As AI transforms how we work, learn, and build, training programs can’t stay static. Generative AI (Gen AI) is moving too fast for one-off courses or fixed curriculums. To keep pace, learning programs need to evolve in real time — using data, feedback, and real-world results to stay relevant, practical, and aligned with what organizations actually need next.

The Imperative of Continuous Improvement for Gen AI Initiatives

Static training programs risk irrelevance as business priorities change and technologies progress. This is particularly true for Gen AI, where quick advancements necessitate regular updates to training content and methodologies. Continuous improvement ensures that learning programs remain effective, engaging, and aligned with organizational goals. 

At the heart of this process are two critical components: feedback from participants and data-driven insights.

Participant feedback provides invaluable qualitative insights into the effectiveness of a learning program. Employees can share their experiences, highlighting what worked well, what was challenging, and what could be improved. 

This feedback can be collected through surveys, focus groups, interviews, or even informal discussions. When analyzed systematically, it provides a clear picture of the program’s strengths and areas for refinement.

For example, imagine a training module on advanced Gen AI concepts that multiple employees describe as overly complex. As a consultant who encounters such situations frequently, I would recommend breaking the module into smaller, more digestible sections or adding supplemental resources such as video tutorials or peer-led study groups. 

These adjustments can make the content more accessible, ensuring that employees grasp critical concepts effectively.

Quantitative data complements qualitative feedback by providing measurable indicators of a program’s performance. Metrics such as engagement rates, assessment scores, and completion rates can identify trends and patterns that inform targeted improvements. For instance, if data reveals that interactive simulations consistently result in higher engagement and better learning outcomes, an organization can expand the use of this approach across its training modules.

In one case, a client I worked with, a mid-sized software development firm, was struggling with low engagement in its Gen AI training program. By analyzing data from the program’s learning management system, we discovered that employees were more engaged with interactive content than with traditional lectures. 

Based on these insights, we redesigned the program to include more hands-on activities, such as simulated Gen AI problem-solving scenarios. This change not only boosted engagement but also improved the employees’ ability to apply their learning to real-world challenges.

Feedback and data-driven insights also ensure that Gen AI learning programs stay aligned with an organization’s strategic objectives. As business priorities alter, learning initiatives must adjust to reflect these changes. 

For instance, if a company begins prioritizing AI-driven decision-making, its training program should evolve to include advanced topics such as machine learning, data analytics, and ethical considerations in AI.

This alignment was critical for a global financial services firm I consulted for. The company wanted to integrate Gen AI tools into its decision-making processes but found that its workforce lacked the necessary skills. By developing a targeted training program informed by feedback and data, we equipped employees with competencies in areas like AI ethics, managing risks, and predictive analytics. 

Regular updates to the curriculum ensured the training remained relevant as the firm’s AI capabilities expanded.

Client Case Study: Gen AI Initiatives at a Mid-Sized Legal Firm

A mid-sized legal firm with just over 100 staff faced significant challenges with its Gen AI training program. The firm had invested heavily in upskilling its workforce but found that many employees were disengaged and struggled to apply their learning effectively. Recognizing the need for a comprehensive overhaul, the firm brought me on board as a consultant.

The first step was to gather participant feedback through surveys and focus groups. Employees reported that the training modules were too theoretical and failed to connect with their day-to-day responsibilities. Using this feedback, we redesigned the curriculum to include practical applications, such as legal case studies relevant to their roles and exercises on drafting contracts with the assistance of Gen AI tools.

Next, we analyzed data from the existing program to identify additional areas for improvement. Completion rates were particularly low for modules that relied heavily on generic training on Gen AI practices. By integrating case studies more relevant to law firms, such as prompts for drafting various legal documents, we made the content more engaging and accessible.

Finally, we aligned the program with the firm’s strategic goals. As the firm aimed to enhance efficiency and accuracy in legal document review, the revised training program included advanced topics such as using Gen AI for contract analysis, AI ethics in law, and integrating AI tools into client advisory workflows. 

The results were transformative. Engagement rates soared, with completion growing by 56%, and employees reported 49% higher satisfaction with the training. Moreover, the firm saw tangible improvements in how AI tools were utilized in legal research and documentation, with a 36% productivity boost. 

This experience underscores the importance of a data- and feedback-driven approach to continuous improvement in Gen AI training programs.

Creating a Culture of Continuous Learning

Beyond improving specific training programs, continuous improvement supports a culture of learning and innovation within an organization. When employees see that their feedback is valued and that the organization is committed to providing high-quality learning experiences, they are more likely to stay engaged and invest in their development.

This was evident in another client, a multinational manufacturing company. By embedding feedback mechanisms and data analysis into all their learning initiatives, the company not only improved its Gen AI training but also inspired employees to take ownership of their professional growth. 

Over time, this culture of continuous learning became a key driver of the company’s innovation and competitiveness.

Practical Steps for Implementing Continuous AI Improvement

For organizations looking to adopt a continuous improvement model for their Gen AI learning programs, the following steps are essential:

1. Establish Feedback Mechanisms: Develop structured channels for gathering participant feedback, such as post-training surveys or regular focus groups.
2. Analyze Performance Data: Use quantitative metrics to assess the effectiveness of different program components and identify trends.
3. Iterate and Adapt: Be prepared to make iterative changes based on insights from feedback and data.
4. Engage Stakeholders: Involve employees, trainers, and leadership in discussions about program improvements to ensure alignment with organizational goals.
5. Communicate Changes: Keep participants informed about how their input has influenced program updates, reinforcing the value of their feedback.

Conclusion

In an era of rapid technological advancement, static learning programs are no longer sufficient. Continuous improvement driven by feedback and data is essential for ensuring that Gen AI training programs remain relevant, effective, and aligned with organizational objectives. 

The case studies demonstrate the transformative impact of this approach. By embracing continuous improvement, companies not only enhance their training outcomes but also build a culture of learning and innovation that prepares them for the challenges and opportunities of the future.

Key Take-Away

Gen AI initiatives succeed when training evolves continuously—using feedback and data to stay relevant, practical, and aligned with real business needs, driving higher engagement, skills application, and measurable 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 Feedback-Driven Training Could Save Your Gen AI Initiatives appeared first on Disaster Avoidance Experts.

Corporate leaders everywhere crave momentum. They seek progress, faster outcomes, and robust growth. Yet siloed teams struggle to integrate innovative technology in ways that produce long-term value. Gen AI, the next big wave of transformative technology, cannot be deployed successfully by a single team operating in isolation. That is why I advocate for cross-functional Gen AI committees, which unite diverse expertise and perspectives and ensure technology seamlessly aligns with strategic goals. Today, organizations are scrambling to incorporate Gen AI into every corner of operations, but the real competitive advantage emerges when Gen AI committees harness the power of collective knowledge to guide, optimize, and champion these initiatives from start to finish.

Video: “Cross-Functional Collaboration Drives Gen AI Excellence”

Podcast: “Cross-Functional Collaboration Drives Gen AI Excellence”

Gen AI Excellence via Cross-Functional Collaboration

Gen AI integration thrives when representatives from different parts of the business collaborate. Information Technology might spearhead the technical aspects, but finance, human resources, marketing, and operations hold knowledge that can make or break a launch. 

I once consulted with a mid-sized manufacturing company looking to leverage Gen AI to forecast demand and automate select processes. The senior leaders initially believed the IT department could handle the entire project. They assumed that data scientists and software developers, working by themselves, would build the perfect solution. That perception changed when I showed how marketing input shaped predictive analytics models, and how frontline employees’ perspectives on production timelines gave the project a ground-level understanding that mere data sets could never fully capture. 

The client formed a cross-functional committee that included IT professionals, a marketing director, an operations specialist, and a data-oriented HR representative who brought valuable insights into upskilling staff. This committee met frequently, shared domain-specific feedback, tested iterative versions of new tools, and ultimately produced a Gen AI forecasting system that improved production efficiency by over 30% and cut waste by 25%. The Chief Technology Officer fully acknowledged that working alone, IT wouldn’t have come close to achieving these outcomes. 

