First Principles
The ROI on AI is adoption. What blocks adoption is a fear you can’t measure.
Ganesh Ariyur, Chief Information Officer at Transform Smarter, on why the return on an AI investment is decided by something that never shows up on the dashboard tracking it.
9 min watch
Ganesh Ariyur
Chief Information Officer, Transform Smarter
Enterprise technology and transformation leader with more than two decades running large-scale modernisation for Fortune 1000 companies. A chess player, which turns out to be the lens he uses on his own team.
What this conversation is about
Ganesh opens the way a chess player would. The person on his team who mattered most last year was the one thinking four or five moves ahead — anticipating what the project, the company and the stakeholders would need before anyone asked. It’s the kind of contribution that’s enormous and nearly invisible, because the problems it prevents never happen, and a problem that never happens leaves nothing on a report.
His read on a struggling team is just as behavioural. Not a metric — a change in someone. The person who engaged in every meeting goes quiet; the words spoken and the words not spoken shift. His response is to move it to a one-on-one, because people won’t say the real thing in a bigger room. When a genuine crisis hit — a production emergency the Wednesday before Thanksgiving — what he remembers isn’t the fix. It’s that one person stayed, and then a second said I’ll stay with you, and then a third, until the thing was solved.
On AI he’s an optimist with guardrails, and disciplined about it: governance, data quality, honest pilots, the expectation that not every pilot ships. But the sharpest thing he said was about return. The payoff from AI, he argues, comes from adoption — and adoption comes from trust — and trust doesn’t form while apprehension sits underneath it, unspoken. Which makes the fear he can’t see the single thing standing between his company and the return on what it spent.
Three things to take from this
1.
The most valuable work often leaves no trace.
The engineer Ganesh singled out is the one who prevents problems, and prevention is invisible by definition — the disaster that didn’t happen generates no ticket, no war-room, no applause. If your best work is seeing three moves ahead and quietly removing the obstacle, you have a visibility problem that firefighters never have, and you have to solve it on purpose. If you lead people, build some way to see prevented problems, or you’ll systematically overlook the person doing the highest-leverage work you have.
2.
The quiet is the signal.
The earliest sign a team is in trouble isn’t in the burndown. It’s the person who used to speak up and has stopped. If you’re the one who’s gone quiet, know that the withdrawal is more legible than you think — it reads before you intend it to. If you manage, treat the one-on-one as what it actually is: not a status update, but the only setting private enough that someone will tell you the real thing. The truth about how people are doing does not survive an audience.
3.
You bought the tool. You may be paying for the fear.
Ganesh’s chain is worth memorising: return comes from adoption, adoption from trust, trust is blocked by apprehension nobody can see. Which means the thing capping your AI ROI isn’t the model or the licence count — it’s a state of the people being asked to use it, and it’s invisible on every adoption dashboard there is. Stop measuring usage and start watching behaviour: what people do when the tool fails tells you more than any number of prompts submitted.
“You have to give them the safety net to open up in a safe zone.”
— Ganesh Ariyur
Host’s note — Dharma Ramasamy
Ganesh did something on the last question that almost no one does: he connected the fear he can’t see directly to the money. Return on AI comes from adoption, adoption from trust, and trust can’t form while apprehension is sitting there unspoken. He’s right, and the reason he’s right is physiological, not motivational.
Apprehension isn’t a belief you can argue someone out of. It’s a state, and while it’s running, the nervous system quietly deprioritises the exact behaviours that adoption is made of — experimenting, reaching for the unfamiliar tool, tolerating the stretch of being slower before you’re faster. A system that reads its situation as threatening doesn’t explore. It conserves. It returns to what already feels safe, which in practice is the old way of working. From the outside that looks like resistance to the technology. It isn’t. It’s a body running risk management the person never consciously chose, and no amount of enablement content speaks to the part of them making the call.
So in most organisations the adoption curve stalls and gets diagnosed as a training gap or a change-management gap, and more training gets thrown at it. Underneath, it’s an unmeasured fear gap, and the two don’t respond to the same medicine. This is why the leaders who care most still fly blind here: the state that decides the outcome is the one thing their instruments were never built to read, and asking directly changes the answer. Ganesh wants a hundred percent adoption. He’s already worked out that the obstacle isn’t the tool. It’s the condition of the people being asked to trust it — and that condition is exactly what stays dark.
Be on the series.
Five more of these this quarter. Twenty minutes, two cameras, nothing to prepare, and you see the edit before it publishes.
Get in touch →The apprehension Ganesh wanted to catch early.
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