Most organizations are asking the wrong question about AI. They want to know whether the model is good enough. The sharper question is whether the worker knows when they are not. A new Management Science peer-reviewed study by Andrew Caplin and colleagues makes that point with unusual clarity: AI helps most when people have an accurate read on their own limits. In other words, the future of human performance may depend less on raw talent than on calibrated judgment.

In the experiment, 732 participants judged whether people in photos were over age 21, sometimes with an AI confidence score and sometimes without it. The headline result is easy to grasp and hard to ignore. AI improved average accuracy, and it helped lower-ability participants more than stronger ones. But the decisive variable was calibration: people who understood how good or bad they actually were captured more value from the machine. That finding fits a broader pattern in the AI-at-work literature. In a landmark Science study on generative AI and professional writing, Shakked Noy and Whitney Zhang found that AI raised productivity most for weaker performers. In the field, Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that AI assistance boosted customer-support productivity by 15 percent on average, with the largest gains going to less experienced workers.

The real bottleneck is self-knowledge

The Caplin team’s contribution is to separate skill from self-awareness. Plenty of workers are wrong about their own competence, and that error changes how they use AI. Overconfident people ignore good advice. Underconfident people defer when they should trust themselves. Either way, the collaboration breaks down.

That sounds intuitive, but it matters because most companies still frame AI adoption as a technology rollout, not a behavioral one. They benchmark models, negotiate licenses, and build workflows, then assume workers will naturally learn when to rely on the tool. The practical implication is stark. Two employees with equal skill can get very different returns from the same system simply because one has a more realistic sense of personal accuracy.

That insight should change how leaders think about training. Firms have spent decades trying to raise worker capability through more instruction, more certification, and more process. AI may make a different lever more valuable: teaching people to judge uncertainty well. The worker who can say, “I’m shaky here; the model probably sees this better,” is suddenly a high-leverage collaborator.

Why AI narrows gaps, but not as much as it could

The optimistic story about workplace AI is that it democratizes expertise. Recent research keeps pointing that way. Noy and Zhang showed large gains for less skilled writers, and the QJE field study of customer-support agents found that AI especially helped newer workers by diffusing the tacit practices of top performers. Caplin and coauthors add an important twist: AI reduces inequality, but miscalibration prevents it from doing the full job.

Their counterfactual is the most policy-relevant result in the study. With actual human behavior, AI reduced performance inequality substantially. With perfect calibration, the reduction would have been nearly twice as large. That means the distributional effects of AI are not fixed by model quality alone. They depend on whether workers can read their own confidence with any accuracy.

This is a crucial distinction for executives and policymakers now debating whether AI will widen or narrow labor-market inequality. David Autor has argued that the best use of AI is to rebuild middle-skill work by embedding expertise in tools that broaden access to capability. The Caplin findings support that vision, but with a caveat: augmentation works best when workers know when to lean on the machine. Without that, the same system can leave value on the table and preserve avoidable gaps in output.

Calibration may be trainable, which changes the policy picture

The most encouraging part of this story is that calibration does not look like a fixed trait. Decades of judgment research have shown that feedback can improve probabilistic reasoning, and newer work suggests that even brief interventions can help. A 2024 study on calibration training using an interactive app reported measurable reductions in overconfidence in under 30 minutes. Older work by Sarah Lichtenstein and Baruch Fischhoff reached a similar conclusion long before anyone talked about copilots and foundation models.

That opens a more practical path for organizations than the usual either-or debate between “train the worker” and “deploy the AI.” In many settings, the better answer may be to train workers in how to work with AI: how to estimate confidence, how to recognize edge cases, how to spot when the model is likely right, and how to challenge it productively when it is not. This is especially attractive in domains where deep upskilling is slow or expensive. A hospital, law firm, insurer, or call center may not be able to turn novices into experts quickly, but it may be able to make them much better judges of when to trust assistance.

That is also why managers should be cautious about reading AI performance metrics in isolation. A tool can look excellent in aggregate and still disappoint inside a team if workers are badly calibrated. The missing KPI may not be model accuracy. It may be whether employees know the difference between confidence and competence.

AI is often described as a force multiplier. The phrase is too vague. The better metaphor is a mirror that also advises. It reflects the worker’s strengths and weaknesses back at them, then offers a recommendation. The people who benefit most are not simply the smartest. They are the ones who can look into that mirror without flinching. As companies race to embed AI into everyday work, that may be the most underrated competitive advantage in the building.

Key Take-Away

Getting AI adoption wrong means focusing on better AI instead of better judgment. Organizations gain the most when employees understand their own limits and know when to trust or challenge AI, unlocking stronger performance and wider productivity…
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Why Most Organizations Are Getting AI Adoption Wrong appeared first on Disaster Avoidance Experts.

A Monday morning earnings call ends, and the CFO opens a chat window to draft the board update in minutes. Down the hall, a frontline finance team still works the old way because access, training, and incentives never arrived. That split reality sits at the heart of the new internationally representative firm survey of almost 6,000 CFOs, CEOs, and senior executives across the United States, United Kingdom, Germany, and Australia, published in the National Bureau of Economic Review by Nicholas Bloom from Stanford University and other scholars.

The NBER paper, titled Firm Data on AI, reads like a progress report and a warning. Executives describe fast diffusion, limited realized impact so far, and large expected gains soon. They also forecast a smaller workforce, largely through slower hiring. The real story for leaders sits in the gap between ambition and daily use, plus the widening disconnect between executive expectations and employee beliefs.

Productivity Expectations Will Pressure Every Operating Model

Senior leaders in the survey expect AI to move the productivity needle in a way that dwarfs most operational initiatives. Across the four countries, executives forecast about 1.4% higher productivity over the next three years from AI adoption, with the United States at about 2.3% over the same horizon, a pace that translates to roughly 0.77 percentage points per year. Those results nearly double baseline growth when firms already plan around about 1% trend productivity.

Yet the paper also reports that realized impact over the past three years stayed modest, with an average realized productivity gain around 0.29% across firms. That “quiet period” matters because it explains why many organizations still treat AI as a pilot program rather than an operating system. Executives forecast acceleration because deployment patterns shifted sharply during 2025, including a jump in usage frequency and a drop in the share reporting zero use to about a quarter of respondents. The adoption signal is clear in the paper’s executive use measures, and it sets expectations that teams will soon face new usage standards. For instance, Accenture now tracks how often senior employees utilize artificial intelligence on a weekly basis, according to recent reports. The firm links these adoption metrics to promotion opportunities for veteran staff to ensure they embrace the growing role of technology in the workplace.

For professionals running functions, the best comparison comes from measured deployments rather than hopes. A large field study of a generative assistant in customer support showed about a 14% productivity lift on average, with the biggest gains among newer workers, a pattern documented in generative AI assistance. That result aligns with what many operators already sense: AI often standardizes and raises the floor before it raises the ceiling. Leaders who plan for broad productivity gains should therefore pair targets with workflow redesign, quality metrics, and role-based enablement, since a tool alone rarely changes an operating model.

Hiring Slowdowns Will Be The First Employment Effect

Executives in the survey predict a net employment decline of about 0.7% over the next three years as AI spreads, and the authors note that this implies roughly 2 million fewer jobs when applied to more than 250 million employed people across the four countries. That estimate matches what many companies already signal in practice: hiring plans move before layoffs do because hiring sits inside annual budgeting, headcount approvals, and backfill decisions.