This approach unites teams under one mission: to embed Gen AI into strategic initiatives that solve real business challenges. In my experience, individuals often resist new technology when they sense it’s being forced on them by senior management or by a department that doesn’t grasp the full scope of their daily activities and fails to grasp the realities of each department’s risk management needs. 

Cross-functional committees eliminate that problem. They give employees a voice in the process. Regular dialogue between departments fosters buy-in because no one feels left behind. Instead, every participant sees his or her insights reflected in the final decision. That sense of ownership matters. It turns reluctant adopters into enthusiastic advocates. 

Effective Committee Composition for Gen AI Excellence

Some leaders worry that forming these committees is cumbersome. They ask whether people with different skill sets and priorities can collaborate without clashing. My answer is straightforward: the friction caused by diverse perspectives is exactly what makes these committees so effective. 

You want IT professionals who understand database security, HR specialists who can foresee how automation affects workforce morale, marketing directors who see how Gen AI can bolster customer engagement, and finance experts who evaluate potential savings. Each member contributes a fresh angle that illuminates corners of the business usually hidden from others. These committees  unify the organization’s purpose under a shared goal and drive progress that resonates across the entire enterprise.

In my consulting work, I once guided a consumer packaged goods (CPG) company seeking to apply Gen AI to inventory management. The supply chain lead quickly recognized that automating the reorder process could be transformative, but only if the algorithm accounted for market fluctuations that the marketing team diligently tracked. We put together a committee including the CFO, who cared about balancing capital locked in inventory, and a customer service manager, who worried about how automated ordering might impact shipping times and product availability. Meetings involved direct discussion of real challenges, not abstract debates.

Every participant pressed each other to explain why certain operational constraints existed. Discussions were lively, and disagreements arose, yet each friction point sparked a more refined solution. Ultimately, the committee designed a system that cut inventory costs by 15% in the first quarter of launch, and another 10% in the second quarter. The CFO’s perspective ensured the algorithm included real-time budgeting triggers, while the marketing department’s input enabled more precise demand forecasting.

I see that synergy repeated in many of my engagements. The tension of varied perspectives helps anticipate problems early in the design phase. Implementation timelines shorten. Resistance diminishes. Workflows flow. 

Technology projects often stumble when decision-making excludes or underrepresents particular voices. Gen AI committees prevent that pitfall by welcoming relevant stakeholders who test assumptions from every angle. Imagine trying to solve a Rubik’s Cube using only one side. That’s how many companies operate when they relegate key decisions to a single department. Cross-functional committees fix that inefficiency by compiling a mosaic of skills that align to produce solutions that stick.

Steering Implementation for Sustained Value

Cross-functional committees serve another crucial function. They help you identify and prioritize use cases for Gen AI with clarity. IT alone might fixate on system integration, whereas a marketing department might prioritize predictive analytics to shape product launches. By synchronizing these visions, the organization can evaluate which projects deliver the greatest return. 

Think of it as risk mitigation. If one department misjudges an emerging risk or an unforeseen bottleneck, someone else in the committee spots it. This ensures that the rollout proceeds smoothly, with minimal wasted resources.

My consulting firm intervened in one recent case where a healthcare enterprise needed to adopt Gen AI for patient billing automation. Leaders worried about compliance with privacy regulations, while patient-facing nurses worried about possible disruptions to the personal aspect of patient care. The newly formed committee pulled in experts from legal, IT, billing, and patient care teams. 

We conducted a pilot rollout with a few specialty clinics to stress-test the technology. The pilot revealed that front-office staff needed more training on adjusting codes for unusual billing scenarios. Without that insight, the entire system might have bottlenecked or even triggered a compliance red flag. Because the pilot was carefully orchestrated by a diverse group with a mandate to test each facet, the committee fine-tuned the solution, provided targeted staff training, and delivered a final product that saved staff hours without compromising patient experiences.

This iterative approach is essential for any Gen AI initiative. Rather than presenting a finished product to the organization in one swoop, committees release early versions, gather feedback, integrate what they learn, and refine processes with each cycle. This generates momentum and confidence. People see tangible benefits within weeks or months, not years. They speak up about functionality that needs improvement, and the committee makes swift revisions to keep morale and efficiency high. Over time, this cycle fosters an environment where employees not only trust Gen AI but also champion continued innovation, which drives sustainable growth.

Why This Matters Now

Organizations often overlook the power of broad-based involvement. They assume senior leaders or technical experts know best. Gen AI, due to its vast potential, requires nuance and creativity that flourish when everyone who might be touched by the technology has a seat at the table. When committees guide development, employees become co-creators rather than passive recipients. Adoption accelerates. Resistance falls away. That kind of buy-in is indispensable, particularly as today’s markets reward agility. 

Gen AI tools evolve, and so must your teams. Cross-functional committees create channels of continuous feedback. They transform conflict into productive dialogue. They help you find overlooked synergies that lead to breakthroughs in everything from cost savings to customer satisfaction.

Innovation rarely follows a smooth path. It thrives on diverse perspectives that reveal hidden stumbling blocks. Cross-functional committees, in my experience, represent the surest way to harness that diversity so Gen AI investments fulfill their promise. They reduce friction and unite different parts of the organization around a shared vision. Leaders who embrace this approach see technology adoption move faster, yield greater returns, and spur deeper employee engagement. Workers relish the chance to shape Gen AI’s direction, and clients benefit from solutions that solve real, day-to-day pains. Committees present a practical, user-friendly route to achieving business goals in a hyper-competitive environment. They funnel each department’s best ideas into an integrated blueprint for success.

Conclusion

When Gen AI is championed by a cross-functional committee, the entire organization feels its value. People who once feared automation or data analytics gain confidence because they see how the new processes make work more efficient and rewarding. There is a tangible sense of unity in purpose, and that emotional energy fosters a culture ready to embrace what comes next. As I always tell my clients, the sweet spot of innovation emerges when leaders encourage broad collaboration. Cross-functional Gen AI committees represent that sweet spot, bringing together the brightest minds, bridging departmental gaps, and building a united front that propels an organization toward sustainable growth.

Key Take-Away

AI excellence is achieved when cross-functional teams collaborate on Gen AI, combining diverse expertise to drive faster adoption, stronger buy-in, reduced risk, and sustainable business value beyond what any single department can deliver.
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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 Cross-Functional Collaboration Drives Gen AI Excellence appeared first on Disaster Avoidance Experts.

Transparency is not a luxury in today’s transformative projects—it is a necessity. As organizations integrate Generative AI (Gen AI) into their operations, the importance of clear, consistent communication around project milestones and outcomes cannot be overstated. Employees are not passive observers of this journey. They are active participants whose trust and engagement are pivotal to the success of these initiatives, since leaders can’t effectively micromanage Gen AI use. When employees see concrete evidence of progress and understand both achievements and challenges, they feel more connected to the transformation, fostering a culture of inclusion and shared purpose.

Video: “What Leaders Lose When They Hide Gen AI Truths”

Podcast: “What Leaders Lose When They Hide Gen AI Truths”

Creating a Tangible Narrative About Gen AI Truths

When an organization commits to transparency, it crafts a narrative that employees can follow and in which they can believe. Each milestone is a chapter in a story of transformation, illustrating where the organization stands in its AI journey and how the future might unfold. Without this transparency, employees are left to fill in the blanks, which often leads to skepticism or resistance.

Consider a consumer goods company I worked with that wanted to deploy an AI-driven customer service tool. The leadership had ambitious goals, including cutting customer response times by 40% and improving customer satisfaction scores by 20%. However, when the rollout faced early hiccups, including integration delays and low initial adoption rates, the leadership team hesitated to share these challenges with employees.

