AI tools for CxOs

Complexity first, not a platform first

In a large organisation, “we will implement AI” is not a plan. It is a budget line that has to survive procurement, data residency, works-council questions, integration with systems that already disappoint, and a customer who will meet the result in public. Generating ROI from that pile is slow because the pile is not a job.

Smaller pieces find the right applications

Slice the work the way a customer already walks it: notice, enquire, buy, get onboarded, hit a problem, come back. Name the two touchpoints that leak money or trust. That is how you find the applications that belong to *this* organisation — not a vendor’s landscape, and not a transformation roadmap that cannot be staffed.

A drafting copilot and a customer-facing agent are different products. They should not share a risk tier. Mapping keeps those paths apart before someone buys a suite and hopes.

Make or buy stays strategic — start on the shelf

Make or buy will remain a key decision. Build when the experience *is* the product, or when no honest specialist exists. Buy when the job is standard enough that a specialised tool already finishes it.

On-the-shelf specialists are often the faster, lower-risk path for learning and experimentation: you see whether the job is real, who must own it, and what “good” looks like, without funding a platform that has not earned the next increment. The catalog is a short shelf for that purpose. The map is where you place one tool on one job.

See Cut a large AI programme into jobs you can actually buy for the working note, and what to ask before a customer-facing agent goes live when the experiment has to face a customer.