Why Gen AI Fails in Associations Without Focus—and How to Fix It
Generative AI (Gen AI) holds immense promise for associations, offering new ways to enhance operations, elevate member service, and empower chapters and sections. However, without clear objectives and a strategic framework, Gen AI challenges and risks can become costly experiments with limited impact. Association leaders must approach Gen AI with a clear vision, aligning it with their organization’s mission while fostering an environment where creative exploration thrives. A well-defined roadmap ensures that Gen AI efforts remain focused, meaningful, and impactful, ultimately driving member value and industry leadership.
Step 1: Defining the “Why”—Identifying Core Challenges to Avoid Gen AI Failures
Every Gen AI initiative within an association should begin with a clear articulation of the specific challenges it aims to address. This foundational step ensures that Gen AI is deployed to solve real-world problems rather than being pursued as a technological novelty. For instance, an association struggling with member engagement might prioritize Gen AI projects that personalize communications or streamline event registration processes. Conversely, an association aiming to enhance its industry advocacy efforts might leverage Gen AI to analyze policy trends or generate impactful research reports.
Consider an association facing declining attendance at its annual conference. By identifying this as a core challenge, the association could explore Gen AI solutions that analyze member data to tailor conference programming, optimize marketing efforts, or even create personalized event schedules. This direct connection between a Gen AI initiative and a specific organizational challenge ensures that resources are invested wisely and that the technology serves a clear purpose.
When association leaders pinpoint these strategic challenges, they create a clear “why” for Gen AI exploration. This alignment guides staff and volunteer efforts and ensures that the resources and energy invested yield tangible outcomes that benefit members and advance the association’s mission.
Step 2: Prevent Gen AI Failure by Balancing Structure and Flexibility
While clarity of purpose is essential, rigidly prescribing every detail of a Gen AI project can stifle the very innovation it seeks to foster. Gen AI thrives on iterative experimentation and discovery. Association leaders should focus on defining desired outcomes rather than dictating specific methodologies. This balance empowers staff and volunteers to explore various Gen AI models, tools, and approaches, ultimately discovering the most effective solutions for their unique needs.
For example, an association seeking to improve its member onboarding process might set a goal to increase new member engagement by 25% within the first year. Rather than mandating specific tools or processes, leadership could outline the desired outcome and empower a task force to experiment. Some might explore Gen AI-powered chatbots to answer new member questions, while others might develop personalized welcome sequences based on member profiles. This flexibility could lead to a hybrid solution that surpasses the initial engagement goal while also providing valuable insights into member preferences.
This approach not only enhances creativity but also drives results. Teams feel ownership over their experiments and are motivated to uncover innovative solutions, knowing they have the freedom to adapt as they learn and gather feedback.
Step 3: Accountability and Adaptability—Staying on Course
Clear goals do more than provide direction; they establish benchmarks for accountability throughout a Gen AI project’s lifecycle. Associations can regularly assess progress against these objectives, ensuring efforts remain focused and aligned with strategic priorities. Dashboards tracking key performance indicators (KPIs) specific to associations, such as member retention rates, event attendance, and volunteer engagement, make it easier to evaluate success and make data-driven adjustments as needed.
Take the case of an association implementing a Gen AI-driven system to enhance its chapter support. Initial experiments might show promising results in streamlining communication and resource sharing, but further analysis could reveal opportunities to refine the system for chapter-specific needs. By revisiting their original objectives and incorporating new insights from chapter leaders, the association could expand the project’s scope to include customized resources and training modules, ultimately strengthening the chapter network.
The iterative nature of Gen AI means that projects often uncover unforeseen opportunities. Association leaders must remain agile, ready to refine objectives as new data or insights emerge. This adaptability ensures that projects continue delivering value even as circumstances evolve and member needs shift.
Step 4: Cultivating Alignment and Transparency—Building a Shared Vision
Setting clear goals also fosters transparency and engagement across the association, including staff, volunteers, chapters, and sections. When everyone understands how their work contributes to broader organizational priorities, they are more likely to feel motivated and invested in the project’s success.
Consider an association that launches a Gen AI initiative aimed at improving its industry research capabilities. By clearly communicating how this effort aligns with the association’s mission to provide cutting-edge insights to its members, leadership can inspire collaboration across departments and volunteer committees. Data analysts, subject matter experts, and communications staff can work together to develop a Gen AI model that identifies emerging trends and generates insightful reports, ultimately enhancing the association’s reputation as a thought leader.
By linking individual and team efforts to organizational goals, leaders cultivate a culture of accountability and pride. Staff and volunteers are more likely to see the value of their contributions, fostering a commitment that transcends technical challenges and drives meaningful outcomes.
Step 5: Measuring Success—From Objectives to Member Impact
The ultimate test of any Gen AI initiative lies in its impact on the association and its members. Predefined goals provide a yardstick for evaluating success, enabling leaders to assess ROI and identify lessons for future projects.
For example, an association might set a goal to improve the accuracy of its member needs assessments by 20% using Gen AI. If the initiative surpasses expectations, achieving a 25% improvement and leading to more targeted programming, the association can leverage this success to secure additional funding or expand the use of Gen AI to other areas.