As their consultant, I emphasized the critical role of transparency in regaining trust. Together, we devised a communication plan that framed the setbacks as opportunities for growth. By sharing the hurdles—like how the tool struggled with regional dialects in customer queries—and the steps being taken to address them, employees saw leadership as honest and committed. Moreover, they were now much more willing to provide feedback on improvements to address rollout challenges, which substantially improved the way the company customized the Gen AI tool to its needs. When the company later achieved its 40% response-time goal, employees felt a shared sense of accomplishment, not because the result was perfect, but because they had been part of the journey and contributed substantially to improving the final standard operating procedures (SOPs) of the Gen AI tool use.

This tangible narrative, built through honest updates, not only reduced resistance but also galvanized employees to champion the technology themselves. But transparency must extend beyond the wins. When challenges arise—such as delays in integrating AI into legacy systems—acknowledging them demonstrates that leadership values honesty over spin. This approach creates a culture where setbacks are not failures but opportunities to learn and adapt. For example, if the deployment of an AI-powered data analytics platform faces setbacks due to data inconsistencies, openly communicating these issues and explaining how they will be addressed builds credibility and shows resilience. Employees are more likely to remain patient and supportive when they see that leadership is committed to navigating obstacles thoughtfully.

The Power of Regular Updates on Gen AI Truths

Imagine the transformative potential of framing AI integration as a collective journey rather than a top-down directive. Regular updates are the fuel that keeps this journey alive in the minds of employees. When milestones are achieved—whether automating 10% of back-office processes or completing a successful pilot program—sharing these achievements reinforces that the organization is moving steadily toward its goals.

Take the case of a retail client that used AI to optimize inventory forecasting. The company initially planned to improve forecasting accuracy by 15% within the first year. Instead of waiting for the final results, we implemented a phased communication strategy that highlighted incremental progress. For instance, at the six-month mark, they shared how a 7% improvement had reduced inventory waste and freed up working capital.

These updates transformed what could have been abstract metrics into concrete benefits. Employees in procurement and logistics saw the direct impact on their workflows, leading them to proactively suggest ways to further integrate AI insights. The periodic updates, coupled with open forums for employee feedback, reinforced the collective nature of the initiative.

By celebrating milestones and acknowledging input, the company cultivated a sense of ownership among employees. It wasn’t just a corporate initiative; it was their initiative, propelled forward by their engagement and ideas. By the end of the year, we achieved a more than 20% improvement in forecasting accuracy due to employee insights.

Outcomes as Lessons and Inspiration

Transparency in communicating outcomes—both good and bad—is a cornerstone of continuous improvement. Sharing success stories allows departments to learn from one another. For instance, if a marketing team leverages AI to personalize customer interactions, leading to a 20% increase in engagement, this success can inspire other departments to adopt similar approaches. Conversely, when outcomes fall short of expectations, such as a chatbot failing to meet customer satisfaction benchmarks, discussing these lessons openly fosters a culture of experimentation and resilience.

One of my most rewarding projects involved a healthcare provider rolling out a Gen AI scheduling tool. The goal was to improve staff allocation and reduce patient wait times. Initial results were mixed: while wait times dropped significantly, some employees felt sidelined by the tool’s lack of flexibility. Transparency became our north star.

We hosted interactive sessions where leadership explained both the tool’s successes and its shortcomings. They also invited employees to provide feedback and propose adjustments. For example, nurses suggested additional variables—like last-minute emergency appointments—for which the Gen AI tools hadn’t accounted. Incorporating this input not only improved the tool but also demonstrated that employee voices were integral to the process.

This openness inspired a shift in perception. What began as a divisive initiative evolved into a collaborative effort, where employees viewed challenges as stepping stones to better outcomes.

Building Trust Through Inclusive Communication

Trust is the bedrock of any successful transformation, and transparent communication is its foundation. Employees need to feel that they are partners in the organization’s journey with Gen AI, not merely bystanders. By openly sharing milestones, celebrating successes, and acknowledging challenges, leaders can create a sense of shared ownership that motivates employees to actively engage with the initiative.

For instance, a fintech company I consulted for was automating its fraud detection process using machine learning. To build trust, we organized quarterly updates that combined data-driven results—like a 30% reduction in fraudulent transactions—with human stories. Employees in customer service shared how the tool made it easier to reassure affected customers, turning a stressful task into a smoother process.

Involving employees in milestone celebrations also played a key role. At the one-year mark, the company hosted a town hall to recognize the teams that supported the AI rollout. The celebration was not just about the tool’s success but also about the collective effort behind it. This inclusive approach reinforced the message that AI adoption was not about replacing employees but enabling them to thrive in their roles.

Conclusion

Transparency in communicating Gen AI project milestones and outcomes is more than a best practice—it is a strategic imperative. It builds trust, reduces resistance, addresses risks, and fosters a sense of shared purpose. By openly sharing both successes and challenges, organizations can create a culture where employees feel engaged and confident in the AI journey. When employees understand how milestones connect to broader strategic goals and see their roles as vital to achieving them, they become champions of change rather than skeptics.

In the end, Gen AI integration is not just about technology—it’s about people. Leaders who prioritize transparent communication will find themselves at the helm of an engaged, motivated workforce, ready to navigate the complexities of transformation and embrace the opportunities it brings.

Key Take-Away

Transparent communication of Gen AI milestones and outcomes builds trust, engagement, and shared purpose, turning challenges into lessons and employees into champions of change. This is the essence of AI truths.
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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 What Leaders Lose When They Hide Gen AI Truths appeared first on Disaster Avoidance Experts.

An association communication director stares at a blank page at 8:07 a.m., coffee cooling beside a half-finished brief. Ten years ago, that page would have pulled in a crowd: copy, art, strategy, maybe a junior team to feed the room. Today, the room can fit in a laptop, and the first sparks arrive in seconds.

A massive new experiment from the University of Montreal points to a clear turning point: generative AI now beats the average person on certain creativity tests, even with older models such as GPT-4 that are over a year out of date. The implication for creative work feels immediate.

Make Idea Abundance Serve Member Value

The peer-reviewed study compared multiple models with 100,000 humans and found that some systems exceeded average human performance on structured divergent creativity tasks. The researchers used the Divergent Association Task, then scored creative writing formats such as haiku and short fiction. For associations, this strength shows up in the work that drives renewal: naming a credential, shaping advocacy messages, drafting sponsor language, and building conference session titles. 

Abundance becomes value after selection and stewardship. A CEO can generate multiple membership value propositions, then rerun the best options through your value statement, DEI commitments, and style guidance. A volunteer committee chair can request alternatives for a public comment letter, then route one version through policy staff and counsel for accuracy and positioning. A credentialing director can draft item-writing concepts tied to a blueprint, then apply SME review and governance controls before piloting.

The study also highlights a creativity tail where highly creative people outperform the models as a group, at least on the older model tested in the study, GPT-4, which is now over a year out of date: more modern models may show different outcomes. That human advantage looks like cultural fluency, stakeholder empathy, and narrative precision that makes members feel seen across communities and chapters. 

Build The One-Person Studio Into Association Operations

A one-person studio succeeds when workflows, permissions, and data practices match association reality. Define repeatable lanes such as event promotion, member onboarding, CE product launches, board packets, and chapter toolkits, then turn them into prompt templates that carry approved policy language and accessibility requirements.

Evidence from a Harvard Business School study shows measurable performance differences when knowledge workers use generative tools on realistic tasks. Associations capture the same gains by measuring cycle time and quality in native workflows, including how fast a webinar page ships, how many edits sponsor deliverables need, and how quickly chapters receive compliant graphics that follow brand standards.

Governance readiness turns speed into trust. Use the AI RMF to assign owners for tool access, prompt libraries, review standards, and incident handling, and extend controls with the Gen AI profile for risks that show up in member-facing content. Keep member personal data out of general-purpose tools, publish an approved stack for chapters, and tie post-event reporting to sponsor outcomes and ongoing learning. 

Case Study: Member Services And Credentialing Studio For A Professional Association

I worked with a 45-employee association that wanted higher member value across member services, certification tracking, and event management while keeping spend aligned with fiscal realities. Vendors proposed a four-tool build priced at 175K plus a recurring 95K maintenance commitment, and leadership wanted a capability that staff and chapters could run.

