
- Which messages are being misunderstood?
- What concerns remain unaddressed?
- Are managers communicating consistently?
- Has the message kept pace with actual adoption?
- What should be changed in future communication?
Assessing Whether Current Measures Show What Matters
- What are we currently measuring?
- Do the metrics reflect meaningful adoption?
- Are we overemphasizing usage counts?
- What important behavior is invisible?
- Are value measures credible?
- Which measures create undesirable incentives?
- What should the measurement approach change?
Reviewing How Adoption Resources Are Being Used
- Where are support resources currently going?
- Which activities consume the most capacity?
- What support creates the most value?
- Where are resources underused?
- Which teams receive disproportionate support?
- What could become self-service?
- How should resources be reallocated?
Identifying Adoption Activities That Should Stop, Start, or Change
- What should we stop doing?
- What important activity is currently missing?
- Which existing activity needs redesign?
- What should continue unchanged?
- What evidence supports each decision?
- Which changes can begin immediately?
- What combination of stop, start, and change creates the strongest next approach?
Defining the Next Improvement Cycle for GenAI Adoption
- What are the most important lessons from the current cycle?
- Which problems remain unresolved?
- What new opportunities have emerged?
- What should the next cycle prioritize?
- Which interventions should be tested?
- What evidence should be collected next?
- What clear improvement objective should guide the next cycle?
Adapting to New GenAI Capabilities
Monitoring GenAI Developments That Could Matter to the Organization
- Which GenAI developments are relevant to our work?
- What sources should we monitor?
- How often should capabilities be reviewed?
- Which updates are mostly incremental?
- What changes could materially affect adoption?
- Who needs to know about important developments?
- What monitoring approach is useful without becoming distracting?
Distinguishing Meaningful Capability Changes From Minor Updates
- What practical work becomes possible because of the change?
- Does the update materially improve reliability?
- Does it remove an existing adoption barrier?
- Is the capability available in tools we can actually use?
- What evidence shows the improvement is significant?
- Could the update matter only to a small group?
- Does the change justify action now?
Assessing How New Capabilities Affect Existing GenAI Opportunities
- Which existing use cases become easier?
- What previous limitations disappear?
- Which workflows could now use GenAI more deeply?
- Are some old methods becoming unnecessary?
- What new risks appear?
- Which existing opportunities should be reprioritized?
- What should we retest because the capability changed?
Identifying New Organizational GenAI Opportunities
- What new work becomes possible?
- Which previously impractical tasks are now realistic?
- What functions benefit most from the new capability?
- Which user groups could gain immediate value?
- What new customer or operational opportunities appear?
- What needs testing before broader attention?
- Which new opportunities deserve exploration first?
Reviewing Usage Boundaries After Capability Changes
- Does the new capability introduce new information risks?
- Are existing usage rules still sufficient?
- What new actions can the tool perform?
- Which review expectations need updating?
- Are new integrations changing the risk profile?
- What guidance might users misunderstand?
- Which boundaries need clarification before wider use?
Updating GenAI Learning and Support
- What new capability do users need to understand?
- Who needs the update most?
- Which old guidance is now incomplete?
- What practical examples should be added?
- Do existing support materials need revision?
- What can users discover through experimentation?
- How should learning stay current without constant formal retraining?
Revisiting Existing GenAI-Enabled Workflows
- Which workflows rely on older capability assumptions?
- Could new capabilities remove existing manual steps?
- What integrations have become possible?
- Which review steps can now change?
- What new risks need consideration?
- Should the workflow be retested before redesign?
- Which established GenAI workflows are most worth revisiting?
Communicating Relevant New Capabilities to Employees
- Which new capabilities are actually relevant to employees?
- What practical difference do they make?
- Which groups need to hear about them?
- What examples make the change concrete?
- What limitations should be explained?
- How can communication avoid creating hype around every release?
- What should employees reasonably try next?
Testing Important New GenAI Capabilities in Real Work
- What real task can test the new capability?
- What result should we compare it against?
- Which users are suitable for the test?
- What quality or safety concerns need review?
- What would count as a meaningful improvement?
- What unexpected limitations appear in practice?
- Should the capability be adopted more broadly?
Retiring Outdated GenAI Practices as Better Approaches Become Available
- Which existing practices rely on old limitations?
- What newer approach works better?
- Is the old method still useful in some contexts?
- What shared resources need updating?
- Which users still depend on the old practice?
- How should the transition be communicated?
- What should be retired to keep organizational GenAI practice current?