Where to start with AI if you're a small Aruba-based team

"We should probably be doing something with AI" is a sentence we hear a lot in Aruba, usually followed by a pause, because nobody in the room is quite sure what "something" means for an organization their size. That hesitation isn't a lack of ambition. It's a reasonable response to a topic that gets talked about almost entirely in terms of enterprise budgets and dedicated data science teams.

Here's the reframe: AI adoption for a 15-person brokerage or a government department doesn't look like AI adoption for a multinational. It looks smaller, more specific, and (if you do it right) a lot faster to see results from.

Start with a task, not a strategy

The organizations that get stuck are the ones that start by asking "what's our AI strategy?" The ones that make progress start by asking "what's one task that eats an hour a day and follows a predictable pattern?" Reformatting client intake notes. Drafting first replies to common questions. Summarizing a report before a meeting. None of that needs a data science team: it needs someone willing to try it on one task and see what happens.

Three questions before you touch a tool

• Is the task repetitive and well-defined? AI is good at patterns it's seen before, not judgment calls that depend on context only your team has.
• Can a person still check the output? The safest first use cases are ones where someone reviews the result before it goes anywhere: a draft, not a decision.
• Would saving this time actually change anything? If the hour saved just gets absorbed into more of the same work, the win is smaller than it looks on paper. Pick a task where the time saved goes somewhere visible.

What "adoption" actually means at this scale

For a small team, adoption isn't a rollout: it's one person trying something for two weeks, telling a colleague what worked, and that colleague trying it next. It's slower to look impressive and faster to produce something that sticks, because nothing gets imposed top-down before anyone's tested whether it's actually useful.

That's the whole first step: pick one task, try it for two weeks, and see what you learn. Most organizations we talk to in Aruba are closer to ready than they think: they've just never seen what "small and practical" AI adoption looks like, because most of what gets written about it assumes a much bigger organization.

If you want a structured way to figure out where that first task might be, the EA Quickscan takes about two minutes and points you toward where the friction actually sits in your organization, AI included.