I implemented my empowerment program for a 28K investment and set up a one-person studio model anchored in governance. We trained a cross-functional core spanning member services, credentialing, meetings, and a chapter liaison, then built shared prompt standards and approval workflows tied to board policy and brand rules. We kept personal data separated from generation, used secure sandboxes for experimentation, and built a chapter toolkit so local leaders reused approved scripts, emails, and graphics with consistent language.

In year one, staff built eight internal tools and launched five member services that improved responsiveness and reduced manual work. The association recorded a 27% retention lift alongside 140 staff hours saved weekly, and leadership used that time to expand CE programming and chapter support. Any association can apply the approach by investing in capability transfer and running governed prompts as shared assets.

Conclusion

Generative AI turns the one-person creative studio into an association advantage when leaders manage abundance through governance. Large-scale benchmarking shows that structured divergent creativity now scales through tools, and that reality shifts staffing toward direction, review, and member impact. Operational design completes the system: governed templates, chapter-ready standards, and event-driven measurement that strengthens sponsor value and CE revenue. Associations that pair speed with accountability ship work members recognize as useful, credible, and worth renewing for.

Key Take-Away

Association creative is shifting as AI enables idea abundance, but real value comes from human selection, governance, and empathy—turning fast outputs into trusted, member-centered outcomes that drive retention and 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 AI Is Making the One-Person Association Creative Studio a Reality appeared first on Disaster Avoidance Experts.

Microlearning has emerged as a dynamic approach to corporate education, breaking down complex topics into concise, focused lessons that are easier to digest and apply. For corporations striving to remain competitive in the age of generative artificial intelligence (Gen AI), this strategy offers a powerful way to upskill employees without disrupting daily operations.

By delivering bite-sized, actionable content tailored to specific roles, microlearning empowers employees to absorb information at their own pace, practice what they’ve learned, and quickly apply new skills. For businesses navigating the complexities of digital transformation, this approach provides the agility needed to stay ahead of the curve.

Video: “Microlearning Offers A Flexible Approach to Gen AI Education”

Podcast: “Microlearning Offers A Flexible Approach to Gen AI Education”

Why Corporations Need Microlearning for Gen AI Education

In today’s fast-paced business environment, corporate leaders face the challenge of equipping employees with the skills required to harness the power of technologies like Gen AI. The vast potential of Gen AI for streamlining processes, enhancing decision-making, and driving innovation makes it an essential area of focus. Yet traditional training programs, which often demand significant time and resources, are no longer practical for many companies.

Microlearning offers a solution by making education flexible, personalized, and accessible. Lessons typically last 10–15 minutes and are delivered through formats that cater to different learning styles, such as videos, interactive exercises, and quizzes. This format is ideal for employees juggling demanding workloads, as it allows them to integrate learning into their schedules seamlessly.

Furthermore, microlearning ensures relevance by offering tailored learning paths. For example, a marketing team can focus on modules that explore Gen AI-powered audience segmentation, while a customer service team might learn about automated response systems and predictive analytics. This customization ensures that training is directly applicable, increasing engagement and retention.

Client Case Study in Gen AI Education: Microlearning in Action

To illustrate how microlearning can transform corporate training, consider the case of a multinational consumer packaged goods (CPG) firm that sought to integrate Gen AI into its operations. The company recognized the potential of AI tools to enhance productivity and innovation but faced several challenges:

1. Time Constraints: Employees were already stretched thin, managing tight deadlines and critical projects.
2. Skill Gaps: Teams varied widely in their familiarity with AI technologies, requiring training tailored to different levels of expertise.
3. Scalability: With offices spread across multiple time zones, delivering consistent, high-quality training to a global workforce was a major challenge.

To address these challenges, the company asked me to help it adopt a microlearning strategy.

Designing a Microlearning Program

We began by identifying the key areas where Gen AI could make an immediate impact, including sales forecasting, product development, and customer experience management. Working with subject matter experts, they created a series of microlearning modules tailored to specific roles and objectives.

For example:

• Sales Teams: Modules focused on using AI tools to predict customer needs, improve lead scoring, and optimize outreach strategies.
• Product Developers: Training covered AI-driven design tools and algorithms to accelerate prototyping and refine product features.
• Customer Support Teams: Lessons explored AI chatbots, sentiment analysis, and personalized service recommendations.

Each module was designed to be engaging and interactive, encouraging employees to apply what they learned immediately. The content was hosted on a mobile-friendly Learning Management System (LMS), ensuring accessibility for employees regardless of location or time zone.

Making Learning Flexible and Personalized

Flexibility was a cornerstone of the program. Employees could access the modules whenever it suited them, such as during breaks, commutes, or downtime between meetings. The LMS also included progress tracking, enabling participants to monitor their development and revisit areas where they needed additional support.

To enhance engagement, we helped the company incorporate gamification elements, such as badges and leaderboards, to motivate learners and celebrate achievements. Employees could also choose their own learning paths, selecting modules that aligned with their roles and career aspirations. This personalization ensured that training was not only relevant but also empowering, as employees felt a greater sense of ownership over their learning journey.

Support and Mentorship

From our experience with other companies, self-paced learning works best with guidance, so we helped the company pair the microlearning program with optional mentorship opportunities. Experienced AI practitioners within the organization served as mentors, hosting weekly virtual office hours where employees could ask questions and receive advice.

For instance, a sales manager might consult a mentor about integrating AI tools into an existing CRM system, while a customer support specialist could seek tips on optimizing chatbot responses for better customer satisfaction. These interactions provided valuable context and practical insights, reinforcing the concepts covered in the microlearning modules.

Results That Speak for Themselves

After six months, the microlearning initiative delivered measurable results across multiple metrics:

1. Increased Efficiency: Sales teams reported a 22% reduction in time spent on lead qualification, thanks to AI-enhanced processes.
2. Improved Innovation: Product developers cut prototyping time by 18%, enabling faster iteration and delivery of new products.
3. Enhanced Customer Experience: Customer satisfaction scores improved by 26%, as support teams used AI tools to provide quicker, more personalized service.

These results not only demonstrated the immediate impact of microlearning but also highlighted its long-term potential to drive operational excellence and competitive advantage.

Building a Culture of Continuous Learning

Beyond the tangible outcomes, the microlearning program had a profound effect on the company’s culture. Employees became more confident and proactive in experimenting with AI tools, sharing their learnings with colleagues, and proposing new applications for the technology.

For example, a marketing team used insights from their training to develop an AI-powered campaign that outperformed previous efforts by 30%. Similarly, a regional office implemented an AI tool for inventory management, significantly reducing waste and costs. These successes reinforced a culture of continuous learning and innovation, where employees were empowered to take initiative and explore the possibilities of emerging technologies.

Microlearning is not a one-and-done solution; it is a dynamic approach that evolves with the needs of the business. As Gen AI capabilities advance, companies can expand their training libraries to cover new applications, ensuring that employees remain at the forefront of innovation.

For example, future modules might focus on advanced AI ethics, regulatory compliance, or integrating AI into sustainability initiatives, while managing risks. By continuously updating and refining their microlearning programs, corporations can maintain a skilled and adaptable workforce ready to tackle the challenges of tomorrow.

The Strategic Advantage of Gen AI Education Through Microlearning

For corporations, microlearning offers a strategic advantage in an increasingly competitive landscape. It allows businesses to upskill employees quickly and efficiently, driving productivity and innovation while minimizing disruption. Moreover, by tailoring training to the unique needs of different teams and roles, microlearning ensures that every employee can contribute meaningfully to the company’s success. Whether it’s a sales representative using AI to close deals faster or an operations manager leveraging AI for process optimization, the benefits of this approach extend across the organization.

By embracing microlearning, corporations not only enhance their operational capabilities but also foster a culture of growth, adaptability, and forward-thinking. In an era defined by rapid technological change, this mindset is critical for long-term success. Microlearning represents the future of corporate education. Its ability to deliver focused, engaging, and personalized training makes it the ideal approach for equipping employees with the skills they need to thrive in the age of Gen AI. By adopting this strategy, corporations can ensure that their teams are not just keeping up with change but leading it, driving innovation and setting new benchmarks for success.

