Most organizations arrive at the same moment: they’ve seen the demos, experimented with the tools, and know that AI matters for their business. What they haven’t found is a clear answer to the simplest question — where do we actually start?
A Getting Started engagement answers that question. Here’s what it looks like in practice.
Step One: The Audit
Before anything gets built, we spend time inside the operation. That means sitting with the people who do the work — not just the executives who assigned the project — and mapping what they actually do day to day.
We’re looking for a few specific things:
Volume. An AI solution that runs five times a month isn’t worth building. We’re looking for workflows that happen constantly — dozens or hundreds of times a week — where even a modest efficiency gain compounds into something meaningful.
Rule-based inputs with variable formats. The sweet spot for AI automation is a process where the rules are consistent but the inputs vary: a customer inquiry that always needs to be categorized and routed, but arrives as an email, a voicemail transcription, or a web form. When both the rules and the inputs are predictable, regular code is faster and cheaper. When the process requires human judgment and domain expertise, it should stay human.
Bottlenecks the team already knows about. Usually, the people closest to the work can name the thing that slows them down the most. That’s often the best place to start.
Step Two: Picking the First Use Case
The audit typically surfaces several candidates. We pick one — the highest-value, most clearly scoped option — and focus there.
The first use case is not meant to transform the business. It’s meant to prove the concept in a real environment, with real data, in a way that the team can actually see working. A good first use case produces a visible result quickly and builds the organizational confidence to go further.
Step Three: Building the Prototype
We build in your environment, not ours. That means using your actual data, your actual inputs, and your actual workflow — not a sanitized demo dataset. The prototype runs in a test environment inside your infrastructure, so the path from prototype to production is as short as possible.
By the end of the engagement, you have something running. Not a slide deck recommending AI. Not a proof-of-concept on synthetic data. A working prototype on a real use case from your operation.
What Clients Typically Discover
A few things come up consistently in Getting Started engagements:
The first use case is rarely what the client expected going in. The audit usually reveals that the highest-value opportunity is somewhere in the middle of the organization — an operational workflow, not the executive-level use case that prompted the engagement.
The second thing: AI is often not the right tool for part of what we find. Some workflows are better handled by conventional automation. Knowing the difference early saves time and money, and it builds trust.
The third: the first working prototype changes how the organization thinks about what’s possible. That’s the real deliverable.
Getting Started engagements are typically Small (half a day to two days) or Medium (three to ten days) depending on the complexity of the use case and how far you want to take the prototype. Get in touch to talk through what the right scope looks like for your organization.