The Real Gen AI Gap in Associations Starts With Management
Gen AI will not transform associations just because a few staff members or volunteers are curious about it. The bigger question is whether the organization knows how to absorb it into daily work. That is the most important lesson in a new Brookings analysis of Gen AI adoption in Europe and the US. Their finding is striking. U.S. workers are more likely to use Gen AI, they use it more intensively, and the difference is not explained by technology access alone. Management practices and employer encouragement do much of the work.
That should get the attention of association leaders. Associations operate through a layered system of staff, boards, committees, chapters, and sections. They also serve as standard setters for the members who look to them for education, ethics, credentialing, guidance, and signals about what responsible practice looks like. When Gen AI adoption is weak inside the association, the organization usually hesitates outside it too. The result is slower member service, uneven volunteer execution, and less confidence in showing members how Gen AI should be used well.
Management Matters More Than Access
The research does not point to a simple hardware or software gap. The authors find that demographics, occupations, industries, and firm size explain part of the U.S. and Europe divide, but only part. A large share of the gap remains. What closes it is management. The World Management Survey has long shown that organizations vary sharply in how they reward performance, promote talent, and address underperformance. That older insight became famous in the digital era through “Americans Do IT Better”, which argued that organizations gained more from information technology when they reorganized around it instead of merely buying it.
The Gen AI evidence follows the same pattern. In the new paper, management quality is closely linked to adoption, and firm encouragement is the strongest predictor of whether workers actually use Gen AI. Official UK analysis on management practices and AI adoption found a similarly steep gradient, with firms in the top management decile far more likely to adopt AI than those in the bottom decile.
For associations, that means the real bottleneck is often not budget. It is leadership ambiguity. Staff wonder whether Gen AI is welcome or risky. Chapter leaders do not know what is approved. Section volunteers improvise their own rules. Legal or policy teams slow decisions because nobody has drawn a clear line between productive use and unacceptable use. In that environment, adoption stays shallow. A few enthusiastic people carry the load, while the organization as a whole learns almost nothing.
Member Service, Chapters, and Sections
Associations feel uneven Gen AI adoption faster than many other organizations because their work is both centralized and distributed. A headquarters team may handle membership, events, education, certification, communications, and standards development, but large parts of the member experience are delivered through chapters and sections led by volunteers. When Gen AI use is inconsistent, members see the inconsistency immediately. One chapter produces polished outreach and fast follow up. Another still depends on manual emails and outdated templates. One section can summarize a regulatory update overnight. Another waits weeks to brief its members.
That matters because the operational upside is real. The Brookings paper links higher adoption with faster productivity growth at the industry level, while finding no clear evidence of broad employment contraction so far. That aligns with the Draghi report on European competitiveness, which argues that growth now depends on how well organizations diffuse new technology through real workflows. It also matches the micro evidence. In customer support, Erik Brynjolfsson, Danielle Li, and Lindsey Raymond found that Gen AI assistance lifted productivity, especially for less experienced workers. In professional writing, Shakked Noy and Whitney Zhang found that workers completed tasks faster and with better quality.
Inside an association, those gains show up in familiar places. Member service teams can draft clearer first responses. Education teams can turn webinars into summaries, study guides, and course updates. Standards and policy teams can scan large document sets faster, while still reserving judgment for humans. Chapters can produce better agendas, sponsorship outreach, and post event follow up. Sections serving early career members, women, international members, or specialty practice groups can create more tailored content without adding full time staff. The point is not to automate the association’s voice. It is to give that voice more reach and consistency.
What Changes When Leadership Takes Ownership
In one recent consulting engagement, I worked with a midsize national association with about fifty staff, a volunteer board, more than two dozen chapters, and several sections organized around career stage and specialty. Leadership had already purchased Gen AI licenses. That was not the problem. The problem was that adoption had no operating model behind it.
A few staff members were using Gen AI to draft member emails and summarize long reports. Some chapter leaders wanted help writing newsletters and meeting scripts. Several section leaders were curious about using it to build discussion guides and resource lists. But nobody was sure what the rules were. Staff worried about privacy. Volunteers worried about quality. The association wanted to advise members on Gen AI, yet it had not built internal confidence first.
So the work began with management, not prompts. We defined approved use cases for member service, event planning, chapter communications, education, and standards support. We set simple guardrails for confidentiality, human review, attribution, and record retention. We trained managers to ask a better question each week: where can Gen AI save time without lowering trust? Then we equipped chapter and section leaders with reusable workflows instead of one off demos.
The shift was immediate. Staff used Gen AI to draft member replies, repurpose conference content, prepare board briefs, and summarize policy developments. Chapter leaders used shared prompts to build agendas, volunteer outreach, and sponsor follow up. Section leaders used it to create more tailored programming for distinct member groups. What changed most was not the software. It was the signal from leadership that Gen AI was now part of how the association worked.
That pattern is consistent with earlier research. In their study of police departments, Luis Garicano and Paul Heaton found that technology created value when it was paired with organizational change, not when it was simply installed. And in “Diagnosing the Italian Disease”, Bruno Pellegrino and Luigi Zingales showed how weak meritocracy and poor management can block technology diffusion even when tools are available.
Conclusion
Associations should stop treating Gen AI as a side experiment and start treating it as a management test. The organizations that pull ahead will not be the ones with the most excited board presentations or the longest policy documents. They will be the ones that give staff and volunteers clear permission, useful guardrails, and practical workflows that fit the real work of serving members. If an association wants to lead its field on Gen AI, it has to do more than talk about standards. It has to model them first.
Key Take-Away
The Gen AI gap is not about access to technology but about leadership. Associations that provide clear guidance, guardrails, and workflows will drive adoption, improve member service, and achieve greater impact. Share on XImage credit: DC Studio/magnific
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.
