Across the association sector, AI is no longer a novelty. Most leaders have moved past the “Should we try this?” phase. Tools have been tested. Pilots have been launched. Demos have been watched. Task forces have met. And yet, when I talk with CEOs, executive directors, and boards, a familiar frustration keeps surfacing.
“We’ve experimented, but we’re not seeing real impact.”
That gap between activity and outcome is where many associations are currently stuck. The issue is not ambition, curiosity, or even investment. It is something more structural, and more human.
The associations seeing real results from AI are not the ones with the most tools. They are the ones that changed how they think about value, work, and leadership.
Here is what separates them.
Associations stuck in pilot mode almost always frame AI as a technology initiative. A new platform. A productivity booster. A staff efficiency play. Something the IT team or innovation committee owns. Associations seeing impact frame AI as an organizational capability. That distinction matters. When AI is treated as a tool, success is measured by usage. When it is treated as a capability, success is measured by outcomes. Better decisions. Faster insight. More responsive member experiences. Reduced friction across core workflows. The question shifts from “Who is using it?” to “What is meaningfully different because of it?” Until leadership makes that shift explicit, pilots tend to remain isolated and impact remains shallow.
Many pilots fail because they start with the tool rather than the tension. “What could we do with AI?” is an interesting question, but it is rarely a useful starting point. Associations making progress start elsewhere. They begin with persistent pain points leadership already cares about. Things like:
AI is then introduced as a way to reduce friction in those specific areas. The result is focus. Fewer pilots. Clearer expectations. Faster learning. And far less fatigue among staff who are already stretched thin.
AI anxiety is real in associations. It shows up quietly, but it is there. Staff worry about relevance. Boards worry about risk. Leaders worry about reputation. Nobody wants to be the cautionary tale. The associations moving forward do not try to talk people out of that fear. They surface it. Name it. Normalize it. Then they put boundaries around it.
Clear guidance on what AI can and cannot be used for. Clear accountability for outputs. Clear ownership of decisions that still belong to humans. This creates psychological safety, which turns out to be a prerequisite for experimentation that actually sticks. Without it, people test tools in private and avoid applying them in public or strategic ways.
This one surprises many leaders. The associations seeing the strongest returns from AI are not necessarily spending more on platforms. They are spending more time on clarity.
Clarity about:
AI amplifies whatever already exists. If workflows are unclear, data is messy, or strategy is fuzzy, AI accelerates confusion rather than solving it.
The most effective leaders slowed down before they sped up. They mapped decision flows. Cleaned inputs. Simplified processes. Then layered AI on top.
That sequence makes all the difference.
In associations still stuck in pilot mode, governance is often viewed as the thing that will slow AI down. More policies. More approvals. More risk conversations.
In associations seeing impact, governance played a different role. Boards and executives focused less on policing tools and more on articulating principles. What does responsible use look like here? What values guide decisions when the technology creates ambiguity? Where does accountability live? That kind of governance does not stifle innovation. It gives it room to breathe. When staff know the guardrails, they move faster inside them.
All of this considered, if we’re being honest, the real shift is not technological. What I am seeing most clearly in 2026 is this: the associations getting results from AI are not ahead because they adopted faster. They are ahead because they led differently. They treated AI as a leadership issue, not a novelty. They connected it to mission, strategy, and trust. They resisted the urge to chase everything and instead focused on what mattered most. The rest will catch up. The tools are getting easier. Access is expanding. Knowledge gaps are shrinking.
But impact will continue to belong to the organizations willing to do the harder work first: clarifying priorities, aligning people, and making intentional choices about how intelligence actually serves purpose.
AI is not the differentiator.
Leadership is.
Avi S. Olitzky is the president and principal consultant of Olitzky Consulting Group, based in Minneapolis, Minnesota. He can be reached at avi@olitzkyconsulting.com.