This is where leadership teams can gain an advantage with clarity. If employment effects arrive through reduced hiring, then workforce planning becomes less about crisis management and more about precision: which roles receive augmentation, which roles consolidate, and which roles shift toward higher-value tasks. That approach also aligns with labor-market exposure research that frames AI as task transformation rather than job deletion. The global exposure estimate from the IMF puts nearly 40% of global employment in AI-exposed categories, emphasizing that complementarity and inequality risks travel together. Meanwhile, the ILO’s analysis finds the strongest exposure in clerical work and expects augmentation to dominate overall effects, detailed in GenAI exposure research.

For executives, the key operational move is to convert “reduced hiring” into an intentional design decision. That means defining where AI substitutes for routine throughput, where it improves decision quality, and where it opens capacity for growth. It also means protecting trust. Employees watch hiring freezes and interpret them as a signal about career paths. Leaders who connect hiring decisions to visible upskilling and internal mobility programs preserve engagement while capturing the productivity upside they forecast.

The Adoption Gap Creates Risk And Opportunity At The Same Time

The paper’s most surprising statistic feels mundane: executives report about 1.5 hours per week of AI use on average, and about 25% report zero use. Those numbers sit alongside a headline that around 70% of firms actively use AI, suggesting a two-speed economy inside the same organization. The adoption headline comes from firm AI usage, while the usage intensity points to a deeper truth: adoption without habit formation stays shallow.

This matters because the paper also finds a stark perception gap. Employees surveyed separately predict AI will increase employment by about 0.5% over the next three years, while executives predict a decline. That divergence appears in the paper’s employee expectations and raises a leadership challenge: execution requires shared belief about what work will look like. When employees expect expansion and leaders expect contraction, governance and change management become decisive.

External surveys show that disagreement is common. The OECD’s cross-country work on job quality and AI points to uneven adoption, mixed perceptions, and the need for worker involvement in deployment design, summarized in job quality evidence. At the macro level, many employers forecast churn: the World Economic Forum projects large job creation and displacement through 2030, with a net gain, while also warning that disruption touches a sizable share of roles, detailed in job disruption outlook. Finance-side estimates skew more aggressive on substitution, including a widely cited projection that AI could expose the equivalent of 300 million full-time jobs to automation, described in automation exposure estimate.

Senior leaders can turn this uncertainty into advantage by measuring reality faster than competitors. The winning play combines three disciplines: instrument adoption by role and workflow, link usage to quality and cycle-time outcomes, and convert productivity gains into a transparent talent agenda. When leaders treat AI as a capital allocation decision, they demand unit economics, control risk, and scale what works. When leaders treat AI as a culture project, they build shared capability and reduce fear. The survey suggests both are required, because expectations already run high and the adoption base still has room to grow.

The executive survey offers a clear message: leaders expect meaningful productivity gains and a smaller payroll footprint, even while recent realized impact stays limited. Those expectations will reshape budgets, performance targets, and hiring plans. Professionals who act early can shape the trajectory by moving from slogans to operating discipline, from scattered pilots to workflow ownership, and from headcount anxiety to skill-based mobility. The organizations that close the adoption gap first will capture the gains their leaders already forecast, and they will do it with a workforce that understands where it fits.

Key Take-Away

AI adoption is accelerating, but real value comes from embedding AI into everyday workflows, upskilling employees, and aligning leadership and workforce expectations to close the gap between strategy and daily execution.
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post AI Adoption Looks Widespread Until You Measure It appeared first on Disaster Avoidance Experts.

A mechanical engineer pulls up a chat window, drops in a set of unit conversions, and watches a clean answer appear in seconds. Ten minutes later, the same engineer reaches for a scratch pad, runs the math again, and cross-checks the result against a familiar formula. That pattern has become the default rhythm of modern engineering work, and the AI adoption report from Omni Calculator puts hard numbers behind it.

Across the United States, AI has already moved from novelty to routine. Engineers treat it like a power tool for speed, then lean on professional judgment for certainty. Tech leadership now owns the gap between those two moves.

AI Use In Engineering Shifts Leadership From Tools To Systems

Engineers have made a clear decision: AI belongs in the workflow, especially where the work feels repetitive. Omni Calculator reports that 86% of U.S. engineers use AI, and most usage targets routine calculations and time-saving tasks rather than high-level design work anchored in domain context and liability. The signal matters for leaders because it reframes the value proposition. AI expands capacity by clearing low-leverage chores, then hands humans more time for judgment-heavy decisions.

That pattern matches what software teams report at scale. A Google research report found AI use has become nearly universal among surveyed developers, with productivity gains tied to automating repetitive work. Software development leaders can treat that as an early indicator of where other disciplines are headed: more AI in the daily loop, plus more responsibility on the organization to keep outputs reliable.

High-performing teams respond by treating AI as a system, not a subscription. The system includes guardrails that define allowed use cases, approved inputs, and required checks, especially where safety, compliance, or customer commitments sit on the line. It also includes training that builds AI literacy the way prior eras built CAD literacy, simulation literacy, and secure coding literacy. The NIST Generative AI profile fits well as an organizing backbone because it pushes teams toward structured testing, documentation, and lifecycle governance.

Leaders also benefit from recognizing that AI use in engineering design still carries uneven performance across tasks. Research on generative AI for engineering design highlights strengths in interpreting briefs and drafting instructions alongside gaps that demand validation. That reality supports a simple leadership stance: let AI draft, let engineers decide, and let the process enforce verification.

The Trust Gap Turns Verification Into The Real Productivity Metric

Adoption looks impressive until trust enters the conversation. Omni Calculator reports that only 6% of engineers accept AI outputs with full confidence, while 89% manually double-check results. That behavior reads like friction, yet it also reflects healthy engineering instincts. Engineers build systems that tolerate uncertainty through validation, redundancy, and testing. They bring the same discipline to AI outputs, and leaders should reinforce that discipline as a professional standard.

Verification carries a cost that leaders should measure explicitly, because it directly shapes ROI. If AI saves time on initial work yet demands long verification cycles, the net gain shrinks. Omni Calculator also reports that only 9% of engineers see accuracy improvements from AI, while 71% use it primarily to save time. That combination pushes leadership toward a better metric: capacity gained after verification, rather than raw speed before checks.

This challenge extends beyond engineering. A national U.S. worker survey update from the Federal Reserve Bank of St. Louis tracks adoption in the broader workforce and explores how usage evolves over time, which helps leaders benchmark internal behavior against external change. Meanwhile, organizational readiness often lags employee behavior. A 2025 workplace report highlights that many companies invest heavily, yet few reach maturity, and leadership alignment often constrains scaling.

Closing the trust gap requires two parallel moves: faster verification and safer inputs. Faster verification comes from structured checklists, reproducible prompts, test harnesses, and approved reference methods. Safer inputs come from clear data handling rules that keep proprietary designs, client data, and regulated information inside governed environments. NIST’s emphasis on test and validation processes aligns directly with this need, especially for teams that already live by verification and validation in every other part of engineering.

A practical leadership win comes from shifting AI use toward tools with built-in transparency and auditable math, then reserving open-ended chat for drafting, brainstorming, and documentation. That approach gives engineers a clear path to confirm outputs quickly, and it keeps the culture centered on credibility, which customers and regulators reward.

