AI Needs a Business Case, Not Just a Pilot

Demonstration article for client preview. This sample illustrates how Mark’s insights can appear in AGP Musings.

Artificial intelligence has made experimentation easier than ever. A team can build a convincing prototype in days. The harder question is whether that prototype changes a business outcome that matters: faster service, better decisions, lower operating costs, or stronger customer retention.

Start with the work, not the tool

A useful AI strategy begins with a specific workflow. Where do people repeatedly search for information, reconcile documents, or translate complex inputs into routine decisions? These activities often provide a clearer starting point than a broad ambition to become an AI-first enterprise.

Consider an operations team preparing customer proposals. AI might assemble a first draft from approved material, flag missing information, and summarize relevant history. The objective is not to remove judgment. It is to give the team more time to apply it.

Measure the outcome before scaling

Before launching a pilot, establish a baseline. Track turnaround time, rework, quality, and the effort required to review the output. A faster draft is not a business improvement if checking it takes longer than writing it.

Scale only when the evidence is convincing. Reliable data access, clear ownership, and a practical review process matter as much as model capability. The strongest deployments treat these foundations as part of the product, not as work to do later.

Build a repeatable capability

Lasting advantage is unlikely to come from a single clever prompt. It comes from learning how to identify valuable use cases, test them responsibly, and embed what works into everyday operations. AI creates opportunity; disciplined execution turns that opportunity into growth.