Demystifying AI To Help Leaders Focus On What Matters Most

Nick Damoulakis is President of Orases.

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​I have sat across from hundreds of executives to figure out their AI strategy, and most begin with the wrong question. They want to know which tool to buy, while I want to know what's actually broken in their business that they think AI will fix. Often, that connection hasn't been made, but the conversation needs to start there.

Therein lies the gap I see most often with companies trying to demystify AI for their leadership teams. Everyone wants to explain the technology. Far fewer want to examine whether the organization is ready to absorb it. Before the approval of any budget, I ask leadership to write the business problem in one sentence. If they can't, there is your actual starting point, long before generating a vendor list.

Fear In The Room Rarely About Technology

I used to assume the hesitation executives brought into these conversations was about the technology itself. I found it’s more often about looking back in six months and realizing real money was spent with little to show for it. I've watched capable leadership teams end up in that position, and almost every time, the root cause was the same: they never agreed among themselves what success looks like.

You can sense uncertainty in a room before anyone names it. Concern about falling behind, wasted spend, a competitor moving first. None of that is unreasonable, but the energy is usually misdirected. I encourage leaders to redirect it by getting the leadership team in a room and agreeing, in writing, on the specific outcome you're chasing before anyone talks to a vendor.

AI Adoption Like Change Management, Not A Technology Rollout

If there is one thing I wish every CEO understood before signing a contract, it's that they are not buying software. They are asking people to work differently, and people resist that in the same way they resist most meaningful change.

I now walk leadership teams through Kurt Lewin's change model in nearly every workshop to give them a practical way to think about what's coming. The model breaks down change into three stages: unfreezing old assumptions, making the change and refreezing the new way of working so it sticks. Most companies skip the first stage, introducing a new tool while the organization still operates under its old assumptions about decision rights and required skills. Then they're caught off guard when adoption stalls.

My advice is to spend real time on unfreezing first: talk openly about what's changing and why, address skill gaps directly and expect a temporary dip in morale before things improve. I've watched that dip in morale happen, frustration and discomfort with letting go of old habits, before teams move into acceptance. Leaders who don't anticipate it often mistake it for a failed rollout. It's the cost of changing how people work, and it passes faster when leadership addresses it early.

Bolting AI On Vs. Building Around It

I can usually tell within the first ten minutes of a meeting which path a company is headed for. Some want to keep their org chart, workflows and decision-making exactly as they are and add a tool somewhere in the middle. That feels safer, but it rarely produces leadership's ideal results.

The companies that see real returns ask a harder question: if we were building this function today, with these capabilities available from the start, would it look the way it currently does?

Most of the time, the honest answer is no. I therefore recommend treating that question as a required step before any tool gets selected. It takes more time upfront, but it's often the difference between a tool that gets embraced and one that gets ignored.

Picking The Tool Without Urgency

When I ask executive teams what specific problem they're solving, the answer is often some version of not wanting to fall behind. While real, it isn't a strategy and shouldn't drive a purchase decision.

I've seen executives arrive at a meeting having already selected a vendor before even articulating a problem it solves. That sequence rarely ends well, and it's a meaningful part of why so many technology purchases get revisited within a year. The smarter approach is to define the outcome first, in specific terms, and let that shape which tool makes sense.

Leadership Alignment Before Purchase

One simple exercise I rely on in workshops is to ask leadership teams, separately, what success looks like for future initiatives. It's rare to get matching answers on the first attempt.

That tells me everything about whether a company is ready to move forward. The technology is rarely the hardest part. Getting the leadership team aligned on what they're trying to accomplish, before any money moves, usually is. My recommendation is to treat that alignment exercise as a formal step in the process and not work on assumptions.

The Takeaway

After running this exercise more times than I can count, the pattern holds. The companies that struggle with AI aren't the ones with smaller budgets or older systems. They're the ones that skipped the leadership conversation and moved straight to the purchase order. Demystifying AI isn't about explaining how the technology works. It's about being honest that the hardest part has never been technical, and aligning before the first dollar is spent.

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