AI Readiness Becomes A Talent Strategy, A Geography Strategy, And A Mentorship Strategy

Regional adoption gaps already shape how quickly teams normalize AI. Omni Calculator found a 14% difference between the South and West versus the Midwest. Local ecosystems help explain why. The AI economy mapping work from Brookings shows AI readiness clustering across metros based on talent, innovation capacity, and industry mix. When talent moves in dense networks, behaviors spread through peer effect, shared vendors, and cross-company hiring.

Hiring and investment follow that gravity. Job markets reveal where employers place bets. The University of Maryland and LinkUp dataset behind AI job mapping tracks AI-related postings across the United States and shows sharp growth since late 2022, which signals increasing demand for AI-fluent talent in many regions. Leaders planning new sites, acquisitions, or major project ramps can treat AI readiness as a workforce input alongside cost, supply chain, and customer proximity.

Inside the company, AI readiness also varies by generation, and leadership needs an HR strategy that respects that variation. Omni Calculator reports Millennials expect disruption at higher rates than Gen Z, while Gen Z expresses stronger optimism about job improvement. That difference fits a career-stage reality: mid-career engineers have invested years in skill stacks that now feel easier to automate, while early-career engineers treat AI as a default tool. The Future of Jobs Report 2025 reinforces the broader need for reskilling and structured transitions as technology reshapes tasks and roles.

Leaders can turn this into a mentorship advantage by redesigning apprenticeship. Junior engineers still need fundamentals, intuition, and a strong sense for unit discipline, boundary conditions, and failure modes. AI can accelerate early work, and leaders can preserve learning by making auditing a core expectation. Seniors can model strong prompting by embedding constraints, assumptions, and acceptance criteria, then requiring juniors to explain the reasoning chain behind any result. That stance reflects the engineering reality that validation underpins resilient systems, whether the source of the number is a human, a calculator, or an AI model.

Winning organizations treat AI adoption as cultural infrastructure. They set clear governance, protect data, build verification habits, tailor rollout by location, and invest in intergenerational skill transfer. The rest buy licenses, chase speed, and wonder why trust never arrives.

Conclusion

AI has already changed engineering workdays. Engineers use it to clear repetitive tasks, then they lean on professional judgment to validate results. That shift hands leaders a bigger job than procurement: leaders must design systems that make verification fast, consistent, and culturally valued.

The trust gap offers a clear roadmap. When nearly everyone verifies outputs, leadership can treat verification as the center of ROI, then invest in governed tools and testable workflows that shrink the verification tax. Regional and generational differences offer a second roadmap. Leaders can place investments where AI-ready talent pools already thrive, and they can lift slower-moving teams through training, mentorship, and clear career pathing that elevates human judgment.

Engineers already understand the standard: sound work earns trust through validation. Companies that operationalize that standard for AI turn adoption into advantage, and they build teams that move faster with confidence.

Key Take-Away

AI-ready organizations gain the most value from AI by treating it as a governed system, not just a tool, using verification, training, and strong oversight to boost productivity while maintaining trust, accuracy, and engineering judgment.
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post AI-Ready Regions And Teams Are Redefining Tech Strategy appeared first on Disaster Avoidance Experts.

A true generative AI (Gen AI) learning culture transcends mere training programs. It’s about weaving a deep appreciation for growth and development into the organizational DNA. This means fostering an environment where learning is continuous, integrated into daily operations, and viewed as essential for both individual and collective success. Such a culture fuels curiosity, innovation, and adaptability – crucial traits for navigating today’s dynamic business landscape, particularly in creating a learning culture around the rise of technologies like Gen AI. This creates an environment where learning is continuous, integrated into daily routines, and seen as essential to both individual and organizational success.

5 Key Elements for Learning Culture That Fulfills Gen AI Promise

Several key elements are crucial for cultivating a robust learning culture, especially one designed to empower organizations to leverage the transformative potential of Gen AI. First, Gen AI learning initiatives must be strategically aligned with the organization’s overarching goals. Simply introducing Gen AI tools without a clear understanding of how they contribute to business objectives will likely result in limited adoption and minimal impact. For instance, if a marketing team aims to personalize customer experiences, Gen AI learning programs should focus on natural language processing for content creation, sentiment analysis for understanding customer feedback, and AI-powered personalization engines. 

This connection between Gen AI learning and tangible business outcomes motivates employees and demonstrates the value of their development. When employees see a clear link between mastering Gen AI and the organization’s strategic objectives—such as increased efficiency, innovative product development, or improved customer satisfaction—they are more likely to engage fully in the learning process. This alignment creates a sense of purpose and direction, making Gen AI learning more meaningful and motivating, while managing risks.

Secondly, leadership must champion Gen AI learning from the top down. Active participation in Gen AI workshops, public support for Gen AI initiatives, and open discussions about the potential and ethical considerations of Gen AI send a powerful message that continuous improvement in this critical area is a priority at all levels. 

When leaders actively participate in Gen AI learning initiatives, perhaps by exploring prompt engineering techniques or discussing the implications of large language models, it demonstrates that learning is not just an expectation for employees but a shared value. This visible commitment from leadership encourages employees to follow suit, as they see that Gen AI learning is valued at all levels of the organization.

Thirdly, Gen AI learning should be a shared and collaborative experience. Creating opportunities for employees to learn from each other through peer-led Gen AI workshops, cross-departmental Gen AI project teams, and internal Gen AI hackathons fosters a sense of community and shared purpose. This collaborative approach enhances learning by allowing employees to share Gen AI knowledge, discuss challenges related to implementation, and learn from diverse perspectives. Learning about Gen AI should not be a solitary activity but rather a shared journey where employees can support and learn from each other.

Fourthly, providing easy access to relevant Gen AI resources is essential. This includes online courses on prompt engineering, fine-tuning, and model deployment, as well as access to documentation, research papers, and industry best practices related to Gen AI. Internal wikis or knowledge bases dedicated to Gen AI can serve as central repositories for information, code snippets, and reusable prompts. Regular knowledge-sharing sessions, perhaps focused on specific Gen AI tools or applications, empower employees to learn at their own pace and find the information they need. These resources provide a platform for employees to ask questions, share insights, and access information relevant to their roles and development within the context of Gen AI.

Finally, acknowledging and celebrating Gen AI learning achievements reinforces the value of continuous development in this critical area. This can range from simple shout-outs for innovative Gen AI use cases to formal awards ceremonies recognizing teams that have successfully implemented Gen AI solutions. Recognizing and celebrating successes and milestones in Gen AI learning is an effective way to reinforce the value of continuous education. These celebrations do more than just reward those who have made significant strides in their Gen AI learning; they also highlight the organization’s commitment to growth and development in this vital technological area.

Case Study: Fulfilling Gen AI Promise at a Healthcare Provider

As a consultant specializing in organizational development and Gen AI implementation, I recently partnered with a regional healthcare provider facing the challenge of integrating Gen AI into their operations. Their existing training programs were fragmented and lacked strategic alignment with the potential of Gen AI. My role was to guide them in building a sustainable Gen AI learning culture.

