By Ashish Malik, Co-Founder and CEO, 108 ideaspace inc. | 01-10-2026
In short
The best AI strategy starts with organizational problems, not tools. At 108 ideaspace, we look for AI opportunities through five lenses: time, friction, insight, value and capacity. For associations and nonprofits, the investigation sometimes shows the answer is a process fix rather than AI, and that is a good result.
The pressure to do something with AI is real. But fear of missing out is not a strategy. The organizations that create the most value from AI will not necessarily be the ones that adopt the most tools. They will be the ones that get better at identifying the right problems to solve.
AI has created an unusual leadership challenge for associations and nonprofits. Executives are hearing about it from almost every direction. Boards are asking what the organization is doing with AI. Staff are experimenting with ChatGPT, Copilot and other tools, whether or not anyone has told them they can. Microsoft and LinkedIn’s 2024 Work Trend Index found that 75 percent of knowledge workers were already using AI at work. Vendors are adding AI capabilities to platforms you already own, and in some cases to the renewal quote. Conferences, webinars, and LinkedIn feeds are filled with examples of organizations using AI to work faster, personalize experiences, and automate tasks.
Somewhere in that noise is a very human emotion: fear of missing out. Nobody wants to discover three years from now that their organization stood still while everyone else transformed around them. That is a legitimate concern. But it can also cause organizations to start their AI journey in exactly the wrong place.
FOMO is a poor AI strategy
The questions often arrive in a predictable order. Where could we use ChatGPT? Should we implement Copilot? Do we need an AI chatbot? Should we build an AI agent? What are other associations doing with AI?
These are not bad questions. They are simply premature. Because the moment we ask “where can we use AI?”, we have already made an important assumption: that AI is the answer. We just have not decided what problem it is supposed to solve. And that is backwards.
A useful AI strategy should not begin with a technology looking for somewhere to be deployed. It should begin with a meaningful organizational problem and then determine the best way to solve it. Sometimes that answer will be AI. Sometimes it will not.
The pressure to be seen doing something
FOMO does not necessarily lead to reckless decisions. More often, it creates subtle pressure to show progress. Launch a pilot. Buy some licenses. Create an AI working group. Add a chatbot. Build an agent. Give everyone access to a tool.
None of those actions is inherently wrong, and experimentation is an important part of developing organizational capability. The problem starts when an organization confuses motion with progress. An organization can have multiple AI pilots underway and still be no closer to solving its most important problems: the renewal process members complain about every year, the report that takes a week to assemble, or the fact that nobody can answer a simple question about engagement without exporting data from three systems.
An organization can also automate tasks without questioning whether those tasks should exist in the first place. As we noted in AI Readiness Isn’t About AI, automation tends to make poor processes permanent. And it can invest in AI while the data needed to make that AI useful remains fragmented, inconsistent or poorly governed.
That distinction matters because AI is not an organizational outcome. Better service is an outcome. So are greater capacity, better decisions, improved member experiences, reduced administrative burden and greater mission impact. Technology is a means to an end. That is why our AI readiness work looks at six foundations together (strategy, data, process, governance, people and risk) rather than at the technology alone.
Replace the fear of missing out with the discipline of finding out
Instead of asking your organization to brainstorm all the places it could use AI, use the urgency surrounding AI to investigate how your organization actually works.
Find out where the friction is: where members, customers or stakeholders struggle unnecessarily. Find out where the time goes, and which activities consume disproportionate amounts of staff capacity. Find out where insight is missing, and where the organization holds information but struggles to turn it into useful knowledge. Find out where value is being lost, and where you are failing to deliver the experience or outcome you want. And find out what your people could be doing instead if routine and administrative work required less effort.
This is a very different starting point, and it changes the conversation from “what can this technology do?” to a much more useful question: “what does our organization need to do better?”
How to find the right AI opportunities: five lenses
When we work with organizations on modernization, the first job is to separate the problem from the assumed solution. For AI, we do that with what we call the Five Lenses for AI Opportunities: time, friction, insight, value and capacity.
1. Time: where does staff effort go?
Where are people spending significant amounts of time on repetitive, administrative or information-heavy work? Think about activities such as reviewing documents, finding information, summarizing material, preparing routine communications or moving information between systems by hand.
But do not assume these activities should be automated. Ask first why the work takes so much time. The answer may be a process problem, sometimes an integration problem, and occasionally a decision nobody has revisited in a decade.
2. Friction: what is harder than it should be?
Where is accomplishing something harder than it should be? Look at the organization from both sides. Where do staff encounter unnecessary steps, approvals or workarounds, or maintain spreadsheets because a system does not do what they need? Where do members repeatedly need assistance with the same tasks, or leave a digital process and contact staff because they cannot complete it themselves?
Friction is often one of the clearest indicators of a modernization opportunity. But again, AI is not automatically the solution. A confusing process does not necessarily need an intelligent assistant. Sometimes it just needs to be less confusing.
3. Insight: where is information not becoming knowledge?
Most organizations are not suffering from a data shortage. They are struggling to make sense of what they already have. Membership information may sit in one system, event participation in another, learning activity somewhere else, and website behaviour on another platform, while staff knowledge lives in documents, email, and people’s heads.
AI has enormous potential to help organizations understand information. But before asking AI for an answer, leadership needs to ask whether it trusts the information it will use to create that answer. AI does not eliminate the need for data governance. It makes data governance more important.
4. Value: what could we do that we could not do before?
I often tell association leaders that members do not care about you; they only care about how you can solve their problem. Where could your organization create considerably more value for the people it serves? This is where the AI conversation becomes more strategic, because the biggest opportunity may not be reducing the time required to perform an existing task. It may be doing something your organization previously could not realistically do at all.
