AI Adoption and Associations: From Experimentation to Enterprise Strategy

AI adoption and associations: From Experimentation to Enterprise Strategy

According to an article by Stephen Rosenlund of ASAE, a recent World Café hosted by ASAE’s International Associations Advisory Council found that successful organizations are mainstreaming AI adoption across operations while balancing innovation with risk management. The overview: enthusiasm is high, adoption is real, but execution gaps remain, particularly around skills, governance, and member-facing strategy.

Rosenlund cites survey data from the Center for International Private Enterprise showing that roughly half of associations in developing and emerging markets have already used AI, while 16 percent are unsure—indicating limited visibility into how AI is already used in enterprise tools. Marketing, communications, and data analysis dominate current use cases, with generative AI far outpacing other technologies. Leaders primarily value operational efficiency, especially in resource-constrained environments, but these gains are framed as a means to advance mission impact rather than an end in themselves.

The article highlights a disconnect between optimism and action. Nearly all association leaders view AI as important over the next three to five years, yet more than half do not currently help members navigate AI in their own organizations. Rosenlund notes this as a missed opportunity for education, partnerships, and new revenue models. At the same time, lack of internal expertise is identified as the top adoption barrier, while most associations still lack formal AI risk policies.

Case examples illustrate what mature AI adoption looks like in associations. Ginger Ausloos of AACSB describes embedding AI into strategic planning, governance, and cross-functional workflows, signaling an organization-wide shift rather than a pilot mindset. Travis Willard of IMA explains how AI is reshaping professional education toward competency-based, modular learning aligned with workforce needs.

AI adoption and associations require deliberate investment in people, policy, and strategy. Efficiency gains matter, but long-term value comes from governance readiness, staff upskilling, and member-facing leadership in an AI-shaped landscape.