My approach began with a thorough needs assessment, interviewing employees at all levels to identify skill gaps and learning preferences related to Gen AI. Based on the assessment, we developed tailored Gen AI learning paths for different roles. Medical practitioners received training on using Gen AI for diagnostic support and personalized treatment plans, while administrative staff focused on using Gen AI to automate administrative tasks and improve patient communication. We integrated Gen AI learning into daily routines through on-demand training modules accessible via the company intranet and dedicated “Gen AI exploration days.” 

I worked closely with senior leaders to ensure their active participation in Gen AI workshops and strategic discussions. The CEO and other executives participated in prompt engineering workshops and discussed the ethical implications of using Gen AI in healthcare, demonstrating their commitment. We established Gen AI learning circles and an online forum to facilitate knowledge sharing and collaboration on Gen AI-driven projects. Finally, we implemented a system for recognizing Gen AI learning achievements through digital badges and quarterly awards ceremonies showcasing innovative Gen AI applications.

Over 12 months, the healthcare provider experienced significant improvements. Employees across roles became more proficient in using Gen AI tools, leading to more efficient operations and higher-quality patient care. Staff time devoted to admin tasks shrank by 33%, with more focus on higher-level projects. Diagnostic accuracy improved by 18%, and diagnostic efficiency by 24% due to enhanced understanding of AI-driven diagnostic tools. Leadership’s visible commitment and the alignment of Gen AI learning with strategic goals fostered a culture of continuous improvement in this crucial area. The collaborative environment facilitated by Gen AI learning circles and online forums led to the development of innovative patient care solutions powered by Gen AI.

Key Takeaways Regarding Promise of Gen AI Learning

Building a sustainable Gen AI learning culture is not a one-time initiative but an ongoing process. Leaders must prioritize Gen AI learning by making it a strategic priority and allocating resources accordingly. They must also lead by example by actively participating in Gen AI learning activities and championing development programs related to this technology. Fostering collaboration by creating opportunities for employees to learn from each other about Gen AI is also crucial. Finally, leaders must measure the impact of Gen AI learning by tracking the effectiveness of initiatives and making adjustments as needed. By embracing these principles, organizations can create a culture where Gen AI learning is not just valued but also drives innovation, adaptability, and long-term success in the age of intelligent machines.

Key Take-Away

A strong learning culture is key to fulfilling the AI promise, aligning AI learning with business goals, leadership support, collaboration, accessible resources, and recognition to drive innovation, adoption, and measurable results.
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Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Gen AI Promise Depends On a Learning Culture Revolution appeared first on Disaster Avoidance Experts.

Organizations worldwide are navigating the transformative potential of generative AI (Gen AI), but the key to unlocking its true value lies in human-centered implementation. One of the most effective approaches to ensure success is inviting employees to participate in Gen AI pilot programs. These programs provide a structured and collaborative environment to evaluate new technologies before full-scale deployment, combining technical rigor with human insight to maximize impact.

Pilot programs for Gen AI are more than experimental playgrounds; they are foundational to creating tools and processes that align with organizational needs and employee workflows. By involving employees in these trials, companies can uncover critical insights, refine functionalities, and foster a culture of innovation. This participatory model not only enhances the effectiveness of Gen AI solutions but also galvanizes employees to become active contributors to technological evolution within their organizations.

Video: “Gen AI Thrives When Employees Lead the Charge”

Podcast: “Gen AI Thrives When Employees Lead the Charge”

Gen AI Thrives With Pilot Programs

Testing Gen AI tools in a controlled environment of pilot programs with employee input allows businesses to bridge the gap between theoretical capabilities and practical applications. For instance, one organization piloted a Gen AI tool designed to automate data entry. Early adopters from the finance department reported that the tool struggled with nuanced contextual understanding, leading to errors. This feedback guided developers in adjusting the algorithm, significantly improving its accuracy before company-wide deployment.

Unlike traditional top-down rollouts, pilot programs encourage iterative development. A report by McKinsey highlights that companies adopting this agile methodology see a 25% faster time-to-market compared to traditional approaches. Employees engage with the technology in real-world scenarios, providing feedback loops that refine the tools. This iterative approach minimizes risks and accelerates the time to value, ensuring that new tools enhance productivity rather than disrupt operations. That’s especially important when dealing with highly expert professionals such as lawyers. 

Moreover, pilot programs offer a unique platform for functional diversity. Engaging employees from different departments—sales, HR, operations, and beyond—allows organizations to explore a wide range of applications. For instance, while the HR team might focus on using Gen AI for talent acquisition, sales teams could prioritize customer relationship management. This breadth of input ensures that the technology evolves into a versatile asset capable of addressing diverse organizational needs.

Building a Culture of Collaboration and Ownership

Inviting employees to participate in pilot programs fosters a sense of ownership and collaboration. This approach moves beyond mere adoption to active engagement, where employees see themselves as co-creators of the technology. When individuals witness their feedback shaping the final product, it cultivates trust and reduces resistance to change.

In my role as a consultant, I recently worked with a mid-sized logistics company aiming to improve operational efficiency through Gen AI. We designed a pilot program to test a new AI-driven scheduling tool. Participants included warehouse staff, dispatchers, and administrative personnel. The program revealed critical insights: warehouse staff noted interface complexities that slowed task execution, while dispatchers highlighted challenges integrating the tool with existing software. Using this feedback, we iteratively refined the tool’s design and functionality.

The results were transformative. After adjustments, the scheduling tool reduced turnaround times by 35%, eliminated scheduling conflicts, and enhanced overall team productivity in the six months after rollout. Employees who participated in the pilot program became champions of the technology, conducting training sessions and addressing peers’ concerns during the broader rollout. This case exemplifies how collaborative pilot programs can ensure a seamless integration of Gen AI while building trust and enthusiasm among employees.

Organizations can further boost engagement by selecting a diverse pool of participants. Including tech-savvy early adopters alongside less experienced users provides a balanced perspective on usability. Incentivizing participation also plays a critical role. Companies can recognize contributors through public acknowledgments, certificates, or professional development opportunities such as AI-related workshops. These measures reinforce a culture of innovation while rewarding proactive engagement.

Pilot programs also serve as incubators for internal champions. These champions—employees deeply familiar with the tools—become invaluable advocates during the broader rollout. They bridge the gap between technical teams and end-users, providing training and addressing concerns. This grassroots support significantly enhances the likelihood of successful long-term adoption.

Empowering Gen AI to Thrive Through Data-Driven Insights

Beyond fostering collaboration, pilot programs offer a data-rich environment for making informed decisions. By testing Gen AI tools on a smaller scale, organizations can analyze performance metrics, user feedback, and system behavior under real-world conditions. These insights enable leaders to identify strengths and weaknesses, and manage risks, guiding strategic adjustments before scaling up.

For example, a regional retail company piloted a Gen AI tool for inventory management. Initial trials revealed that the AI struggled to adapt to seasonal fluctuations. By incorporating feedback from store managers and refining algorithms, the company developed a system that dynamically adjusted to demand patterns, reducing stockouts and overstocking by 30%.

This evidence-based approach minimizes costly mistakes that could arise from large-scale implementations without adequate testing. Importantly, the iterative nature of pilot programs aligns with modern agile principles, allowing organizations to refine processes continuously. This agility not only ensures technical excellence but also instills confidence among stakeholders that the technology is ready for broader deployment.

A Case Study in Success

A mid-sized healthcare provider sought to integrate Gen AI to enhance patient intake processes. I worked with them to initiate a pilot program involving administrative staff, nurses, and IT personnel to test an AI-powered tool designed to streamline patient registration and documentation.