Key Take-Away

Microlearning accelerates Gen AI education by delivering flexible, role-based, bite-sized training that boosts productivity, drives innovation, and builds a culture of continuous learning without disrupting daily operations.
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Image credit: Luis Sevilla/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 Microlearning Offers A Flexible Approach to Gen AI Education appeared first on Disaster Avoidance Experts.

Picture a budget retreat for an association with national reach, dozens of chapters, and an annual conference that keeps the lights on. A Gen AI tool has produced next year’s membership forecast, estimated sponsorship revenue, and suggested changes to dues tiers. The narrative reads smoothly, the graphs look convincing, and one understated percentage change in the cash-flow section quietly flips the board’s view of risk. Staff, volunteer leaders, and section chairs walk out aligned around a story that never should have passed the first calculator check.

Video: “Should Associations Trust AI With Their Numbers?”

Podcast: “Should Associations Trust AI With Their Numbers?”

The Jagged Frontier Of Gen AI

This is exactly the class of problem the team at Omni Calculator set out to test with the ORCA Benchmark. Across 500 real-world quantitative tasks, including finance, health, and everyday math, no leading model scored above 63 percent. Users face a material chance of getting the wrong answer even for straightforward questions. A companion research paper on ORCA shows that most errors come from basic calculation and rounding mistakes rather than exotic logic failures. For associations that manage reserves, fund advocacy, and support chapters through shared services, that level of unreliability in Gen AI-generated numbers demands a deliberate response.

Harvard researchers describe this pattern as a jagged technological frontier. In their field experiment with 758 consultants, GPT-4 dramatically improved speed and quality on some tasks while degrading performance on other tasks that looked similarly difficult to human eyes. Their jagged frontier study shows that Gen AI capability does not rise smoothly with complexity. Instead, it spikes in some activities and drops in others, and even experienced professionals struggle to predict where the tool helps and where it harms. Workers did best when they had explicit guidance about when to lean on the system and when to rely on their own judgment. That lesson translates directly to association executives, staff, chapter officers, and section leaders. Access to Gen AI without training is a recipe for confident mistakes.

The ORCA findings reveal why this matters so much for numerically anchored work inside associations. Large language models behind Gen AI are designed to predict the next token in a sequence, not to execute deterministic arithmetic. The ORCA team reports that even when a model describes the correct formula for interest, amortization, or present value, it often misapplies that formula in multi-step calculations. Users have roughly a 40 percent chance of receiving a wrong answer on everyday math problems, with compounding errors common in financial reasoning. For associations, that touches dues modeling, conference pricing, reserve drawdown scenarios, scholarship funding, and chapter support formulas.

Where Associations Are Most Exposed

Regulators and standards bodies are starting to codify this risk. The U.S. National Institute of Standards and Technology released a dedicated Generative AI Profile as a companion to its AI Risk Management Framework, warning that Gen AI systems confabulate, producing fluent but false content, including incorrect logic and fabricated references. A practical summary of the same document explains how the profile catalogues risks such as over-reliance, automation bias, and opaque failures, and offers controls tailored to generative tools. An accessible overview of the guidance notes that the Gen AI profile exists precisely because organizations struggle to see where language fluency masks underlying unreliability. Associations sit in that crosshairs: they use narrative to persuade boards and members, and they rely on arithmetic to budget, price, and manage risk.

Governments are already experiencing how fast Gen AI adoption grows once the door opens. A recent GAO report on federal Gen AI use found that across 11 major agencies, reported generative use cases rose from 32 in 2023 to 282 in 2024, a ninefold jump. A related summary from the Defense Management Institute underscores that total AI-related use cases nearly doubled in the same period, with generative tools filling a growing share of mission support work. Analysts reviewing the GAO findings stress that agencies now need structured governance and training simply to keep up with their own pilots. Associations face a similar pattern at smaller scale: once staff, committees, and chapters start using Gen AI for member communication, education content, and basic analysis, usage accelerates and informal habits harden into de facto practice.

For associations, the jagged frontier has three direct implications. Gen AI excels at language-heavy tasks with loose numerical stakes, such as drafting policy briefs, segment-specific newsletters, or conference session descriptions. It performs inconsistently on tasks that blend narrative with arithmetic, such as sponsorship forecasts or multi-tier membership projections. It also underperforms as a primary calculator for high-stakes, multi-step financial reasoning. The right response is not to ban Gen AI, but to teach staff and volunteers exactly where it belongs in the process and to pair it with deterministic tools whenever money, risk, or member fairness depend on the numbers. The ORCA team’s public benchmark summary explicitly recommends this pattern: let Gen AI structure the problem and explain it, while trusted calculators handle the math.

A Case Study In Association Gen AI Governance

Consider a concrete case from my own work. Envision yourself as a consultant engaged by a national association with roughly 40,000 members, strong chapter activity, and sections for early-career professionals, senior leaders, and international members. The CEO and board want to use Gen AI everywhere for operational efficiency and to set an example for the field. You propose a structured approach rooted in the jagged frontier. In the first phase, you inventory how staff, chapter officers, and section volunteers already use Gen AI for budgeting, member outreach, and program design. You then run a simple awareness session where you explain the Harvard jagged frontier research and walk through examples from their own work where Gen AI shines and where it fails.

Next, you help the finance and membership teams redesign their workflows. Gen AI drafts narrative explanations for dues recommendations, scenario descriptions, and board memos. Deterministic tools perform every calculation that touches cash: dues projections by segment, conference break-even models, grant allocation formulas, and reserve stress tests. Staff cross-check any Gen AI-generated number against a vetted calculator, including public tools such as Omni’s APY calculator for interest-based scenarios or internal spreadsheets for margin analysis. Together, you define clear rules that treat Gen AI outputs as drafts that gain authority only after verification.

Then you extend that discipline to the network. Chapter treasurers receive a short playbook that explains where Gen AI supports them, for example in drafting member updates or translating guidance into local context, and where they must avoid using it as a calculator. Section leaders responsible for specialized content learn to treat Gen AI as a research and drafting partner, not as a source of final statistics. Across each audience, training emphasizes the same three messages: Gen AI is uneven, numbers that matter require independent checks, and the association expects that discipline as part of its culture of professionalism.

The payoff in this case is twofold. Internally, the association reduces the risk of Gen AI-driven miscalculations in budgets and pricing while still unlocking time savings in drafting and analysis. Externally, it models a mature standard for members who are wrestling with the same technology in their own organizations. When board members see that their association uses Gen AI to accelerate work, yet insists on calculator-backed numbers and staff trained in the jagged frontier, they see a credible blueprint rather than hype.

For association leaders, the message from ORCA and Harvard’s jagged frontier research converges on a simple principle: do not trust Gen AI with critical numbers unless a human and a deterministic tool stand between the model and the decision. Gen AI already changes how associations write, plan, and serve members. The question now is whether those changes come with quiet arithmetic errors or with clear guardrails, training, and verification. Associations that choose training over blind trust, and calculators over wishful thinking, will protect their finances, support their volunteers, and set a standard their members can confidently follow.

Key Take-Away

Gen AI excels in drafting but falters in calculations, so pairing it with verified tools, training, and human judgment is essential to avoid costly, confident mistakes.
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Image credit: Yan Krukau/pexels

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders replace overpriced vendors with staff-built AI solutions. He serves as the CEO of the future-of-work 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 Should Associations Trust AI With Their Numbers? appeared first on Disaster Avoidance Experts.

Learning About AI Best Practices

The first step in the journey towards effective use of generative AI is to look outward. Companies that have already integrated AI into their operations can serve as valuable case studies. These pioneers have navigated the challenges of implementation, and their successes and failures provide a roadmap for others to follow.

For instance, a number of companies report having marketing teams using AI to generate creative content, while the sales team uses it to predict customer behavior. By identifying these practices, companies can consolidate their efforts and create a unified strategy for AI usage. 

