Not sure where AI fits? How we help you find the first use case

Many teams want to use AI but don’t know where it will actually help. That’s normal. The goal is not to use AI everywhere — it’s to identify one workflow where it creates a clear, measurable lift.

Step 1: Map the workflow

We start by mapping the current process: inputs, tools, handoffs, and time spent. This quickly reveals bottlenecks and repetitive steps.

Step 2: Define the outcome and owner

We agree on a single outcome (time saved, faster response, fewer errors) and assign a business owner. If no one owns the outcome, the pilot won’t stick.

Step 3: Check data readiness

AI needs consistent inputs. We verify what data exists, where it lives, and what quality it has. If data is messy, we scope a cleanup step first.

A quick readiness checklist:

• Do we have enough historical examples?
• Are key fields consistent across sources?
• Can we safely access the data we need?

Step 4: Decide AI vs. rules

Some tasks are better handled with deterministic automation. We look for the split between what can be rules-based and what needs AI, so the workflow stays reliable and explainable.

Step 5: Score the ROI

We estimate impact based on time saved, error reduction, and business risk. If the ROI is unclear, we don’t build it.

Step 6: Define a pilot

We convert the best use case into a 4–6 week pilot with clear success criteria. The pilot proves value before you scale.

Step 7: Plan adoption and scale

We align on training, handoffs, and quality monitoring so the pilot becomes a repeatable workflow. This is where AI stops being a demo and starts being a real system.

If you’re not sure where to start, an AI Opportunity Audit is the fastest path to a confident first step.