January 16th, 2026 by 
The headline from MIT’s 2025 State of AI in Business research is sobering: despite massive investment in generative AI, the vast majority of organizational pilots are not delivering measurable business value.
As many as 95% of organizations are getting zero return on their AI pilots. Meanwhile, 5% of integrated AI pilots are generating millions in ROI. The gap between outcomes is impossible to ignore, but what does it mean?
But here’s the silver lining for associations: the gap between experimentation and impact, dubbed the “GenAI Divide”, is driven by problems in approach, not a fundamental failing of the technology.
Which means associations and nonprofits, as historically cautious adopters, are perfectly positioned to learn from the missteps of their for-profit peers and craft enterprise-level implementations that are intentional, integrated, and measurably impactful.
How Associations Can Succeed with AI
Most AI pilots fail not because the technology doesn’t work, but because it’s implemented without clear intent or integration. Associations that succeed with AI focus on execution over experimentation by:
- Defining mission-aligned problems before selecting tools
- Embedding AI into existing workflows instead of creating parallel processes
- Prioritizing high-impact, low-visibility initiatives over “shiny” use cases
- Choosing AI systems that retain context and improve through learning
- Partnering strategically to extend internal capacity
- Involving frontline staff early to ensure adoption and relevance
This approach allows associations to move beyond experimentation and achieve measurable, sustainable impact with AI.
What Did For-Profits Get Wrong?
To be clear, we’re not bashing experimentation – it’s the necessary component to every advancement, and failure is an inevitability. And for the organizations that moved extra fast and broke things early, we’re grateful for the lessons they’ve surfaced for the rest of us.
But when you zoom out, clear patterns emerge:
Choosing AI Tools That Didn’t Mesh with Existing Workflows
Integrated or task-specific GenAI tools face a dramatic drop-off from investigation (60% of organizations) to pilot (20%) to production (5%).
The primary issue? These tools exist in isolation, requiring staff to step out of their established workflows. When AI feels like “extra work: instead of embedded support, adoption stalls quickly. These tools may technically function, but they never become essential.
Overlooking the Importance of Context and Learning
The wonderfully useful part about hiring an intern or assistant is that you teach them how to do something once or twice, and they can replicate independently from there on out. Ideally, they can even extrapolate how to solve similar problems based on the context you’ve given them about your organization and desired outcomes.
For AI to be truly impactful, it has to behave similarly.
The lowest-friction, easiest-to-deploy tools often lack this key capability. They don’t retain institutional context. They don’t learn from feedback. And they don’t improve meaningfully over time. As a result, they fall flat far before they ever improve, let alone transform, a business process.
Prioritizing Visibility Over Impact
In the quest for demonstrable value, many organizations gravitated toward highly visible AI use cases: chatbots, personalization. These projects look impressive but ultimately provide little impact. Meanwhile, lower-visibility initiatives like operational support and internal enablement were deprioritized, even though they frequently offer significantly higher ROI.
In too many of these cases, the gain just wasn’t worth the pain: The level of transformation offered by the most oft-piloted tools (chatbots, generative assistants, etc) wasn’t enough to significantly shift processes or even impact business outcomes. Instead of becoming a catalyst, AI became a showcase.
How Can Associations Get Better AI Outcomes?
Many nonprofits operate with smaller teams, less technical depth, and greater scrutiny around spending and change. Yet expectations remain identical to better-resourced, for-profit organizations: deliver superior member service, create engaging programs, demonstrate measurable outcomes, and operate more efficiently.
“So, Noel,” you might be asking. “Where’s the advantage, here?”
Associations and nonprofits are already practiced at being intentional. That discipline, when applied to AI, is a competitive advantage.
1. Start by Defining the Problem AI is Meant to Solve
MIT’s finding supports something we’ve long known to be true. Tools adopted without a clear problem to be solved simply will not solve one. Instead, they will languish, quietly draining budget and eroding your team’s appetite for change.