Feedback from frontline staff revealed critical areas for improvement. Nurses highlighted the need for better integration with existing electronic health record systems, while administrative staff suggested tweaks to the interface for faster data entry. The IT team’s insights helped optimize backend performance.

The iterative development process led to a refined tool that reduced patient intake times by 40% over the nine months following the successful pilot program, freeing up staff to focus on patient care. Employees who participated in the pilot became champions of the technology, training their peers and addressing initial hesitations. The program’s success underscored the value of collaboration and adaptability in implementing Gen AI solutions.

Conclusion

Gen AI pilot programs represent a transformative approach to technology adoption, merging innovation with human-centric design. By actively involving employees, these programs not only refine tools but also build trust, foster collaboration, and empower organizations to make data-driven decisions. As businesses navigate the evolving landscape of Gen AI, embracing pilot programs can unlock unparalleled opportunities for growth and efficiency.

By anchoring implementation strategies in employee engagement and iterative refinement, companies can ensure that Gen AI becomes a cornerstone of their success, driving innovation and fostering a future-ready workforce. The path to transformative change begins with collaboration, and pilot programs are the compass guiding businesses toward sustainable, impactful adoption.

Key Take-Away

AI thrives when organizations pair gen AI pilot programs with employee participation, using real-world feedback to refine tools, reduce risks, build trust, and accelerate successful adoption at scale.
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Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Gen AI Thrives When Employees Lead the Charge appeared first on Disaster Avoidance Experts.

Gen AI will not transform associations just because a few staff members or volunteers are curious about it. The bigger question is whether the organization knows how to absorb it into daily work. That is the most important lesson in a new Brookings analysis of Gen AI adoption in Europe and the US. Their finding is striking. U.S. workers are more likely to use Gen AI, they use it more intensively, and the difference is not explained by technology access alone. Management practices and employer encouragement do much of the work.

That should get the attention of association leaders. Associations operate through a layered system of staff, boards, committees, chapters, and sections. They also serve as standard setters for the members who look to them for education, ethics, credentialing, guidance, and signals about what responsible practice looks like. When Gen AI adoption is weak inside the association, the organization usually hesitates outside it too. The result is slower member service, uneven volunteer execution, and less confidence in showing members how Gen AI should be used well.

Management Matters More Than Access

The research does not point to a simple hardware or software gap. The authors find that demographics, occupations, industries, and firm size explain part of the U.S. and Europe divide, but only part. A large share of the gap remains. What closes it is management. The World Management Survey has long shown that organizations vary sharply in how they reward performance, promote talent, and address underperformance. That older insight became famous in the digital era through “Americans Do IT Better”, which argued that organizations gained more from information technology when they reorganized around it instead of merely buying it.

The Gen AI evidence follows the same pattern. In the new paper, management quality is closely linked to adoption, and firm encouragement is the strongest predictor of whether workers actually use Gen AI. Official UK analysis on management practices and AI adoption found a similarly steep gradient, with firms in the top management decile far more likely to adopt AI than those in the bottom decile.

For associations, that means the real bottleneck is often not budget. It is leadership ambiguity. Staff wonder whether Gen AI is welcome or risky. Chapter leaders do not know what is approved. Section volunteers improvise their own rules. Legal or policy teams slow decisions because nobody has drawn a clear line between productive use and unacceptable use. In that environment, adoption stays shallow. A few enthusiastic people carry the load, while the organization as a whole learns almost nothing.

Member Service, Chapters, and Sections

Associations feel uneven Gen AI adoption faster than many other organizations because their work is both centralized and distributed. A headquarters team may handle membership, events, education, certification, communications, and standards development, but large parts of the member experience are delivered through chapters and sections led by volunteers. When Gen AI use is inconsistent, members see the inconsistency immediately. One chapter produces polished outreach and fast follow up. Another still depends on manual emails and outdated templates. One section can summarize a regulatory update overnight. Another waits weeks to brief its members.

That matters because the operational upside is real. The Brookings paper links higher adoption with faster productivity growth at the industry level, while finding no clear evidence of broad employment contraction so far. That aligns with the Draghi report on European competitiveness, which argues that growth now depends on how well organizations diffuse new technology through real workflows. It also matches the micro evidence. In customer support, Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that Gen AI assistance lifted productivity, especially for less experienced workers. In professional writing, Shakked Noy and Whitney Zhang found that workers completed tasks faster and with better quality.

Inside an association, those gains show up in familiar places. Member service teams can draft clearer first responses. Education teams can turn webinars into summaries, study guides, and course updates. Standards and policy teams can scan large document sets faster, while still reserving judgment for humans. Chapters can produce better agendas, sponsorship outreach, and post event follow up. Sections serving early career members, women, international members, or specialty practice groups can create more tailored content without adding full time staff. The point is not to automate the association’s voice. It is to give that voice more reach and consistency.

What Changes When Leadership Takes Ownership

In one recent consulting engagement, I worked with a midsize national association with about fifty staff, a volunteer board, more than two dozen chapters, and several sections organized around career stage and specialty. Leadership had already purchased Gen AI licenses. That was not the problem. The problem was that adoption had no operating model behind it.

A few staff members were using Gen AI to draft member emails and summarize long reports. Some chapter leaders wanted help writing newsletters and meeting scripts. Several section leaders were curious about using it to build discussion guides and resource lists. But nobody was sure what the rules were. Staff worried about privacy. Volunteers worried about quality. The association wanted to advise members on Gen AI, yet it had not built internal confidence first.

So the work began with management, not prompts. We defined approved use cases for member service, event planning, chapter communications, education, and standards support. We set simple guardrails for confidentiality, human review, attribution, and record retention. We trained managers to ask a better question each week: where can Gen AI save time without lowering trust? Then we equipped chapter and section leaders with reusable workflows instead of one off demos.

The shift was immediate. Staff used Gen AI to draft member replies, repurpose conference content, prepare board briefs, and summarize policy developments. Chapter leaders used shared prompts to build agendas, volunteer outreach, and sponsor follow up. Section leaders used it to create more tailored programming for distinct member groups. What changed most was not the software. It was the signal from leadership that Gen AI was now part of how the association worked.

That pattern is consistent with earlier research. In their study of police departments, Luis Garicano and Paul Heaton found that technology created value when it was paired with organizational change, not when it was simply installed. And in “Diagnosing the Italian Disease”, Bruno Pellegrino and Luigi Zingales showed how weak meritocracy and poor management can block technology diffusion even when tools are available.

Conclusion

Associations should stop treating Gen AI as a side experiment and start treating it as a management test. The organizations that pull ahead will not be the ones with the most excited board presentations or the longest policy documents. They will be the ones that give staff and volunteers clear permission, useful guardrails, and practical workflows that fit the real work of serving members. If an association wants to lead its field on Gen AI, it has to do more than talk about standards. It has to model them first.

Key Take-Away

The Gen AI gap is not about access to technology but about leadership. Associations that provide clear guidance, guardrails, and workflows will drive adoption, improve member service, and achieve greater impact.
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post The Real Gen AI Gap in Associations Starts With Management appeared first on Disaster Avoidance Experts.

