AI Business Cases

There’s enormous pressure to “do something with AI,” and that pressure produces a lot of projects that sound exciting and deliver very little. A good business case cuts through the hype by asking unglamorous but decisive questions: what problem are we solving, is it worth solving this way, and how will we know if it worked? You don’t need to be a strategist to contribute here. If you can keep the conversation anchored to real value and real cost, you’ll help your team back the AI ideas that pay off and quietly drop the ones that don’t.

Start with the problem, not the technology

The most common mistake is starting from the tool — “let’s add an AI chatbot” — instead of the problem. Flip it around. Name a specific, painful, measurable problem first: support agents spend hours each day answering the same ten questions; contracts take a week to review; staff can’t find the right policy document. Then ask whether AI is genuinely the best fix, or whether a simpler change — a better FAQ page, a fixed process — would solve it more cheaply. AI is one option among several, and sometimes the boring option wins.

Weigh value against the full cost

If AI does look like the right tool, weigh the value against the full cost — and people routinely underestimate the second. The value side is the benefit: hours saved, faster turnaround, fewer errors, happier customers, ideally something you can put a rough number on. The cost side is more than the software licence. It includes building and integrating the tool, the ongoing cost of running it, the time people spend reviewing AI output, preparing and maintaining data, and the risk if it gets something wrong in front of a customer. A project that saves ten minutes but needs fifteen minutes of checking isn’t a win.

Beware the demo that dazzles. The real question isn’t “can AI do this once impressively?” but “does it create more value than it costs, reliably, every day, at scale?”

Account for risk and effort honestly

Some AI projects carry risk that quietly outweighs the benefit: anything touching legal advice, financial decisions, hiring, or sensitive personal data needs heavy human review, and that review is a real ongoing cost. Factor in data readiness too — if the documents or records the project depends on are messy or scattered, the cleanup may dwarf the AI work itself. An honest business case names these costs out loud instead of hoping they disappear.

Start small, measure, then scale

The smart pattern is a small pilot with a clear success measure agreed before you start: “cut average ticket handling time by a quarter,” or “answer 80% of policy questions correctly with sources.” Run it on a limited scope, measure honestly against that target, and only scale if the numbers hold up. Be willing to stop a pilot that doesn’t deliver — a cheap “no” early is far better than an expensive disappointment after a full rollout. Measuring also turns vague enthusiasm into evidence you can take to a decision-maker.

Spot it: strong case or shaky case?

Read each situation and decide for yourself, then tap a card to flip it and check your answer.

Sort the business-case elements

Drag each item into the bucket it belongs to — or tap an item, then tap a bucket. Hit Check placement when you’re done.

How to use it

Next time an AI idea lands on the table, steer it with a few questions: “What specific problem does this solve, and how do we measure it today?” “Is AI genuinely the best fix, or would something simpler do?” “What’s the full cost once we include review and data work?” “Can we run a small pilot with a clear success measure first?” Keeping the conversation on problem, value, full cost, and proof helps your team invest in AI that actually earns its place — and walk away early from the projects that won’t.