Could you make a large body of knowledge genuinely easier to access? Could you identify patterns that help you understand member needs earlier, before the renewal notice goes out? Could you help someone navigate a complicated eligibility question at nine in the evening without a staff member available? Could you deliver a more relevant experience without requiring staff to manually personalize every interaction? The most valuable AI use cases may not simply make existing work faster. They may make new forms of value possible.
5. Capacity: what could our people do instead?
This may be the most important lens for mission-driven organizations. Every organization has finite human capacity, so the question should not simply be how many hours AI can save. Ask instead what our people could do with those hours.
If AI saves 500 hours of administrative effort but those hours simply disappear into an already overloaded organization, the strategic value is difficult to see. If that capacity lets staff spend more time supporting members, developing programs, building relationships, improving services or advancing the mission, then the value proposition changes considerably. The objective is not necessarily to replace human work. It is to decide where human capability creates the most value.
What if the answer is not AI?
Sometimes the most useful conclusion an AI strategy can reach does not feel like a success at the time.
You identify an expensive, frustrating process. You investigate it. And you discover you do not need AI; you need to eliminate three approval steps. Or integrate two platforms you already pay for. Or establish a reliable source of truth for member status. Or redesign a form. Or automate a workflow using technology you already own. Or stop producing a report nobody uses.
That is not a failed AI opportunity. That is good strategy. The goal was never to maximize the number of AI use cases your organization can identify. The goal is to improve organizational performance and mission impact, and AI earns its place when it is the right tool for accomplishing that.
Before the pilot, define the outcome
Once you have identified a problem where AI appears promising, resist the temptation to jump directly into implementation. Define what success looks like, in numbers where you can. If AI is going to reduce administrative burden, by how much? If AI will improve service, how will you know? If AI will help employees find information, what should become faster or more accurate, and measured against what baseline? If AI will improve the member experience, what should members experience differently? And if AI will create capacity, where will that capacity be reinvested?
This changes the business case from “we are implementing an AI assistant” to “we are trying to reduce the time our team spends finding and synthesizing information by 30 percent, so they can spend more time providing direct support.” Now the technology can be evaluated against an organizational outcome, which is a much stronger basis for deciding whether an experiment deserves to become an investment.
Do not confuse moving quickly with rushing
There is a legitimate argument for moving quickly with AI. Organizations need to experiment. Employees need opportunities to develop AI literacy. Leadership needs firsthand understanding of what these technologies can and cannot do. And waiting for everything to become certain is not a viable strategy either.
But there is a meaningful difference between moving quickly and rushing. Moving quickly means rapidly learning about your organization, testing hypotheses, measuring outcomes and adjusting. Rushing means buying tools because everyone else appears to be buying them. One builds organizational capability. The other builds a collection of technology. And in a world where we are sold shiny objects all the time, the second is the default unless someone deliberately chooses the first.
You are probably not as far behind as you think
One of the forces driving AI FOMO is the perception that everyone else has already figured this out. In our conversations with association leaders, most have not.
There is a significant difference between using AI, experimenting with AI, and having the organizational capability to use AI strategically. That capability requires more than licenses and prompts. It rests on the same six foundations: strategy, data, process, governance, people and risk.
That is why AI readiness is not really about AI, and it is why your AI strategy should not begin with an AI tool.
Start with the problem
So, the next time someone asks “where can we use AI?”, try changing the question. Where are we spending too much time? Where are people experiencing friction? Where are we struggling to generate insight? Where could we create considerably more value? And where could greater capacity help us advance our mission?
Then investigate. Understand the problem. Challenge the process. Define the outcome. Assess the data. Consider the risks. And only then ask what technology belongs in the solution.
Because the organizations that succeed with AI may not be the organizations that adopt it fastest or use it most. They will be the organizations that become exceptionally good at knowing where it matters.
Finding AI opportunities: frequently asked questions
How do you find the right AI opportunities in an association?
Start with organizational problems, not tools. Look at the organization through five lenses (time, friction, insight, value and capacity) and let the technology decision follow the problem. Asking “where can we use AI?” assumes the answer before the question is defined.
What are the five lenses for finding AI opportunities?
Time: where staff effort goes on repetitive or administrative work. Friction: where something is harder than it should be for staff or members. Insight: where information is not turning into knowledge. Value: what the organization could do that it could not do before. Capacity: what people could do with the hours AI frees up.
What if an AI investigation concludes the answer is not AI?
That is a good result. Removing approval steps, integrating platforms you already pay for or retiring a report nobody uses are legitimate outcomes. The goal is better organizational performance and mission impact, not more AI use cases.
How do you measure whether an AI pilot worked?
Define the outcome before choosing the tool: a number, a baseline, and where the saved capacity will go. “Reduce the time our team spends finding information by 30 percent” can be evaluated. “Implement an AI assistant” cannot.
Is my organization behind on AI?
Probably not as far as you think. Using AI and having the organizational capability to use it strategically are different things. That capability rests on the six foundations of AI readiness: strategy, data, process, governance, people and risk.
Where are your best AI opportunities?
In a 30-minute conversation, we will walk through the five lenses with your team and identify the problems worth solving first, whether or not the answer turns out to be AI.
CTA: Book a 30-minute AI opportunity conversation →
Related: AI Readiness Isn’t About AI
About the author
Ashish Malik is Co-Founder and CEO of 108 ideaspace inc., a Toronto-based technology consultancy for associations, nonprofits, regulators and mission-driven organizations. He has worked with organizations in the sector for more than 15 years.
