By Sarah Nicastro, Creator and Editor in Chief, Future of Field Service

There’s no shortage of urgency around AI right now.

It’s featured (heavily) at every conference, and plenty of leaders have shared with me that every boardroom discussion seems to circle back to the same question: “What are we doing with AI?” In fact, I’ve heard more than one time tales of being directed along the lines of, “You have $X to invest, go do AI.”

While we all can (hopefully) agree that not only is that approach foolish, it’s also just not how AI works – the pressure is nonetheless real. Where does this leave service leaders? Caught between top-level pressure to invest and bottom-up resistance to change trying to figure out exactly how and where AI does fit.

On last week’s podcast, Jayda Nance, AI Product Owner at IBM, and I had an interesting and enlightening conversation around this topic. We started with a hard truth that many seem to want to avoid. As Jayda put it simply, “AI isn’t a magic wand.”

Expecting it to be is where, in many instances, things start to go wrong. AI is important, absolutely – but it’s not a magic want, and it’s not a strategy; it’s a tool. Keeping that perspective is very important, and Jayda shared with us what she’s learned in her role that’s proven helpful in determining where AI’s true value in service lies.

The Danger of “Temporary Energy”

To start, Jayda described something I see playing out across the service landscape: organizations chasing AI because of that pressure they feel. She described this as “temporary energy” and explained its danger.

Temporary energy, as she describes it, is allowing yourself to be driven by that pressure to keep up. To be seen as innovative. To not fall behind.

But the risk of moving this way is that speed begins to replace direction. When that happens, organizations invest time, budget, and resources into initiatives that may look promising on the surface but ultimately fail to address any meaningful business problem.

In other words, you end up with solutions in search of a problem. The more sustainable path, as Jayda emphasizes, is far less glamorous: slow down long enough to deeply understand what problems you’re actually trying to solve.

Problem Determination Is a Discipline (Not a Step)

We often talk about “identifying the problem” as if it’s a box to check before moving on to the real work. But what Jayda describes is something much more rigorous. Rather than a step in the process, problem determination is a discipline.

She encourages what she calls a “reporter mindset”—immersing yourself in the problem the way a journalist would, observing it from every angle, understanding the context, and resisting the urge to jump to conclusions.

Because the reality is, most organizations won’t misapply AI because they lack capability; they’ll misapply it because they haven’t fully understood the problem. Without that clarity, even the most sophisticated technology will fall short of delivering value.

Sometimes the Answer Isn’t AI at All

This is where the conversation gets particularly interesting—and, for those under pressure to apply AI, perhaps a bit uncomfortable.

Because when you truly break down a problem, you may find that the solution has nothing to do with AI. It might be a broken process. Poor data quality. Unclear ownership. Overloaded teams.

Jayda shared an example of slow service renewals as something that might easily be flagged as an opportunity for AI. But when you peel back the layers, the root cause could just as easily be inefficiencies in workflow or bottlenecks in approvals. And if that’s the case, layering AI on top doesn’t solve the problem; it complicates it. Organizations end up over-engineering solutions instead of addressing fundamentals.

Another area where Jayda brings important clarity is in distinguishing between automation and AI. These terms are often used interchangeably (even by yours truly!), but she explains that they shouldn’t be.

She describes that automation is about consistency and scale. It excels at executing repeatable, rule-based tasks. AI, on the other hand, is about learning, adapting, and making decisions in more complex or variable environments. There can be instances where they are used together, but sometimes the terms are used synonymously without clarity.

The goal should be to ensure that within your organization you are clear on how each are defined and what solution, or set of solutions, best fits each problem you’ve defined. While AI’s capabilities are exciting, applying them to scenarios that don’t demand that level of intelligence adds complexity and cost without adding value.

When you match the tool to the problem—rather than the other way around—you avoid unnecessary complexity and accelerate time to value.

A Simple Framework That Brings Clarity

One of the most practical insights Jayda shared is a structured approach to process mapping. She breaks down workflows into four layers:

• Inputs
• Rules
• Actions
• Outputs

While simple, the discipline of actually mapping these layers forces a level of clarity that many organizations skip. This framework helps ensure you consider:

• Where is the breakdown happening?
• Is it in the data coming in?
• The logic being applied?
• The actions being taken?
• Or the outcomes being delivered?

Without this level of understanding, decisions about automation or AI are left to guesswork or gut feel. With it, they become intentional.

Why Pilots Matter, Arguably More Than Your Big Plans

When organizations succumb to the pressure to keep pace, there’s a tendency to feel you need to think (and act) big. This can result in designing large-scale transformations or launching expansive MVPs that take months to develop.

While there’s nothing wrong with having a vision in mind, Jayda advocates for taking action in a way that is much more focused: pilots. Not as a stepping stone, but as a strategy. A pilot isn’t about building something impressive; it’s about proving something valuable. It’s taking a small, defined use case—say, a handful of service requests within a single team—and testing whether an approach actually works.

This does two critical things:

1. It creates tangible evidence to justify further investment.
2. It builds trust—both with leadership and with the frontline teams who will ultimately need to adopt the change.

And that second point is often overlooked. Because in service organizations especially, transformation doesn’t succeed on strategy alone, it requires belief and buy-in.

The Cultural Side of AI Adoption

Which brings us to what may be the most underestimated aspect of achieving success with AI: mindset.

Jayda is very clear on this—technical capability is not the primary barrier to AI adoption. Mindset is.

The hesitation, the skepticism, the fear of job displacement—these are real and valid concerns within service organizations. But they can also become self-imposed limitations if not addressed.

While we’ve talked about those who are rushing due to the pressure, the flip side of that is feeling frozen. Whether due to fear, overwhelm, or other circumstances, this is equally risky.

What Jayda shares, and what I’ve observed as well, is that those who approach AI with curiosity are the ones who unlock its potential. Curiosity brings with it a power of neutrality that insulates the business from the pressure that causes rushed investments or the hesitancy that keeps organizations stuck.

The Bigger Question

Stepping back, what this conversation really underscores is that AI success isn’t about how quickly you adopt the technology. It’s about how intentionally you apply it.

If you want to assess how intentional you’re being, reflect honestly on the following:

• Are you chasing momentum, or building it?
• Are you solving real problems, or reacting to external pressure?
• Are you designing for value, or hoping to discover it along the way?

Because the organizations that get this right won’t be the ones that moved fastest. They’ll be the ones that thought most clearly about what they were trying to achieve and built from there.