AI Program Management
AI Business Case: Getting Board Approval
The board does not approve an AI project because the model is elegant. It approves it when someone shows it a business case that ties the investment to a measurable return, expressed in the language it uses to judge every other corporate expense: pipeline, margin, cost avoided. The technical part interests whoever builds the system; whoever signs the budget cares what changes in the P&L and with what margin of error. In this article I explain how I build an AI business case that holds up in front of decision-makers.
Speak the board's language, not engineering's
The first mistake I see is presenting an AI project by describing the technology: the type of model, the architecture, its capabilities. These are the things that thrill whoever builds them and leave whoever must fund them indifferent. An effective business case runs the opposite way: it starts from the number the project will move (a higher conversion, a cost that disappears, a shorter sales cycle) and uses the technology only as the means to get there.
I have built business cases in these terms even outside the strictly AI perimeter. The partner-led go-to-market for Optimizely that I led in its first six months generated €1M in pipeline, 5 opportunities and 15 proposals, and that result came from a business case that promised a specific number, not a promising technology.
The one-line rule
I have a simple test for whether a business case is ready: if I cannot write in one line what will change in the P&L or in the customer experience, and with what margin of error, the business case is not ready for the board. "We cut video localisation costs by 75% while keeping editorial control" is a line a board understands and can judge. "We implement a multimodal AI pipeline" is not.
Tie the investment to credible KPIs
A business case with no verification metrics is a promise no one can ever disprove, and precisely for that reason the board trusts it little. Every figure I put in a business case is tied to a KPI that will be measured in production, so that in six months you can honestly say whether the promise was kept. How I instrument these KPIs and tie them to the business case is what I describe in the article on how to measure an AI project in production.
The credibility of a business case, in a board that has seen others fail, depends more on the honesty of the estimates than on their ambition. A modest but defensible return convinces more than a spectacular one no one really believes is achievable.
Anticipate the objections before you present them
A business case is presented to a table that will ask "and if it does not work?". Preparing for that question in advance means stating the risks explicitly: what happens if the data is not ready, which part of the return is certain and which is a bet, and what the cost of stopping after the pilot is if the numbers do not come. A business case that hides its risks looks stronger in the room and collapses at the first setback; one that puts them on the table is harder to attack.
This matters especially for AI-in-marketing projects, where the temptation to promise vague results is stronger. How I bring AI into real marketing operations, with the concrete cases and numbers behind it, is in the guide to AI for marketing. The method I use to build the whole program, from roadmap to delivery, is instead in the guide to AI program management.
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