Business & Strategy

Value Creation: Cost, Revenue, Moat

AI creates value in exactly three ways. It lowers cost by removing labor from a repeatable process. It raises revenue by increasing capacity, speed, or conversion — answering every lead instantly, quoting in minutes instead of days. Or it builds a moat, where your proprietary data and workflows produce something competitors can't copy because they don't have your data. Cost savings are the easiest to prove and the right place to start; moats are where durable advantage lives.

Build vs. Buy, and Vendor Evaluation

Buy anything that isn't differentiating — email drafting, transcription, document extraction are solved commodities. Build only where your specific process or data is the advantage. When evaluating vendors, look past the demo: ask what model sits underneath and what happens when it changes, where your data is stored and whether it trains their models, what the real cost looks like at your volume, and how you'd export your data and leave.

• What model powers this, and what happens when it changes?
• Where does our data live, and is it used for training?
• What is total cost at 10x current volume?
• How do we export everything and exit?

Measuring ROI

Capture the baseline before you deploy anything — hours spent, error rate, response time, cost per transaction. Without that number, every later claim is an argument. Then measure the same things after, and include the full cost: licenses, inference, integration, training, and the ongoing human review time the workflow now requires. A project that saves ten hours a week but requires four hours of review saves six, not ten. Say so honestly; credible numbers survive scrutiny and get you the next project.

Org Design and Adoption

Adoption fails for human reasons far more often than technical ones. People quietly stop using tools they don't trust, weren't trained on, or suspect are designed to replace them. What works: pick one team and one painful workflow, involve the people who do that work in designing the solution, be explicit that the goal is removing drudgery, train properly, and let that team's results sell the next rollout internally.