The second prong of this approach involves looking inward. Companies must understand how their own employees are already using generative AI. This internal audit can reveal surprising insights about the company’s current AI capabilities and areas for improvement. This internal exploration is not just about finding existing uses of AI, but also about encouraging employees to come forward with their ideas and experiences. This can foster a culture of innovation and make the integration of AI a company-wide endeavor. 

However, it’s at this step that I most often see problems in companies for which I consult on integrating AI into their workflow. Initial evidence suggests that AI can significantly boost personal efficiency for individual employees by anywhere from 20% to 70% for many tasks, with the quality of output surpassing that of tasks completed without AI assistance. This is a testament to the transformative power of AI when used as a personal productivity tool, especially when operated by someone within their area of expertise.

It’s important to note that the current state of AI primarily enhances individual productivity rather than organizational productivity as a whole, as highlighted by Ethan Mollick, a professor at the Wharton School of the University of Pennsylvania. This is because AI, in its current form, makes for rather unpredictable software. It can be inconsistent, prone to error, and generally doesn’t behave in the way that traditional IT is expected to behave. As a result, AI doesn’t scale well in its current state.

But don’t let this deter you. The key is to recognize the potential of AI as a personal productivity tool and to harness this potential within your organization. By doing so, you can empower your employees, improve efficiency, and pave the way for the future integration of AI on a larger scale. As AI technology continues to evolve, we can expect it to become more reliable and scalable, eventually becoming an integral part of organizational productivity.

Establishing an AI Center of Excellence

Once a company has gathered this information, the next step is to establish a Center of Excellence for using Generative AI. My clients found the most success when this center was co-led by a team from IT, who can handle the technical aspects of AI, and HR, who can oversee learning and development.

The Center of Excellence serves as a hub for AI-related activities within the company. It provides guidance, sets best practices, and ensures that all departments are aligned in their use of AI. This centralized approach ensures that AI is used effectively and ethically throughout the company. Moreover, the Center of Excellence can also serve as a platform for continuous learning and development, keeping the company up-to-date with the latest advancements in AI.

But what makes a Center of Excellence truly successful? There are several guiding principles that underpin its operation, each of which can be applied specifically to the integration of generative AI.

Firstly, the Center of Excellence should have a clear vision and mission. This includes defining the strategic objectives of the center and how it aligns with the overall business strategy. For instance, if a company’s strategy is to leverage generative AI for content creation, the center’s mission could be to develop and implement best practices for using AI in this area.

Secondly, the Center of Excellence should foster collaboration and communication across the organization. It should act as a bridge between different departments, facilitating the sharing of knowledge and best practices. For example, if the marketing team is using generative AI to create content, their insights and experiences could be shared with other departments through the Center of Excellence.

Thirdly, the Center of Excellence should focus on continuous improvement. This involves regularly reviewing and refining its processes and practices to ensure they remain effective and relevant. In the context of generative AI, this could involve staying abreast of the latest AI technologies and updating the company’s practices accordingly.

Lastly, the Center of Excellence should be committed to promoting a culture of learning and development. This includes providing training and resources to employees to enhance their AI skills and knowledge. For example, the center could offer workshops on using generative AI tools, or provide resources for self-learning.

Establishing a Center of Excellence is a critical step in harnessing the power of generative AI. By adhering to these guiding principles, companies can ensure that their center is effective, relevant, and capable of driving AI integration across the organization.

The Ultimate Goal: Serving Business Operations through an AI Center of Excellence

The ultimate goal of this two-pronged approach and the establishment of a Center of Excellence is to serve business operations. Generative AI has the potential to streamline processes, improve efficiency, and drive innovation. By learning from others, understanding internal usage, and establishing a centralized hub for AI, companies can harness this potential and transform their operations.

The Center of Excellence plays a pivotal role in this transformation. It serves as the nerve center of the company’s AI initiatives, guiding the integration of generative AI into business operations. Whether it’s using AI to automate routine tasks, generate creative content, or predict market trends, the Center of Excellence ensures that these initiatives align with the company’s strategic objectives and adhere to best practices.

For instance, if a company wants to use generative AI to streamline its customer service operations, the Center of Excellence could develop a roadmap for this initiative. This could involve identifying the best AI tools for the job, training customer service staff on how to use these tools, and setting up a system for monitoring and improving the AI’s performance.

Moreover, the Center of Excellence also plays a crucial role in fostering a culture of continuous learning and innovation. It keeps the company up-to-date with the latest advancements in AI, encourages employees to explore new ways of using AI, and promotes a culture of experimentation and risk-taking. This culture of innovation is key to harnessing the full potential of generative AI and staying ahead of the competition.

Conclusion

The journey towards effective use of generative AI may seem daunting, but with the right approach, it can lead to unprecedented growth and success. So, take the leap, look outward and inward, establish your Center of Excellence, and watch as AI propels your business into the future. Remember, the future of business is not just about adopting new technologies, but about understanding them, integrating them effectively, and using them to drive operational excellence. The Center of Excellence is your guide on this journey, leading the way towards a future powered by generative AI.

Key Take-Away

Establishing an AI center of excellence centralizes best practices, fosters collaboration, and drives continuous learning, enabling organizations to harness generative AI for operational efficiency and innovation.
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Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. He wrote the first book on returning to the office and leading hybrid teams after the pandemic, his best-seller Returning to the Office and Leading Hybrid and Remote Teams, as well as seven other books. His cutting-edge thought leadership was featured in over 650 articles in prominent venues such as Harvard Business Review,Fortune, and Fast Company. His expertise comes from over 20 years of consulting for Fortune 500 companies from Aflac to Xerox and over 15 years in academia as a behavioral scientist at UNC-Chapel Hill and Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

The post Harnessing the Power of AI Through a Gen AI Center of Excellence appeared first on Disaster Avoidance Experts.

Renewal season exposes every bottleneck in an association, from member support backlogs to a conference program that ships late because staff ran out of hours. In late 2025, many teams are clearing those bottlenecks with a chatbot open in another tab, even when the association never launched AI officially, pointing to extensive shadow AI usage. The latest adoption report from the St. Louis Fed shows generative AI use among U.S. adults ages 18 to 64 at 54.6 percent as of August 2025, up ten points from August 2024, and work use rising to 37.4 percent. Association leaders now need to decide how that private efficiency becomes visible member value, stronger governance, and better fiscal performance.

Video: “Gen AI Productivity Is Finally Hitting Associations”

Podcast: “Gen AI Productivity Is Finally Hitting Associations”

Treat GenAI As Association Infrastructure, Not Personal Productivity

Generative AI use has moved from curiosity to routine. TheSt. Louis Fed tracker built on the Real-Time Population Survey shows adoption continuing to climb through August 2025, which means staff and volunteers are already automating pieces of writing, analysis, and planning. When that happens ahead of policy, associations inherit shadow AI risk in every member email, advocacy draft, and credentialing artifact.

This is not an association-only pattern. Microsoft and LinkedIn report that 75 percent of global knowledge workers use AI at work and 78 percent of AI users bring their own tools, according to the trend index summary, and that reality follows your people into committee work and chapter operations. Program committees use AI to shape session descriptions, government affairs teams tighten testimony, and education staff draft course outlines faster than the approval cycle can keep up.

Treat genAI like infrastructure and govern it like infrastructure for managing risks. A board-endorsed use policy, backed by the NIST risk framework, sets the rules for data handling, attribution, and review, then makes those rules portable across HQ and components. When chapters share a common prompt library, brand standards, and reporting expectations, you gain speed without losing consistency.

Turn Time Savings Into Measured Member Value And Revenue

The time savings are large enough to manage like a real capacity investment. In the St. Louis Fed productivity analysis, workers who used generative AI in the prior week reported average time savings of 5.4 percent of their work hours, and 20.5 percent of those users reported saving four or more hours that week. In association terms, those hours become faster member responses, cleaner board packets, more frequent advocacy alerts, and more usable postevent learning.