Integrating Generative AI (Gen AI) into organizational workflows offers transformative potential, yet the transition often encounters skepticism and resistance. One effective strategy to mitigate these challenges is leveraging early adopters—team members who enthusiastically embrace new technology—to facilitate communication, boost efficiency, and drive adoption. By empowering these champions, organizations can establish a peer-driven communication model that reduces fear, fosters trust, and accelerates the learning curve.

Video: “Why the Best Gen AI Champions Are Hiding in Plain Sight”

Podcast: “Why the Best Gen AI Champions Are Hiding in Plain Sight”

Why Early Gen AI Champions Are Key to Rollout Success

Early adopters are not just users of new technology; they are multipliers of its potential. By advocating for Gen AI within their teams, these individuals influence adoption rates and attitudes across the organization.

• Building Trust and Relatability: Colleagues often perceive early adopters as credible because they experience the same organizational dynamics. Their firsthand demonstrations of Gen AI benefits—such as faster workflows or enhanced accuracy—resonate more deeply than theoretical presentations from leadership. For example, in a healthcare company, an early adopter showed how Gen AI reduced paperwork errors, directly impacting patient outcomes. This kind of practical, relatable evidence builds trust.
• Encouraging a Collaborative Culture: By acting as mentors and addressing concerns in real time, early adopters foster an environment of mutual learning. A team member struggling with integrating Gen AI into their workflow can turn to a colleague champion for tailored advice, reducing frustration and building confidence.
• Driving a Bottom-Up Adoption Model: Involving early adopters sends a message that employee voices matter in the adoption process. This inclusive approach boosts engagement, as workers feel more involved in shaping how technology integrates into their routines. A study by LTIMindtree reported in Forbes highlights that organizations with strong peer-driven adoption models see 40% cost savings.

Client Case Study: A Retail Company’s Gen AI Transformation

In one consulting engagement, I worked with a mid-sized retail company aiming to implement Gen AI for inventory management and customer service. The company’s goals included reducing overstock, improving customer experiences, and automating routine tasks. However, the workforce initially resisted the changes, citing fears of job loss and complexity in using Gen AI tools.

First, we used surveys and focus groups to identify staff concerns about the changes, and we also used these tools to identify 15 early adopters from various roles, including store managers, warehouse staff, and customer service representatives. These individuals were known for their openness to innovation and held influence among their colleagues, and all of them used Gen AI in their personal lives, with many also using it in their work. Their diverse perspectives ensured the initiative addressed the needs of different departments.

Next, we invested in comprehensive training for the champions. This included:

• Gen AI Tool Mastery: A hands-on understanding of how to use the new tools effectively. That included uncovering best practices from those early adopters already using Gen AI tools in their job at that company, and sharing these best practices. Likewise, I brought in my experience of use cases from other retail company clients. Together, we developed a toolkit of use cases most relevant to this company and its individual departments and roles.
• Communication Strategies: Training on how to explain benefits, address concerns, and present success stories in relatable terms.
• Access to Resources: Providing step-by-step guides and direct access to experts for troubleshooting.

Then, after the champions experimented with these tools for a month, we launched peer-led initiatives, as part of a broader communication and education campaign for Gen AI rollout. The champions helped facilitate a range of activities to support adoption, such as:

• Workshops and Demonstrations: Hosting informal sessions to showcase how Gen AI optimized tasks like inventory forecasting and customer query management.
• One-on-One Mentoring: Offering tailored support to colleagues struggling with the technology.
• Sharing Quick Wins: Regular updates highlighting efficiencies gained through Gen AI to build momentum.
• Public Recognition: We publicly recognized, praised, and celebrated early Gen AI adopters, through highlighting them in the company newsletter, at town halls, and through innovation awards.

Within six months, the organization achieved:

• 70% Adoption Rate: A majority of employees actively used Gen AI tools.
• Improved Operational Efficiency: Inventory management accuracy increased by 25%, and customer service resolution times dropped by 30%.
• Enhanced Employee Morale: Staff transitioned from apprehension to enthusiasm, seeing Gen AI as an enabler rather than a threat.

This case underscores the power of leveraging early adopters to drive technological change.

How to Maximize the Impact of Gen AI Champions

Organizations aiming to replicate these successes should follow a structured approach to empower their Gen AI champions.

1. Select the Right Champions: Identify individuals who are enthusiastic about technology, respected by their peers, and adept at communicating. Consider representatives from multiple departments to ensure broad relevance.
2. Invest in Comprehensive Training: Equip early adopters with a thorough understanding of Gen AI tools, as well as the skills to teach and advocate for them. Provide ongoing access to resources and experts.
3. Encourage Peer-Led Learning: Support early adopters in hosting workshops, one-on-one mentoring, and informal “lunch-and-learn” sessions. Create opportunities for them to demonstrate real-world applications of Gen AI.
4. Promote Success Stories: Highlight early wins achieved through Gen AI. Encourage champions to share personal anecdotes and practical examples of how the tools have made their work more efficient or rewarding.
5. Recognize Contributions: Acknowledge the efforts of early adopters publicly to reinforce their value and encourage others to embrace the technology.

The Bigger Picture: Building a Culture of Innovation

Leveraging early adopters is not just a tactical move; it’s a strategic approach to fostering a culture that embraces innovation while ensuring risk management. By involving employees at every level in Gen AI adoption, organizations can break down resistance and build a workforce that sees change as an opportunity rather than a threat.

• The Role of Leadership: Leadership plays a critical role in supporting early adopters. By providing them with the necessary tools and autonomy to lead, managers signal their commitment to a collaborative and inclusive transition.
• Future-Proofing the Workforce: As technology continues to evolve, the skills and confidence gained from Gen AI adoption will prepare employees for future changes. Early adopter programs create a template for rolling out other innovations, ensuring the organization remains agile and competitive.

Key Takeaways

Gen AI offers extraordinary potential, but its success depends on more than the technology itself. The people within the organization—especially early adopters—are the true drivers of meaningful change. By empowering these champions to lead the way, businesses can create a ripple effect of trust, collaboration, and enthusiasm that ensures a smoother, more successful transition.

Key Take-Away

AI champions accelerate Gen AI adoption by building trust, mentoring peers, and showcasing real-world wins, turning resistance into engagement, boosting efficiency, and creating a culture of innovation that drives lasting organizational change.
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Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Why the Best Gen AI Champions Are Hiding in Plain Sight appeared first on Disaster Avoidance Experts.

A rule that would have sounded extreme two years ago is starting to look like standard management practice. According to a recent WRITER and Workplace Intelligence survey, 60% of companies say they plan to lay off employees who refuse to adopt AI, 77% of executives say resisters will lose out on promotions, and 92% say they are deliberately building an “AI elite” inside their organizations. Association leaders should not treat those numbers as a corporate sideshow. They are an early warning. As ASAE has argued, members are already integrating these tools into daily decisions, and associations are running out of time to redefine where they add indispensable value.

For associations, Gen AI is not just another software decision. It reaches into member service, education, credentialing, chapter support, volunteer management, and the standards and best practices members expect their association to model. The real question is no longer whether Gen AI belongs in the association. It is whether the association will shape its use with enough discipline to strengthen trust instead of diluting it.

The New Baseline Is Arriving Faster Than Governance

The speed of this shift is easy to underestimate. The World Economic Forum’s Future of Jobs Report 2025 says 39% of core job skills are expected to change by 2030, with technology driving much of that change. At the same time, McKinsey’s 2025 workplace report found that nearly every organization is investing in AI while only 1% consider themselves mature in deployment. That gap between pressure and preparedness is where most associations now live.