Rather than falling for the “shiny object trap”, start with the outcome, not the technology. Ask sharper questions:
- What problem consistently frustrates our team or limits our ability to serve members?
- What would change if we solved this – for staff AND for members?
- How will we know it’s working (beyond just time saved)?
When the problem is well defined, the right application of AI is more easily identified and put into place.
2. Prioritize High-Impact AI Projects, Even if They’re Low Visibility
This is the most tempting mistake to repeat, but it’s especially damaging for nonprofits with tight budgets and lean staff.
Instead of leading with attractive public-facing tools like chatbots or event assistants, look for ways to strengthen your real differentiator: your authority.
We consistently see AI initiatives succeed when they support the systems, processes, and expertise that make associations an indispensable resource.
Some recent real-world examples we’ve seen find success:
- LLMs trained on SOPs to support internal operations
- Internal agents embedded in business processes to assist staff
- Internal agents that take the first pass at application eligibility evaluation before staff reviews for final approval
- Member benefit in the form of an LLM trained on trusted organizational content rather than the open web
These projects may not make splashy headlines (although it’s possible), but they’re much more likely to move the needle.
3. Embed AI into Your Association’s Existing Workflows
The most successful implementations follow a workflow-first approach. They embed AI into the systems your team already uses, rather than asking people to adopt entirely new ones.
Look for tools and functionalities that already exist within your current platforms (CRM, AMS, LMS, etc.), configure with process flexibility in mind, and, when choosing new tools, prioritize native integrations.
When AI feels like a natural part of the job, adoption follows more reliably.
4. Choose AI Tools that are Contextual and Capable of Learning
Static AI tools plateau quickly. Contextual systems that learn from usage, feedback, and organizational data continue to improve.
Over time, learning systems reduce manual work, increase trust, and unlock compound returns.
5. Partner Strategically to Extend Internal Capacity
One of the most telling findings in the MIT research: internally built AI failed at nearly twice the rate of externally partnered ones. Strategic partnerships achieved deployment rates of roughly 67% compared to 33% for internal development. Employee adoption was almost double.
The most successful organizations expand their team’s capacity by turning to trusted outside expertise, treating their AI vendors less like software providers and more like business partners – aligned to internal processes, accountable to outcomes, and invested in long-term success.
6. Involve End-Users Early to Steer Direction and Drive Adoption
Another striking insight: while only 40% of companies purchased official LLM subscriptions, employees from over 90% of surveyed organizations reported regular use of personal AI tools for work tasks.
Your team is already using AI in their everyday work, and that can be a huge leg up.
Your team knows which parts of their work are most frustrating, where AI fits best in their workflow, and where its capabilities fall short. They’ve already done a significant part of the discovery for you – lean into that.
Why Associations Should Act on AI Now
The takeaway here isn’t that AI is overhyped. It’s that execution matters – more than enthusiasm, more than tooling, more than trend-chasing.
For associations, this clarity is your advantage. You now know what works: workflow integration, learning-capable systems, strategic partnerships, and mission-aligned problem definition. You know what doesn’t: shiny tools, isolated pilots, generic solutions, and going it alone.
While best practices will continue to evolve, this is the golden window of opportunity, the perfect combination of lessened risk and space for upside.
Organizations that move intentionally now have the space to align AI with real needs, existing workflows, and long-term strategy. Organizations that delay may find themselves forced to adopt AI later, faster, and under more pressure, reacting to board expectations, peer activity, or operational strain. That’s how fragmented tools, rushed implementations, and disappointing outcomes take root.
What It Takes to Move from AI Experimentation to Success
The organizations successfully crossing the GenAI Divide share a common trait: intention. They ask harder questions about problems worth solving. They prioritize integration over innovation theater. They measure progress honestly and adjust based on evidence.
Most importantly, they understand that successful AI for associations boils down to supporting the mission, serving members more effectively, and enabling their teams to do the best work.
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