A recent ASAE Collaborate discussion about whether “AI will take our jobs” surfaced legitimate concerns. That uncertainty deserves honest conversation, not dismissal.

AI will reshape how associations operate. Some roles will change significantly, and some tasks will disappear. But associations are better positioned than many sectors to navigate this thoughtfully, due to their smaller size (larger organizations are more likely to expect AI-related workforce reductions, according to McKinsey’s 2025 State of AI Survey). They also benefit from the cross-functional perspective of staff, which isn’t easily automated, at least not yet.

What gives me hope is that someone still needs to validate outputs, catch errors, and ensure the work supports our mission and reflects our values. PwC’s 2025 Global AI Jobs Barometer, which analyzed close to a billion job postings globally, found that AI can make people more valuable, not less, even in the most automatable roles. Human oversight is an ethical responsibility. We owe it to our members, our teams, and our missions to keep humans meaningfully involved in outputs and decisions that affect people’s lives and livelihoods.

The professionals who thrive will be those who can see how systems connect, question AI outputs with healthy skepticism, and guide work toward the mission. That requires clean data, transparent processes, and strong leadership. AI becomes a real tool for improvement if you get these foundations right. If you skip them, you’ll automate problems you didn’t know you had.

The Operational Reality

Most associations run lean. One person might manage membership data, coordinate events, and pitch in on marketing. Many of us also operate with fragmented systems where platforms don’t easily share information, making it difficult to understand members, trust data, or measure what matters. AI will amplify whatever foundation you give it. If that foundation is fragmented, your problems get bigger, not smaller.

Why Cross-Functional Knowledge Matters

The breadth of perspective that association staff bring is exactly what AI implementation requires. Understanding how systems, data, and processes interact helps you guide technology to advance your goals. But AI cannot replace the judgment, empathy, and ethical reasoning that association work demands. When AI influences decisions affecting real people, someone must be responsible. That someone cannot be an algorithm.

Associations can’t approach AI the way large corporations might. We don’t have the redundancy to spare. Our people are the experts, and their knowledge ensures that AI serves our mission. Research points to roles transforming rather than disappearing, and the skills your team has already built are the basis for that evolution. AI doesn’t diminish that expertise. It magnifies it.

Anthropic’s March 2026 labor market research found that actual AI adoption is still a fraction of what is theoretically possible. For association staff, that future shift looks different than it does in large enterprises. Most association professionals carry institutional knowledge that does not transfer easily to an algorithm. ASAE’s 2026 Insight Update found that more than three-quarters of associations maintained or increased staffing levels over the past year. The window to prepare thoughtfully is still open.

Building Your Foundation

Before applying AI effectively, strengthen your operational foundations. Standardize identifiers across systems, document where data originates and how it flows, define clear ownership for data accuracy, and create AI policies that reflect your values. Break down silos through cross-departmental collaboration and align budgets and hiring with the skills you’ll need. For finance and operations leaders, this is familiar territory. The same rigor driving accurate reporting and compliance applies here.

Who Owns This Work?

Data governance requires commitment across the organization. It’s a leadership priority, not a technology project. You need data stewards in every department, data owners with authority over data collection and use, someone at the center maintaining consistent standards, and real executive investment in budget, time, and attention.

Be honest about capacity. If your team is stretched thin, adding governance responsibilities won’t work. Leaders must prioritize this, not just assign it. Start with data critical to your first AI pilots and build from there.

The Bottom Line

Most organizations are still figuring this out. McKinsey’s 2025 Superagency in the Workplace report found that only 25% of C-suite leaders across industries have a comprehensive AI roadmap in place.

You are not behind. But by focusing on readiness, collaboration, and ethical responsibility, we can build a future where technology strengthens what’s best about our organizations: our people, our purpose, and our ability to serve. Success won’t be measured by how fast you adopt AI. It will be measured by whether it genuinely improves member value, furthers your mission, and whether we’ve done right by our teams.