Experiments explain where associations get the quickest lift. An NBER call center study found a 14 percent productivity increase for support agents with a conversational assistant, and a preregistered writing experiment found professionals finished writing about 40 percent faster with higher quality output. Those results map to member services, certification communications, sponsorship proposals, and policy briefs, especially when you support newer staff, new chapter leaders, and volunteer chairs with validated prompts and examples. For associations with in-house product teams, a coding experiments on AI coding assistants show measurable developer gains that translate into faster website fixes and smoother event app releases.

Scaling gains requires workflow redesign and measurement, not personal tricks. McKinsey’s economic estimates tie long-run productivity lift to redeploying saved time, and ITIF’s time-savings note warns that reclaimed minutes can disappear into on-the-job leisure if leaders do not assign them. In associations, assignment means choosing member-visible outcomes, setting cycle-time targets, and cutting low value meetings so capacity stays real. Guardrails belong in the same plan: the EEOC worker guidance helps HR teams avoid discrimination risks when AI touches hiring or evaluation, and the WCAG 2.2 accessibility standard keeps AI-generated digital content inclusive.

Case Study: Statewide Credentialing Association Builds A GenAI Operating System

A statewide medical association came to me with flat headcount, rising member inquiries, and an expanding microcredential portfolio. Staff were already using public chatbots to rewrite responses and summarize meetings, while chapters were experimenting with AI-generated newsletters that drifted off brand. The CEO and board wanted productivity gains without risking member data, exam content, or advocacy credibility.

I mapped the real work across HQ and chapters, then built a shared operating system: a policy and training package aligned to the NIST risk framework, a prompt library for member support and education drafts, and a simple review step for any AI-assisted public-facing language. We set up monthly office hours for staff and volunteer leaders to share what worked, retire risky uses, and keep templates aligned with brand standards.

Within eight weeks, internal tracking showed average first response time in member services down 32 percent and after-hours catch-up down about six staff hours per week. Certification staff shortened item-writing cycles enough to launch a microcredential two months earlier than planned, which created new nondues revenue tied to course registrations, and chapters reduced rework because they used the same templates and approval path.

Conclusion

Generative AI is already inside your association because it is already inside your people’s habits. Treat it as infrastructure, redesign workflows around member-visible outcomes, and govern it with clear rules that travel across HQ and chapters. Do that, and the hours your team is already saving turn into renewal-strengthening service, stronger advocacy, better learning products, and sponsor value you can defend.

Key Take-Away

Gen AI productivity is shifting from personal shortcuts to organizational infrastructure, turning time savings into faster services, stronger governance, and measurable member value when guided by clear policies and workflows.
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Image credit: Yan Krukau/pexels

Dr. Gleb Tsipursky, called the “Office Whisperer” by The New York Times, helps tech-forward leaders replace overpriced vendors with staff-built AI solutions. He serves as the CEO of the future-of-work 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 Gen AI Productivity Is Finally Hitting Associations appeared first on Disaster Avoidance Experts.

The integration of Generative AI (Gen AI) into the workplace is a game-changer, offering transformative potential for efficiency and innovation. Yet, with this potential comes uncertainty, as employees may feel unprepared or apprehensive about the implications of this technology on their roles. To address these challenges, forward-thinking organizations are creating internal Gen AI portals—a centralized resource hub that provides employees with the knowledge and tools they need to thrive in this evolving environment. By offering clear, accessible, and interactive learning opportunities, these portals not only demystify Gen AI but also empower employees to see it as an opportunity rather than a threat.

Video: “How to Build a Gen AI Portal Employees Will Actually Use”

Podcast: “How to Build a Gen AI Portal Employees Will Actually Use”

The Importance of a Centralized Gen AI Portal

The introduction of Gen AI into the workplace marks a significant shift in how tasks are performed and decisions are made. While leadership may focus on its potential to drive business outcomes, employees often focus on what it means for their day-to-day work and career prospects. A centralized Gen AI portal addresses these concerns by serving as a trusted source of information, fostering transparency, and guiding employees through the changes.

According to research from McKinsey, organizations that prioritize upskilling during technological shifts like the Gen AI paradigm shift are significantly more likely to achieve their digital transformation goals. A Gen AI portal supports this by providing a space where employees can learn at their own pace and gain the confidence needed to leverage new tools effectively.

Beyond education, a well-designed portal also alleviates fear by addressing misconceptions about Gen AI. Employees often worry that automation and advanced technologies will replace their roles entirely. By offering clear information about the technology’s purpose—augmenting human capabilities rather than replacing them—companies can foster a more optimistic mindset among their workforce.

Core Features of a Gen AI Portal

For an internal Gen AI portal to meet its objectives, it must be thoughtfully designed to cater to the diverse needs of the workforce.

1. Educational Resources
The portal should offer a variety of materials to accommodate different learning styles and levels of expertise. Examples include:

• Introductory Guides: Written explanations of Gen AI concepts, including its benefits and practical applications.
• Video Tutorials: Visual demonstrations that simplify complex topics, such as using specific Gen AI tools.
• Interactive Modules: Quizzes, gamified lessons, and self-paced courses to engage employees actively.

2. FAQ Section
A comprehensive FAQ section is crucial for addressing common concerns and misconceptions, and facilitating risk management. Questions might include:

• How will Gen AI affect my role?
• What skills do I need to stay competitive?
• Are there ethical concerns with using Gen AI at work?

Regular updates to the FAQ ensure it stays relevant and reflects employees’ evolving needs. Research from Deloitte highlights that frequent communication and transparency are essential for building trust.

3. Feedback and Interaction
The portal should be a two-way communication tool, allowing employees to submit questions or suggest topics for future resources. This not only keeps the portal dynamic but also reinforces the organization’s commitment to employee involvement.

4. Accessibility and Security
Ensuring the portal is user-friendly and accessible across devices is critical. Features like single sign-on (SSO) integration and a responsive design make it easy for employees to use the portal from anywhere. At the same time, robust security measures are essential to protect sensitive organizational information.

Case Study: Implementing a Gen AI Portal in a Nonprofit Organization

A nonprofit organization focused on environmental advocacy recently faced challenges when adopting Generative AI (Gen AI) to streamline operations and enhance community engagement. The leadership team believed that Gen AI tools could optimize donor communications, automate administrative tasks, and improve data analysis for impact reporting. However, employees expressed concerns about their roles, fearing that automation might reduce the need for human input, especially in areas like grant writing and outreach. The nonprofit hired me to help them address this challenge.

Through employee surveys and small focus groups, we identified several concerns:

1. A lack of clarity on how Gen AI would be integrated into their daily work.
2. Anxiety about job security, particularly in roles involving repetitive tasks.
3. Limited understanding of how to use Gen AI tools effectively to complement their current skills.

I collaborated with the nonprofit to design an internal Gen AI portal that addressed these concerns while aligning with the organization’s mission and values.

• Learning Resources Tailored to Nonprofits:
  We created specific learning modules, including video tutorials and guides, demonstrating how Gen AI could assist with donor segmentation, crafting personalized outreach emails, and automating volunteer scheduling.
• Interactive FAQ Section:
  Employees were encouraged to submit questions anonymously, which were addressed in a dedicated FAQ section. Topics included, Will Gen AI replace my job? and How can I use Gen AI to enhance grant writing?
• Ethical Guidelines Module:
  Given the nonprofit’s focus on social impact, we included a section on using Gen AI responsibly. This module outlined ethical considerations, such as avoiding biases in automated content and ensuring transparency with stakeholders.
• Feedback Loop for Continuous Improvement:
  A portal feature allowed employees to suggest new resources or flag unclear content, ensuring the platform remained dynamic and responsive to their needs.

To encourage portal usage, we launched it alongside a live webinar that demonstrated its features and addressed employee concerns in real time. A follow-up email campaign highlighted key resources and celebrated early adopters who shared their success stories.

