Is Your Team Actually Ready for AI? A Checklist for Sri Lankan Business Leaders

A founder I know put it bluntly: "I bought everyone ChatGPT Plus licences eight months ago. I have no idea if anyone's actually using them."

That's not a training problem yet. It's a visibility problem. And it's more common than most leaders admit out loud.

Before you spend money on AI training, it's worth answering an uncomfortable question honestly: is your team actually not ready for AI, or is something else quietly in the way? The checklist below is the one we walk clients through before we design anything. Be honest with yourself on each point; the answers tell you exactly what to fix first.

1. Do people know what's actually allowed?

Ask three people on your team, separately, what data they're allowed to put into ChatGPT or Claude. If you get three different answers, or blank stares, this is your real blocker, not a lack of enthusiasm. People who are unsure what's allowed default to using nothing, quietly, rather than asking and looking careless.

Fix first if: nobody can answer this clearly.

2. Is there a person your team can actually ask?

"Figure it out yourself" is not a rollout plan. Every team that's adopted AI well has one person, doesn't need to be technical, just credible and available, who colleagues can go to with a real question. Without that person, curiosity dies quietly in a browser tab nobody opens twice.

Fix first if: the honest answer is "no one, really."

3. Does leadership actually use it, or just talk about it?

Teams read what leadership does, not what leadership says in the town hall. If the founder or department head has never actually used AI for a real piece of their own work, the team notices, and treats the initiative as optional.

Fix first if: you can't name something you personally used AI for this week.

4. Are people protected enough to experiment, or just watched?

If every AI-assisted piece of work gets picked apart harder than normal work, people stop trying. Early experimentation needs a bit of grace: clear boundaries, yes, but also permission to get it wrong on a low-stakes task before it matters on a real one.

Fix first if: your team's honest answer is "we'd get in trouble for using it wrong."

5. Is there time, or just intention?

"Everyone should be using AI more" said in a meeting, with no protected time and no lighter workload to make room for the learning curve, produces nothing. Adoption needs actual hours, not just encouragement.

Fix first if: nobody has been given time to practise, only the expectation that they will.

6. Do you know what "good" looks like for your specific business?

Generic enthusiasm ("AI is powerful!") doesn't change behaviour. Specific examples do: this report went from two hours to twenty minutes, this proposal draft used to take a day and now takes an hour. If your team can't picture a concrete before-and-after for their own job, they don't yet have a reason to change how they work.

Fix first if: the goal has never been made specific to your actual workflows.

What This Tells You

If most of your answers point to "no" on points 1 to 3, training isn't your first move, clarity and a named point person are. Bringing in a workshop before that just adds information to a team that still doesn't know what's allowed or who to ask.

If you're mostly solid on 1 to 3 but stuck on 4 to 6, that's exactly what structured training is for: giving people permission, protected time, and specific examples tied to their actual work, not a generic AI 101 session.

Either way, the honest audit matters more than the enthusiasm. We've seen companies spend real money on training that never had a chance, because the basics, what's allowed, who to ask, whether leadership uses it themselves, were never in place first.

If you want a second pair of eyes on where your team actually stands before you commit to anything, we run a short AI Skills Audit as part of every discovery conversation, no cost, no obligation, just an honest read on where the real blockers are.