The pattern is already visible across the sector. ASAE’s recent reporting says many associations are using AI for content creation and data analysis while still feeling underprepared on strategy, workforce impact, and data security. Another ASAE analysis of global association practice points to the same promise areas: member services, marketing and communications, and reporting. In other words, Gen AI is already entering the operating core before many boards and executive teams have set clear rules for where it should and should not go.

Gen AI Reprices Association Work Before It Replaces It

That does not mean every staff role or volunteer function is about to vanish. It means the economics of routine work are changing quickly. A widely cited National Bureau of Economic Research study on generative AI in customer support found a 14% productivity lift on average, with the biggest gains going to less experienced workers. For associations, that matters because so much value depends on small teams handling large volumes of repeatable work: member questions, chapter leader onboarding, meeting summaries, content repurposing, renewal outreach, and first-draft communications.

The most promising association uses are not glamorous. They are operational. ASAE has noted that experimentation works when staff and volunteers are actually empowered to test Gen AI inside real workflows. It works even better when those pilots are cross-functional. In one ASAE example, a legal association built a Gen AI research assistant by bringing together continuing education leaders, IT staff, and member volunteers.

A representative case makes the point. Consider a midsize national association with several dozen chapters, multiple member sections, a credentialing program, and a lean headquarters team. As a consultant helping that association apply Gen AI, we did not start with a flashy enterprise rollout. The smarter move is a governed pilot in three places: first-draft responses to common member inquiries, chapter and section leader toolkits, and staff preparation for volunteer meetings. Within weeks, the association saw where Gen AI speeds the work, where it creates risk, and where human review remains nonnegotiable. The payoff is not just efficiency. It is consistency across chapters, better support for volunteer leaders, and more time for staff to focus on judgment, relationships, and strategy.

Governance Is the Product

Associations face a harder test than most employers because they do not just serve members. They also signal what responsible practice looks like for a field. That is why governance cannot sit behind innovation. It has to lead it. ASAE’s launch of the Association Coalition for AI framed the issue correctly: responsible adoption is inseparable from public trust.

That applies internally first. A chapter president should not invent one Gen AI policy while a certification committee invents another and staff quietly use a third. WRITER’s enterprise adoption research shows how quickly these tools can create tension between leadership, IT, and the people expected to use them. Associations are especially vulnerable because authority is shared across boards, staff teams, chapter leaders, committee chairs, and member sections. Without common guardrails, Gen AI can amplify fragmentation instead of reducing it.

Training matters just as much as policy. ASAE has made the case that practical, sprint-style learning beats passive awareness sessions, especially for busy staff and wary volunteers. That is exactly right. Associations do not need more abstract enthusiasm. They need working norms, approved use cases, and enough fluency among staff and volunteers to model the standards they want members to follow.

The association that wins here will not be the one with the flashiest Gen AI demo. It will be the one that answers member questions faster, equips chapters better, supports sections more consistently, and publishes more credible guidance for the profession it serves. Gen AI fluency is becoming a condition of relevance. The real advantage, though, will come from knowing where the machine can scale the mission and where the association must still supply judgment, ethics, and trust.

Key Take-Away

AI skills are becoming a condition of relevance. Associations that pair AI adoption with strong governance, training, and human judgment will better serve members, build trust, and stay competitive in a rapidly changing workforce.
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Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post AI Skills Are Becoming the New Association Job Requirement appeared first on Disaster Avoidance Experts.

The introduction of generative AI (Gen AI) into workplaces offers remarkable potential for innovation and efficiency, but it often arrives with a shadow of doubt. Employees often view AI as a threat to their roles, fearing displacement, surveillance, or an overwhelming shift in their day-to-day responsibilities. These fears are not only understandable but also predictable, given the often hyperbolic narratives and misinformation surrounding AI. Addressing these concerns effectively smooths the path for AI integration while empowering employees to embrace the technology as a tool for growth.

Video: “Myth-Busting Gen AI Fears to Create a Culture of Confidence”

Podcast: “Myth-Busting Gen AI Fears to Create a Culture of Confidence”

Education for Myth-Busting Gen AI Fears

The first step in dispelling myths about Gen AI is education. Employees’ apprehension often stems from a lack of understanding about what Gen AI truly is, what it can do, and, critically, what it cannot do. Educational initiatives tailored to a company’s unique needs can illuminate Gen AI’s capabilities while debunking misconceptions.

In one case, I consulted for a mid-sized manufacturing firm that wanted to implement Gen AI for streamlining inventory management. However, employees feared that adopting AI would lead to job losses, particularly among warehouse staff. To address this, we organized a series of interactive workshops that demonstrated how Gen AI would automate repetitive tasks like inventory counting and reporting, allowing employees to focus on process optimization and customer service enhancements.

During these sessions, employees had the opportunity to interact with AI tools, gaining hands-on experience that demystified the technology. The workshops also provided a platform for candid discussions, enabling employees to voice their concerns and ask questions. By the end of the training, skepticism had transformed into curiosity, and employees began suggesting additional ways AI could support their roles. Follow-up surveys showed a marked increase in confidence, with 80% of participants reporting that they felt prepared to use Gen AI tools effectively. 

Beyond live sessions, companies can create accessible, on-demand resources to reinforce learning. Infographics, explainer videos, and detailed FAQs are powerful tools that provide clarity in a format employees can explore at their own pace. For example, in a project with a regional consulting firm, I recommended the development of a visual learning hub on their internal platform. This included videos showing AI tools automating client research and generating data-driven insights, as well as a step-by-step guide to integrating these tools into existing workflows. The accessibility of these materials ensured that employees at all levels, from interns to senior consultants, could grasp the nuances of AI and see its practical applications.

Leadership’s Role in Myth-Busting Gen AI Fears

Leadership plays a pivotal role in shaping employees’ perceptions of AI. Transparent communication from leaders about the company’s vision for Gen AI and its alignment with organizational goals can build trust. Employees are more likely to embrace change when they see that it is part of a thoughtful strategy rather than a knee-jerk reaction to industry trends.

In one instance, the leadership team of a regional healthcare provider held a series of town hall meetings focused on their AI initiative. Executives shared how AI was being introduced to enhance patient scheduling and administrative efficiency, emphasizing that it was not a replacement for clinical staff but a means to free them from mundane tasks.

These town halls included a question-and-answer segment where employees voiced concerns about job security and data privacy. Leadership’s candid responses, paired with real-life examples of AI implementation in similar settings, helped ease anxieties. By the end of the rollout, employees had a clear understanding of how AI would enhance their work and facilitate risk management rather than diminish their value.

Highlighting Early Wins to Build Confidence

Sharing success stories within the company can further bolster trust and engagement, creating a powerful narrative that transforms apprehension into enthusiasm. When employees witness tangible benefits from AI implementation, they begin to see the technology not as a threat but as a resource that improves their roles and contributes to the organization’s success. Highlighting these early victories not only reassures employees but also sparks curiosity and innovation throughout the workforce.

