I spend most of my time talking to organizations that are somewhere along their ServiceNow journey.
Some are just getting started, trying to make the right early calls and avoid stepping into anything they’ll have to clean up later. Others are a few years in, with a platform that’s grown into something the business depends on every day, whether they planned it that way or not. Different industries, different priorities, but after a while, the conversations start to sound familiar.
It usually begins with what the platform can do. And for a while, that’s exactly where it should stay. There’s a lot to figure out early on, and most teams have a pretty good handle on what they’re trying to accomplish.
But then, at some point, it shifts. A few months after go-live, sometimes a little longer, the questions start to change. They’re not about features or capabilities anymore. They’re about how the organization is using it, and what comes next now that it’s part of how the business runs day to day.
I’ve been in enough of these conversations to know how they usually go.
What I’ve come to appreciate is that when these questions show up, it doesn’t mean something’s gone wrong. If anything, it’s WORKING. The platform has become important. People are relying on it. Teams are building around it. Leadership is starting to look at it as something that drives outcomes, not just something that supports them.
That’s a great place to be. It just means you’ve moved past implementation and into ownership and even transformation, and that’s where things tend to get more interesting.
Over time, I’ve noticed that most of these conversations tend to circle a few core questions. They show up in different ways depending on the organization, but the underlying themes are consistent.
The first one is almost always about scale. Early on, there’s a defined scope. A specific workflow, a business unit, a clear use case that everyone can rally around. The team builds something that works, and naturally, other parts of the organization start asking if they can use it too. That’s usually when things begin to stretch a bit. New teams come in with different needs and wants, integrations start to stack up, and the platform becomes more central than anyone originally planned for.
What worked well in one area doesn’t always translate cleanly to another, and you start to feel the edges of the original design. Underneath all of that is a simple question: How do we grow this without breaking what we’ve already built?
That’s also about the time when AI starts to enter the conversation in a more practical way. Not as a headline or something that lives on a roadmap slide, but as something people want to use in their day-to-day work. There’s interest in automation, in assistive experiences, in using AI to route work, surface insights, or take some of the friction out of how things get done.
What I’ve seen, though, is that AI has a way of amplifying whatever foundation is already there.
If the data is clean and the processes are consistent, things tend to improve quickly. If they’re not, the gaps get a lot harder to ignore. Scaling the platform and scaling automation start to become the same conversation, whether people realize it at first or not.
Not long after that, the governance question starts to come into focus. At some point, someone asks who really owns the platform. Not just in a formal sense, but in a practical one. Who decides how things get built, how changes are approved, and how different teams are expected to work within the same system?
Most organizations don’t start with a fully defined answer to that. Governance tends to grow organically. A few key people take on responsibility, standards begin to form over time, and decisions get made as situations come up. That works for a while, especially when the scope is still relatively contained.
But as more teams get involved, you start to see some drift. Similar workflows are built in slightly different ways. Data is defined differently depending on the group. Good people making reasonable decisions, just not always in alignment with each other.
That’s usually when leadership starts asking for more structure, not because anything has failed, but because the platform has become too important to leave loosely defined.
AI raises the stakes here in a quiet but meaningful way. Once you begin layering in automation, recommendations, or agent-driven actions, consistency starts to matter a lot more. Small differences in how things are defined or executed can lead to very different outcomes at scale. Trust becomes part of the conversation, not just trust in the platform itself, but trust in how decisions are being made inside it.
Governance, at that point, stops being about control and starts being about clarity and confidence. It’s a different kind of conversation than most teams expect to have at the beginning.
Then there’s the question that tends to come from a slightly different angle, and usually from a different part of the organization. At some point, someone looks at the platform and asks whether they’re getting everything they can out of it.
It’s a fair question, and it’s usually where things become more honest. Because most organizations, if they take a step back, realize there’s more in the platform than they’re actively using. Capabilities that were implemented but never fully adopted. Workflows that exist but haven’t been refined. Data that’s there but not really driving decisions in a meaningful way.
None of that is unusual. It’s just how these programs tend to evolve. Where AI enters the picture, it is here as a kind of multiplier. There’s a lot of interest in using it to unlock insights, streamline work, and improve experiences, and it absolutely can do that. But it tends to work best when it’s building on something that’s already being used consistently.
If the underlying workflows are unclear or adoption is uneven, AI doesn’t solve that on its own. It just makes the gaps easier to see. On the other hand, when the foundation is solid, it can surface value that’s been sitting there the whole time, sometimes in ways that surprise people.
If there’s one thing that ties these questions together, it’s adoption. Those questions don’t always get asked as directly, but they’re always there in the background. How do we get the organization to adopt the new processes? How do we get the team to use the technology the way it was intended?
Because at some point, this stops being about the platform itself and starts being about behavior. How people work, what they trust, and what they go back to when things get busy.
You can have the right workflows, the right data, and a clear strategy, but if people aren’t using it consistently, it becomes very difficult to scale, to govern effectively, or to extract real value from what you’ve built.
AI has a way of making that more visible. It relies on patterns. It learns from behavior. When adoption is strong, it tends to reinforce good outcomes. When it’s not, the results can be uneven, and that becomes hard to ignore.
Which brings things back, again, to people. Not just training, but alignment. Not just enablement, but a shared understanding of why the change matters in the first place.
What’s interesting is that once organizations go through this cycle, they tend to approach things a little differently the next time around.
They think earlier about how the platform will expand and the outcomes they need to procure, they put more structure around governance before it becomes urgent, and they pay closer attention to adoption from the beginning instead of trying to correct for it later.
And when it comes to AI, they tend to see it for what it is. A powerful layer that sits on top of everything else and depends on all of it working together.
If there’s one pattern I’ve seen hold up over time, it’s that the technology matters, the strategy matters, but the organizations that get the most out of this are the ones that treat it as part of how they operate, not just something they implemented. That’s when the real value starts to show up.