# Examples

Each example starts with a business problem, then shows what we'd do and what changes. Examples are illustrative.

## Twenty AI pitches, one plan.

Strategy · Mid-size manufacturer

- Before: Leadership was fielding AI pitches from every vendor, with no way to compare them and no one owning the decision.
- What we'd do: Map the opportunities against their processes and data, rank them by value and effort, and set up an operating model: owners, decision rights, and measures.
- After: Three projects underway with clear owners, two set to fix first, and the rest set aside with the reasons written down.

## An agent team for the back office.

Build · Field service company

- Before: Every new job took a chain of calls, lookups, and follow-ups before a crew could go out.
- What we'd build: Agents that take requests, prepare jobs from service history, schedule crews, and follow up with customers. The office handles the exceptions.
- After: More jobs move through with the same office team, and nothing waits on a callback.

## Find out why the assistant gets it wrong.

Evaluate and improve · Customer support team

- Before: Their AI assistant answered most questions well and some badly, and no one could tell which.
- What we'd do: Build a test set from real tickets, compare fixes on it side by side, and keep the one that measures best.
- After: Fewer wrong answers reach customers, and the team knows how good the assistant really is.

## Someone finally owns AI.

AI leadership · Growing services firm

- Before: AI questions kept landing on the COO's desk, and the board wanted a plan.
- What we'd do: A part-time AI lead sets priorities, reviews vendors before contracts are signed, and reports to the board.
- After: Clear owners, fewer impulse purchases, and a plan the board can follow.

## Everyone using AI, inside the rules.

Training · Professional services firm

- Before: Staff used AI tools unevenly, and partners worried about client data.
- What we'd do: Role-based workshops on the firm's own documents, with clear rules for what goes into which tool.
- After: Consistent everyday use, inside rules the partners agreed to.

## Turn expertise into a new product.

Strategy, then Build · Training company

- Before: Revenue stopped at the edge of the instructors' calendars.
- What we'd build: A practice tool that lets customers rehearse between sessions, with feedback grounded in the company's method.
- After: A new product to sell that doesn't need an instructor in the room.

## Quote the unusual orders too.

Build · Specialist supplier

- Before: Custom requests waited on one expert to pull specs, stock, and pricing together. Some never got a quote.
- What we'd build: Agents that draft each quote from those sources and the company's pricing rules. The expert reviews and sends.
- After: More requests get an answer, and the expert spends the day on the calls that need judgment.