In this economy, the competitor might be that your prospective buyer does nothing at all.
The organization across the street is a small threat compared to inertia — the donor who closes the tab, the stakeholder who decides to wait another quarter, the community member who gives up trying to navigate your services page at midnight and quietly stops trying.
That’s the competition. Indifference. The path of least resistance. And the problem I see most right now: most AI adoption is being driven by fear of the wrong opponent.
Fear is setting the budget
I’m having a lot of conversations with clients and prospects where, in the best case, revenue is flat. Even though Wall Street appears to be booming, folks in their own households don’t have the disposable income they had a year ago. Overall spending looks steady, but it’s shifting toward essentials — electric bills, transportation, food. That sentiment walks into the office with people every morning, and it shows up in operational budgets. Belts are tightening. Projects get canceled. Teams get smaller.
At the same time, there’s a second pressure pushing from the opposite direction. Leaders feel obligated to use AI, even if they don’t quite know how it fits into their workflows yet. The board asks about it. Peers post about it. Vendors pitch it. So organizations position AI as the answer to both problems at once: the cost squeeze and the competitive anxiety.
Then the two forces collide.
That desire to use AI to save money runs straight into a hard realization: replacing an existing workflow with an AI workflow often means a significant upfront investment, followed by a different kind of investment in quality control of those AI outputs. The savings arrive later, if they arrive at all, and only if someone in your organization knows how to judge whether the output is any good.
Fear-based adoption skips that math. It buys the tool first and asks the design questions after. I’ve watched the result across enough organizations to call it a pattern: a pile of subscriptions, a confused team, and outputs nobody trusts.
You’re solving the wrong problem
Fear-based adoption gets something wrong at the foundation. It assumes the game is keeping pace with other organizations. The actual game is earning motion from people who have every reason to stand still.
You have to prove why your product deserves to be in your customers’ hands. You have to prove why your service is superior or unique when compared to what your peers are doing. That proof lives in the experience you deliver — how fast someone finds the help they need, how clearly you explain what you do, how it feels to interact with your organization when money is tight and patience is thin.
An AI tool bought out of anxiety does nothing for any of that. It adds weight without adding strength.
Design-based adoption looks completely different. It starts with the friction your audience actually hits, then asks whether AI removes it. In our own practice, we use AI to sweep across team notes and generate project reporting that used to require billable junior-analyst hours. Clients get more effort on the actual problem and less on documenting it. That worked because we designed the workflow first and brought the tool in second, with a sharpened knowledge base underneath it so the output stays grounded instead of guessing.
The tool didn’t create the discipline. The discipline made the tool useful.
The question underneath the question
The biggest differentiator, in everything I’ve observed, is how you’re treating your people. They will determine how your organization treats your customers or your stakeholders. A burned-out team leaning on unchecked AI outputs delivers exactly the experience you’d expect. A supported team using AI to clear busywork gets to spend its creativity on the humans you serve.
Which brings us to a harder question than “should we be using AI?”
Do you have the right plan? Every single one of my clients right now is realizing that what they say they do now is probably not what they’re going to be doing in five years. The economy is shifting. The technology landscape is shifting. The kind of organization you’re running today may look very different in ten years, and the plans that pretend otherwise are the ones that crack first.
That requires organizational humility. It means saying, out loud, that what we are now is probably not what we need to become, and building a team around that uncertainty instead of around a fixed plan. It’s uncomfortable, and it’s also the position of strength. Because once you accept it, the follow-up becomes practical: do you have the right folks around you to go on that journey together.
When you have that, you’re in a position to use AI and a variety of other tools to operate efficiently, to run little experiments that create big results, and to keep overhead and technical debt from piling up in ways that stop you from moving when your community needs you to.
Where we go next
AI maturity is a property of your operating model — whether your knowledge is documented, your people are engaged, your workflows are designed on purpose, and your leadership is honest about what needs to become different.
Fear of competitors will push you toward reactive purchases. Respect for inertia — for the buyer who does nothing, the community member who gives up — pushes you toward better experiences. One of those paths compounds.
So here’s your assignment, and it starts before any board meeting or vendor call.
Have the first honest conversation with yourself. Write down what your organization actually does today, what your audience actually experiences when they reach you, and what you’d need to become to still matter in five years. Then look at every AI initiative on your list and keep only the ones that serve that answer.
If you want a grounded place to start, get a Website Reality Check. For $27, you’ll see what your audience actually experiences when they arrive — which is the clearest signal you’ll ever get about which problem deserves your investment first.
