AI use cases: The ones you might be missing

88% of organisations are now using AI in at least one business function. Fewer than a third are scaling it across the organisation.

In our experience, that gap isn't a technology problem. It often comes down to two things, and they're more fundamental than most AI programmes are designed to address.

The first is confidence and capability.

Teams that haven't been properly upskilled struggle to see, and to realise, AI opportunities in their own workflows, let alone across the business. We find that building genuine AI literacy is a prerequisite for any meaningful use case development. By this we mean not just awareness, but the practical ability to identify and articulate where AI could make a difference. This is why any of our AI engagements, whether they are entirely training focused, or broader programmes, focus on leaving a business with skills, not slides. We equip teams to run with AI by themselves, rather than leaving you with strategies that are out of date immediately.

The second problem is more structural

Most organisations identify AI use cases function by function. Each team maps its own high-toil tasks and builds a list. But often, many of the highest-value opportunities aren't inside functions. Instead, they're in the connective tissue between them, where information moves from sales to ops, from ops to finance, from finance to the board. That's where one team's output becomes another team's problem, and nobody claims ownership over the gap.

Those use cases don't get deprioritised. They never get identified at all.

This matters because the organisations capturing real value from AI aren't bolting it onto existing processes. They're rebuilding whole workflows end-to-end, picking a value stream that crosses functional boundaries and redesigning it from scratch. That requires someone with a view across the whole business, not just a seat at one team's table.

For PE-backed businesses, this is acutely important. If your AI use cases were identified by function heads working in isolation, some of the opportunities that would actually move the needle on EBITDA probably aren't on the list, and the impact won't materialise in line with investor expectations.

How Palladium approaches this

Our AI Launchpad engagement is built around exactly this problem. We offer a spectrum of speed and depth of engagements, tailoring it to your business, but we focus on the same principles each time.

We start with AI Discovery, running structured workshops across the business to map the value chain, identify high-toil processes, and surface the cross-functional opportunities that function-led approaches typically miss. We build internal capability alongside it, so teams have the confidence to keep identifying and iterating on opportunities independently. We then prioritise ruthlessly, build working pilots fast, and hand over a functioning centre of excellence.

For businesses that want to understand their AI position before committing to deployment, our AI Horizon assessment provides a structured view of where AI represents the greatest risk and opportunity across the business model, competitive landscape, and value chain.