At a quickly-growing D2C startup where I consulted, we faced initial resistance from employees who were concerned about the introduction of an AI-driven customer support tool. The skepticism stemmed from fears that the AI would replace their roles or create additional complexities in their workflow. To address this, we adopted a deliberate strategy of showcasing measurable, real-world outcomes from the tool’s deployment. Within weeks, the tool had reduced response times by 40%, a significant improvement that allowed support staff to redirect their energy toward resolving complex customer queries that required human empathy and problem-solving skills.

To amplify the impact of this success, we spotlighted stories of individual employees who benefited directly from the change. For instance, one support agent shared how the AI tool handled routine inquiries during peak hours, freeing up time for her to craft personalized solutions for high-value customers. These testimonials were shared in team meetings, internal newsletters, and even informal lunch-and-learn sessions, fostering a sense of ownership and pride among employees.

As employees observed the practical advantages of the tool, those who were initially resistant began to adopt a more positive outlook. Many became vocal advocates for the AI system, actively sharing ideas for further optimization and championing its potential benefits in cross-departmental conversations. This shift in perception created a ripple effect across teams, cultivating an atmosphere of curiosity and openness toward technological advancements.

The success of the AI-driven support tool had another important outcome: it inspired other departments to explore how AI could enhance their own processes. For example, the marketing team began investigating AI-powered analytics to identify trends and tailor campaigns more effectively, while the operations team piloted AI tools for inventory management. These cross-functional initiatives gained momentum, accelerating the company’s broader digital transformation strategy.

Ultimately, by celebrating early wins and framing AI as an enabler of greater efficiency and creativity, the startup not only eased employees’ fears but also fostered a culture of innovation. These stories, rooted in factual outcomes and authentic employee experiences, became a cornerstone of the company’s narrative, demonstrating how AI could serve as a collaborative ally in achieving organizational goals.

Conclusion

By prioritizing education, fostering transparent communication, and showcasing early wins, organizations can create a culture where employees feel empowered to embrace AI rather than fear it. The key lies in addressing concerns with empathy and providing clear, practical pathways for integrating AI into the workplace. With the right approach, companies can turn uncertainty into opportunity, building a future where humans and Gen AI work together seamlessly.

Key Take-Away

Busting gen AI fears in the workplace requires education, transparent leadership and early success stories.
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Image credit: Vlada Karpovich/pexels

Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Myth-Busting Gen AI Fears to Create a Culture of Confidence appeared first on Disaster Avoidance Experts.

Gen AI has moved past the novelty phase. It is now reshaping how organizations work, how people learn, and how standards spread through entire fields. The core finding of Stanford HAI’s 2026 AI Index is that capability, adoption, and investment are accelerating faster than most institutions can absorb. The latest McKinsey global survey on Gen AI and organizational rewiring reaches a similar conclusion: use is broad, but real operating change still lags. For associations, that gap matters more than it does for many organizations, because an association is both an operating institution and a signal-setting institution. It has to serve members well while also showing members what responsible adoption looks like.

Video: “Gen AI Is Rewriting the Association Operating Model”

Podcast: “Gen AI Is Rewriting the Association Operating Model”

Gen AI Changes the Work Before It Changes the Strategy

The first mistake association leaders make is treating Gen AI as a technology project. In practice, it arrives first as a workflow issue. It changes how staff draft member communications, summarize research, prepare education content, support certification, and respond to routine questions. It also changes the experience of volunteer leaders who run chapters, lead sections, review programs, and contribute expertise in limited slices of time. Research from NBER on Gen AI in customer support found a 14% average productivity gain, with even larger gains for less experienced workers. That matters in associations, where junior staff and volunteer leaders often carry uneven workloads and need faster access to institutional knowledge.

The opportunity is bigger than efficiency. A well-designed Gen AI layer can help chapter leaders produce cleaner agendas, help section councils compare member feedback, and help staff turn scattered expertise into more usable guidance. But the gain only appears when leaders redesign the work itself. Stanford’s AI Index report shows how quickly Gen AI has spread; McKinsey’s findings on workflow redesign show why adoption without redesign rarely creates lasting value.

Governance Is Now a Member Service Issue

The second mistake is assuming Gen AI lives only inside software tools. It now sits inside infrastructure, policy, trust, and public expectations. The International Energy Agency’s analysis of energy and AI makes clear that this revolution runs on data centers, electricity, and physical capacity, not just clever interfaces. That reality should push associations to ask harder questions about vendor dependence, data handling, cost, and long-term resilience.

Governance is just as urgent. When chapter volunteers, board members, faculty, and staff all start using Gen AI in different ways, inconsistency becomes a risk. The NIST Gen AI profile within its AI Risk Management Framework offers a practical model for identifying risks around accuracy, privacy, and oversight. The EU AI Act framework underscores the broader direction of travel: expectations are moving toward transparency and accountability. For associations, this is not just a compliance story. Members look to associations for standards, ethics, and examples. If an association cannot explain how it uses Gen AI in education, certification support, content creation, or volunteer operations, it will struggle to guide members on the same questions.

A Case Study in Applied Gen AI

In one recent consulting engagement, I worked with a midsize national association with a credentialing program, a large annual meeting, a dozen chapters, and several member sections serving different career stages and practice interests. Leadership had AI enthusiasm but no shared operating model. Staff were experimenting individually, chapter volunteers were using public tools without guidance, and the board wanted the organization to lead its field without taking unnecessary risk.

We started with the work, not the tools. I helped the association map five high-friction workflows: member inquiries, meeting-content development, chapter communications, knowledge retrieval, and first-draft policy guidance. From there, we set rules for where Gen AI could assist, where human review was mandatory, and where it should not be used at all. We built prompt libraries for staff, simple playbooks for chapter leaders, and review standards for sections producing member-facing content. The result was not a dramatic robot takeover. It was steadier execution. Staff cut drafting time, volunteers gained confidence, and leadership finally had a consistent story about responsible use. That kind of practical experimentation mirrors what ASAE has described as a competitive advantage from Gen AI experimentation and the new skills shift association leaders are being urged to address.

Conclusion

The real issue for associations is no longer whether Gen AI matters. It does. The question is whether leadership will treat it as a scattered toolset or as a new operating layer that affects staff, volunteers, chapters, sections, and members all at once. Associations that move early, govern clearly, and teach by example will do more than improve internal efficiency. They will help set the terms of trust for the people and fields they serve, at a moment when public confidence and expert confidence still diverge sharply.

Key Take-Away

Gen AI is rewriting how associations operate and lead, delivering productivity gains, reshaping workflows, and making governance, trust, and responsible adoption essential for serving members and setting industry standards.
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Image credit: Gustavo Fring/pexels

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders stop overpaying for AI while boosting engagement and innovation. He serves as the CEO of the AI consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his forthcoming book with Georgetown University Press is The Psychology of Generative AI Adoption (2026). His most recent best-seller is ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI (Intentional Insights, 2023). His cutting-edge thought leadership was featured in over 650 articles and 550 interviews in Harvard Business Review, Inc. Magazine, USA Today, CBS News, Fox News, Time, Business Insider, Fortune, The New York Times, and elsewhere. His writing was translated into Chinese, Spanish, Russian, Polish, Korean, French, Vietnamese, German, and other languages. His expertise comes from over 20 years of consulting, coaching, and speaking and training for Fortune 500 companies from Aflac to Xerox. It also comes from over 15 years in academia as a behavioral scientist, with 8 years as a lecturer at UNC-Chapel Hill and 7 years as a professor at Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

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