Within six months of the portal’s launch:

• Over 80% of employees reported improved confidence in using Gen AI tools.
• The organization reduced the time spent on administrative tasks by 35%, allowing staff to focus more on mission-driven activities, as well as fundraising.
• Employee feedback indicated a 50% drop in anxiety about Gen AI, as they began to see the technology as a collaborator rather than a replacement.

This initiative not only eased the transition to Gen AI but also strengthened the nonprofit’s ability to deliver on its mission, demonstrating how tailored resources can empower employees in even the most purpose-driven organizations.

The Broader Implications for Workforce Transformation

Creating an internal Gen AI portal isn’t just about addressing immediate concerns—it’s about fostering a culture of innovation and continuous learning. Employees who feel supported and informed are more likely to embrace new tools and even suggest novel applications for them. This engagement can drive both individual and organizational growth.

Furthermore, a Gen AI portal signals to employees that the organization values transparency and development. In a competitive job market, this can be a significant factor in attracting and retaining top talent. A study by LinkedIn Learning found that opportunities for upskilling and reskilling are among the top drivers of employee satisfaction.

Conclusion

The integration of Gen AI into the workplace is inevitable, but the success of this transformation depends on how well employees are supported during the transition. An internal Gen AI portal, with its comprehensive resources and interactive features, is an effective way to empower employees, address their concerns, and foster a sense of collaboration.

As demonstrated in the case study, such a portal can turn apprehension into engagement, helping organizations unlock the full potential of Gen AI. By investing in education and transparency, companies can build a workforce that is not only prepared for the future but excited to shape it.

Key Take-Away

A well-designed internal AI portal empowers employees to confidently adopt Gen AI, easing anxiety, boosting skills, and turning technology into a collaborative tool that drives both personal growth and organizational innovation.
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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 How to Build a Gen AI Portal Employees Will Actually Use appeared first on Disaster Avoidance Experts.

The demos for your association look slick, the promises even slicker. In slides and keynotes, agentic assistants plan, click, and ship your work while you sip coffee. Promoters like McKinsey call it the agentic AI advantage. 

Then you put these systems on real client work and the wheels come off. The newest empirical benchmark from researchers at the Center for AI Safety and Scale AI finds current AI agents completing only a tiny fraction of jobs at a professional standard.

Benchmarks, Not Buzzwords, Should Guide Associations

Headlines say “agents are here.” Data says otherwise. The new Remote Labor Index (RLI), a multi-domain benchmark built from 240 real freelance-type projects across 23 categories, reports an automation rate topping out at 2.5 percent across leading agents, meaning almost all deliverables would be rejected by a reasonable client. The dataset spans design, operations, BI, audio-video, game development, CAD, architecture, and more, reflecting the work that actually 

Other benchmarks grounded in real tasks tell another story. In the WebArena benchmark, a GPT-4-based agent achieved 14.41 percent end-to-end success while humans reached 78.24 percent, a gap that maps directly to member-facing web tasks like registration changes, content uploads, and policy lookups. On real desktops, OSWorld aggregates 369 multi-app computer tasks and reports humans at 72.36 percent versus the best model at 12.24 percent, revealing brittle tool use, file handling, and GUI grounding. Software results echo the pattern: the original SWE-bench found top models solving only a small slice of real GitHub issues, and follow-on evaluations such as SWE-bench Pro still show modest completion on tougher, multi-file changes.

These gaps explain why agents that look fluid in a demo struggle with association deliverables that demand many small, correct steps. A dashboard is not a single chart; it is a documented product built on reliable sources like the World Happiness Report data, with repeatable transforms and clear licensing. A conference paper is not a PDF export; it conforms to strict IEEE author templates. Benchmarks show agents drop files, misname assets, misread interfaces, and stall on long sequences, the exact failure modes that generate support tickets from chapters and sections.

Associations also face budget discipline and reputational stakes. Analysts warn that overreach is common: Gartner projects that more than 40 percent of agent projects will be canceled by 2027 due to cost and unclear value, a trend of agent washing already visible in the market. For associations, that cancellation risk translates into sunk time across staff, volunteer leaders, and committees, plus uneven member experiences across chapters.

Tasks Automate; Projects Still Need Staff And Volunteers

The evidence is consistent: Gen AI is excellent at specific tasks inside projects and unreliable at running projects itself. In a large field deployment, customer-support workers using a generative assistant increased resolutions per hour by about 14 percent, with the largest gains for less-experienced staff. 

That is the right mental model for associations. Gen AI drafts outreach emails that chapter leaders personalize, produces first-pass captions for event reels that a section communications chair approves, suggests agenda outlines for committee meetings that staff adapt, and scaffolds chart code for a member-benefit dashboard. It does not reliably deliver a finished annual meeting microsite, an accredited module, or a standards update without human direction and quality control.

This scope line matters because associations also model responsible practice for members. If the association expects practitioners to follow standards, it should publish its own guidance and embed governance in daily work. Use the NIST AI Risk Management Framework and the AI RMF 1.0 playbook to frame member-facing norms about transparency, data protection, attribution, and human accountability. When vendors pitch autonomous agents, ask how they handle multi-application workflows of the kind that stump OSWorld or visually grounded web tasks flagged by VisualWebArena. If the answers are hand-waving, keep agents fenced to task-level assistance with staff and volunteer oversight.

Build A Responsible Gen AI Program For Chapters And Sections

Associations can harvest gains now without gambling on autonomy. The operating model is simple: staff assigns clear outcomes, AI accelerates repeatable components, volunteer leaders add context, and humans stay responsible for the result. That model scales across chapters and demographic sections when headquarters supplies guardrails and resources tuned to how chapters actually work.

Start with common standards. Publish a short set of internal rules for prompts, sourcing, data retention, and review built on the NIST AI framework. Provide chapter-ready templates: email drafts, caption styles, agenda outlines, and dashboard starters pre-wired to trustworthy sources. Use emerging connection standards such as the Model Context Protocol (MCP) to link models to approved repositories and tools so staff and volunteers work from the same, clean data.

A brief case study shows the approach in practice. I guided a national professional services association with a small headquarters team, active statewide chapters, and several demographic sections through a 12-week pilot. We fenced AI to task-level accelerators aligned to member value: draft social copy for chapter events, first-pass visualizations for a member-needs survey, baseline narration for a 2D training video, and scaffolded chart code for a simple dashboard. We paired content generation with human review anchored to the templates for any regulation, and we evaluated tools against benchmark-exposed risks drawn from WebArena and OSWorld. 

Chapters received a shared prompt library, a permission model grounding agents like interns, and a clear playbook for what counted as ready for members. Sections contributed tone, imagery, and accessibility guidance to ensure materials served different demographics. Headquarters stood up an MCP-based connector to an internal knowledge base using the MCP standard, so staff and volunteer leaders pulled from the same facts.

The results were practical. Turnaround times on repetitive tasks dropped. Chapter communications became more consistent without flattening local voices. Section leaders spent less time on formatting and more on community-specific content. Most importantly, the association published a member-facing Gen AI guideline, grounded in the NIST AI RMF playbook, that set expectations for responsible use in the field. We deliberately avoided autonomous project claims, because the same benchmarks that inspire caution today also chart a path to progress. As agents mature, headquarters can expand the scope safely, measuring acceptance rates and defects the way benchmark authors measure success and failure.

Conclusion

Associations thrive on trust, standards, and service. Benchmarks on the web, desktop, and code show that current agents fall short on long, multi-step deliverables even as Gen AI accelerates well-scoped tasks. Leaders who treat agents as accelerators inside projects, with staff and volunteer leaders accountable for outcomes, will bank tangible gains now and set credible norms for members. Invest in governance, connect agents to clean data, and train chapters and sections to use tools within clear fences. Ignore the hype, publish the standard, and show your field what responsible Gen AI looks like.

Key Take-Away

AI agents aren’t ready to run end-to-end association projects—benchmarks show low reliability—yet they deliver real value when fenced to specific tasks with human oversight, governance, and accountability.
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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 AI Agents Aren’t Replacing Remote Association Workers Anytime Soon appeared first on Disaster Avoidance Experts.