How AI Workslop Drains Associations and Undermines Member Value
Artificial intelligence promised to streamline association operations and elevate member services, yet a troubling challenge now surfaces across professional communities. Researchers at BetterUp and Stanford’s Social Media Lab coined the term “workslop” to describe AI-generated output that appears polished but lacks substance or accuracy. For associations committed to delivering credibility, trust, and member value, this phenomenon represents more than a nuisance. It erodes confidence among members, staff, and volunteers while driving up hidden costs.
The research estimates that workers waste nearly two hours correcting or redoing each instance of workslop they receive, creating a financial burden approaching $9 million annually for larger organizations. For associations operating on tightly managed budgets, these productivity losses threaten mission delivery. More critically, workslop damages the trust that associations depend upon to foster community, advance professional standards, and maintain influence with policymakers and industry leaders.
Eroding Trust and Wasting Capacity
Workslop does not merely drain productivity. It chips away at the trust associations rely upon to engage members and fulfill their missions. Researchers found that over half of workers receiving workslop lose confidence in their colleagues’ capabilities. In the association context, this translates to volunteer leaders questioning staff reports, chapter officers doubting headquarters guidance, and members losing faith in certification materials or advocacy briefings.
The interpersonal damage exceeds the financial waste. When 54 percent of professionals view AI-using colleagues as less creative, 42 percent as less trustworthy, and 37 percent as less intelligent, the ripple effects on collaboration become severe. For associations, this eroded trust threatens membership renewal, sponsor confidence, continuing education credibility, and overall brand integrity.
The problem often stems not from laziness but from pressure. Staff and volunteers view AI as a mysterious “black box” they feel compelled to use without proper training or strategic alignment. The automation anxiety proves real. Surveys show that 89 percent of workers express concern about AI’s impact on job security, with 65 percent anxious that AI might replace their specific role. For association professionals already managing lean operations, this manifests as performative adoption: using AI tools simply to demonstrate they keep pace with technology, regardless of output quality.
One project manager described the predicament: “Receiving this poor quality work created a huge time waste and inconvenience for me. Since it was provided by my supervisor, I felt uncomfortable confronting her about its poor quality and requesting she redo it.” In associations, this dynamic plays out when certification staff hesitate to challenge volunteer subject matter experts, or when chapter leaders defer to national office despite receiving unusable AI-generated materials. The sender saves minutes while the receiver inherits hours of rework. Artificial intelligence makes this productivity-draining behavior scalable, enabling rapid creation of content that actively harms organizational effectiveness.
Shifting to Agency and Co-Creation
Forward-thinking associations take a different path. They treat AI as a platform for co-creation rather than a shortcut for volume. When members, staff, and volunteers gain empowerment to build their own AI tools through accessible no-code and low-code platforms, the entire culture shifts from passive consumption to active problem-solving.
Consider certification program teams. Rather than using generic AI to draft exam items that often fail standards, staff can design a custom assistant trained on past validated questions aligned with the association’s job analysis. The process of building such a tool requires them to define quality metrics, clarify success measures, and iteratively refine outputs through testing cycles. The AI transforms from a mysterious content factory into a transparent collaborator.
Similarly, advocacy teams can develop policy brief assistants trained on the association’s legislative archives, position statements, and regulatory comment history. Chapter operations leaders can create event planning tools that reflect the association’s brand standards and member preferences. Member services staff can build personalized learning recommendation engines based on actual professional development patterns within the community.
This approach strengthens multiple dimensions of association operations. Low-code platforms enable staff to reduce time wasted fixing poor drafts. They give chapter leaders tailored tools aligned with headquarters standards. They ensure members receive reliable, high-quality resources reflecting the association’s expertise rather than generic AI output. Most importantly, they foster professional growth among staff and volunteers who gain confidence, discernment, and ownership through the building process.
When an association professional builds an AI tool, they must think critically about the entire workflow. They define what success looks like, structure the necessary inputs, document decision logic, and test outputs against real scenarios. This hands-on experience demystifies the technology and instills deep ownership over quality. The process teaches AI’s inherent limitations, which paradoxically increases comfort and produces more discerning critical thinkers when evaluating any AI-generated content.
Client Case Study: Transforming Policy Development Through Co-Creation
One national healthcare association faced mounting frustration when AI-generated first drafts of policy briefs landed on committee tables. Volunteer physician leaders spent hours correcting inaccurate clinical references and irrelevant regulatory examples, which weakened trust between staff and member volunteers. Committee meetings devolved into line-editing sessions rather than strategic discussions. Recognizing the long-term risk to volunteer engagement and policy influence, the CEO engaged me to help address the problem.
I led a structured readiness assessment examining cultural dynamics, workflow bottlenecks, and technical infrastructure gaps. My team identified that staff felt pressure to demonstrate AI usage without clear quality standards or member input. Volunteers felt excluded from technology decisions affecting their expertise contributions. The assessment revealed that the association lacked governance frameworks for AI tool selection and deployment.
I then facilitated co-creation workshops where policy staff and member volunteers jointly built an “Advocacy Assistant.” Rather than pulling from generic sources, we trained the tool on the association’s legislative archives, position statements, fact sheets, and successful regulatory comments spanning fifteen years. Staff and volunteers co-defined quality standards covering clinical accuracy, regulatory context, and persuasive tone. They tested drafts together using actual pending legislation, creating a shared evaluation rubric.
The transformation proved substantial. Within six months, committee meeting time spent revising staff drafts dropped by 47 percent. Volunteers reported greater confidence in the association’s outputs and deeper appreciation for staff expertise. Staff freed capacity to focus on relationship-building with legislators and regulatory agency officials. The board noted improved transparency, since members could understand how AI tools operated and shaped policy positions.
Most significantly, renewal rates among advocacy-engaged members rose by 16 percent over the following year, demonstrating tangible value from the member-driven AI approach. The association also documented secondary benefits: faster response times to regulatory comment periods, more consistent messaging across advocacy materials, and increased volunteer satisfaction scores on annual surveys.
This case illustrates that the antidote to workslop lies not in adopting faster AI models but in deliberate co-creation. Associations investing in capacity-building, governance standards, and shared ownership see both efficiency gains and stronger member trust.
The Path Forward for Association Leaders
Eradicating workslop requires deliberate strategic shifts from association leadership. This begins by prioritizing strategy over tools, conducting readiness assessments examining cultural dynamics and workflow patterns before deploying platforms. Leaders should cultivate agency rather than dependency by framing AI as building blocks and investing in training programs that empower non-technical staff, volunteers, and member committees to create solutions.
Associations must establish clear quality standards through facilitated conversations about what constitutes high-quality, AI-assisted work. Creating communities of practice where chapters share successful implementations and lessons learned accelerates adoption while maintaining standards. Measuring outcomes rather than activity becomes crucial. Incentives should reward substantive results like tools solving real member problems, improving volunteer engagement, or enhancing advocacy effectiveness, not vanity metrics like prompt counts or tool adoption rates.
AI workslop signals shallow, ultimately ineffective implementation that threatens association credibility and member value. The path forward lies not in finding better AI models but in adopting thoughtful leadership approaches that empower people, foster co-creation cultures, and build genuine human-centric AI capability. This represents the only sustainable path to turning technological potential into defensible competitive advantage while preserving the trust and community that define successful professional associations.
Key Take-Away
AI workslop drains association trust and productivity, but co-creation and staff empowerment transform AI from shallow output into reliable, member-focused tools that strengthen engagement and value. Share on XImage credit: Nataliya Vaitkevich/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.