Measuring outcomes also builds a foundation for continuous improvement. Leadership can analyze what worked, what didn’t, and how to refine strategies for future Gen AI endeavors. These insights ensure that each project contributes not just immediate value but also long-term organizational learning and growth.
Client Case Study: Transforming Member Engagement with Gen AI
As a consultant specializing in the future of work for associations, I recently worked with a mid-sized healthcare association of just under 20 staff struggling to personalize its member engagement efforts. With a diverse membership base spanning various career stages and specialties, the association found it challenging to deliver targeted content and services that resonated with each member’s unique needs. The leadership team set a clear goal: increase member engagement, as measured by event attendance, resource utilization, and volunteer participation, by 15% within one year by implementing Gen AI-driven personalization strategies.
Identifying the Pain Points
The association faced several challenges in achieving its engagement goals. Its existing member database, while comprehensive, lacked the dynamic capabilities needed to segment members effectively and deliver personalized experiences. Communications were often generic, failing to address the specific interests and needs of different member groups. Additionally, the association’s website and online resources were not optimized for personalized content delivery, leading to low utilization rates.
The association’s leadership recognized the potential of Gen AI to overcome these challenges. They envisioned a system that could analyze member data, identify patterns and preferences, and automate the delivery of tailored content and opportunities.
Designing and Implementing the Gen AI Solution
To address these challenges, we developed a Gen AI-powered personalization engine tailored to the association’s specific needs. The system was designed to integrate with the existing member database and leverage advanced machine learning algorithms to segment members based on their demographics, engagement history, interests, and career stage.
The implementation process began with a thorough analysis of the association’s member data. By examining historical engagement patterns, survey responses, and website activity, we identified key member segments and their unique needs. These insights guided the development of personalized content strategies and communication workflows.
Next, we integrated the Gen AI engine with the association’s website, email marketing platform, and event management system. This enabled the automated delivery of personalized content, including:
- Targeted event recommendations: The system analyzed member profiles and suggested relevant conferences, workshops, and chapter meetings.
- Curated resource libraries: Members received personalized recommendations for articles, webinars, and other resources based on their interests and career stage.
- Tailored volunteer opportunities: The system matched members with volunteer roles aligned with their skills and passions.
- Personalized section outreach. Members received news and resources based on their demographics and section membership.
To ensure the system’s accuracy and effectiveness, we trained it on a comprehensive dataset of the association’s content and member interactions. We also implemented ongoing monitoring and feedback mechanisms to continuously refine the personalization algorithms.
The Results
The implementation of the Gen AI-powered personalization engine exceeded expectations. Within the one-year target period, member engagement increased by 18%, surpassing the initial goal of 15%. This improvement was reflected across various metrics:
- Event attendance rose by 12%, with a notable increase in participation from previously under-engaged member segments.
- Resource utilization increased by 25%, indicating that members found the personalized recommendations valuable.
- Volunteer participation grew by 10%, demonstrating the effectiveness of matching members with relevant opportunities.
Moreover, the Gen AI system provided valuable insights into member preferences and behavior, enabling the association to make data-driven decisions about programming and service development. The association’s leadership was particularly impressed by the system’s ability to identify emerging areas of interest among members, allowing them to proactively develop new resources and initiatives.
Lessons Learned and Future Directions
Throughout the project, several key lessons emerged. First, the importance of clear, measurable objectives cannot be overstated. The leadership’s decision to set a specific engagement target provided a clear focus for the team and a benchmark for success.
Second, collaboration between staff, volunteers, and technology experts was crucial. By involving representatives from different departments and member groups in the design and implementation process, we ensured that the Gen AI system addressed the association’s diverse needs and priorities.
Finally, ongoing monitoring and refinement proved essential. Post-launch, we implemented regular performance reviews to evaluate the system’s accuracy and effectiveness. These reviews allowed us to make iterative improvements, ensuring the system continued to meet member needs as they evolved.
Inspired by the success of this initiative, the association is now exploring other applications of Gen AI, including predictive analytics for membership renewal forecasting and automated customer support through chatbots.
Conclusion
Setting clear goals and expectations is the cornerstone of any successful Gen AI initiative within an association. It provides direction, fosters innovation, ensures accountability, and aligns efforts with strategic priorities. Leaders who strike the right balance between structure and flexibility create an environment where experimentation flourishes without losing sight of tangible member outcomes. By cultivating alignment and transparency, associations can harness the full potential of Gen AI, transforming it from a buzzword into a driver of sustainable growth, member value, and industry leadership.
As associations navigate the complexities of Gen AI, the lesson is clear: clarity and purpose are not constraints; they are enablers. When goals are well-defined, Gen AI ceases to be a speculative investment and becomes a transformative force, delivering measurable value and driving innovation that benefits members, chapters, sections, and the industry as a whole.
Key Take-Away
Clear objectives prevent costly Gen AI fails. Aligning projects with mission, fostering experimentation, and measuring impact ensures Gen AI drives real member value and sustainable growth. Share on XImage credit: Sebastian Herrmann/unsplash
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.