I learned what ownership really means on a motorbike, long before I ever heard a consultant use the word.
I was mid-lesson, out on an open road, when a car pulled up to a side junction ahead. I had right of way and rode straight past. Afterwards, my instructor asked me a simple question: “If that car had pulled out and you’d hit it, whose fault would that have been?” I said, “Theirs, obviously. I had right of way.”
“If you’re going to ride a bike, you need a mindset of total responsibility. What the Highway Code says won’t matter at all while you’re lying in hospital.”
There were things I could have done to cut the risk: slow down a little, move across the lane to be more visible, cover the brake. The fact that I “shouldn’t have to” was irrelevant. My job wasn’t to be technically in the right. It was to get home safely.
That distinction, between being entitled to something and being responsible for the outcome, is the whole of what we mean by ownership in consulting. And it’s worth revisiting now, because AI is testing it in ways some people haven’t clocked yet.
What ownership actually means
We see people arrive at ownership from very different starting points. Some seem to have the instinct for it from day one, whether that comes from a previous career, how they were brought up, or it’s hardwired into them. Others have to build it deliberately. We shouldn’t see that as a character flaw - just a different starting position.
One trait that connects strongly to ownership is a natural aversion to risk, and an ability to spot risk early. People with this instinct sometimes get labelled pessimists. I’d push back on that. As long as they act on what they see rather than just worrying about it, that instinct can be a superpower. It’s the same thing my instructor was training into me: don’t wait for the risk to become the accident.
The test we use with consultants is deliberately uncomfortable:
“Could you look your Partner in the eye and say you did everything you could reasonably have done to make this work?”
Not, “It wasn’t my job.” Not, “They were supposed to.” Ownership means holding yourself accountable for the outcome, not just the piece of work you were handed. It’s the difference between delivering your task and delivering the result the client is paying for.
What it looks like day to day
We can think of ownership as a set of behaviours. Owners:
Check and confirm, rather than leaving ambiguity to resolve itself.
Think ahead, spotting dependencies and risks before they become problems, rather than assuming a task is in hand because someone said it was.
Close the loop on open items instead of treating silence as agreement.
Escalate early, while there are still options, rather than reporting a problem once it’s already too late to do much about it.
Bring recommendations, not just issues.
None of that requires seniority or authority. It requires noticing, and then doing something about what you’ve noticed. The consultant who spots a client dependency slipping and picks up the phone rather than waiting for the next status call is exercising the same instinct I was being trained into on that bike. Don’t wait for the risk to become the accident.
Why AI is putting this to the test
In recent months, more clients than usual have been lamenting the lack of ownership displayed by their teams. I’m convinced that AI is exacerbating the problem, and for two reasons.
The first is speed. Used well, AI gives us genuine capacity, time we can reinvest in thinking harder about the thing that matters, rather than grinding through the thing that doesn’t. That’s great, but speed can have an unwelcome side effect: it compresses the moment where ownership should show up, the pause where you’d normally ask “does this actually stand up, and have I checked what needs checking?”
The second (more insidious) problem is the apparent authority of what AI produces. A confident, fluent, well-structured answer feels ‘finished’ and it’s easy to let that fluency replace your own judgement. People aren’t cutting corners intentionally, but are being hoodwinked into believing the polished-looking outcome is thorough and flawless.
Put those two together and you get consultants making incorrect assumptions, reporting a confident answer without interrogating it, and not checking because checking doesn’t feel necessary.
Can ownership be trained?
Yes. Every bit as much as leadership can be trained, and for the same reason: it’s a collection of behaviours, not a fixed trait.
It starts as a checklist. Back on the bike, that meant something very concrete: slow down by five miles an hour, cover the brake with your right hand, move towards the right of the lane. None of that was instinctive at first. I had to think my way through it, consciously, every time. What changed it into instinct was time, repetition, and good coaching from people who’d ride behind me and tell me what they’d seen.
The same applies to AI-assisted work. The starting point is a checklist: the handful of no-regret actions a team commits to doing every time they use AI to support delivery. Check the output against what you actually know, don’t let a confident answer substitute for your own thinking, flag what you’re not sure about rather than smoothing over it.
Trained effectively, practised consistently, and coached well by seniors who notice when it slips, these become things you just do because you’re committed to the outcome (be arriving home safely or delivering the piece of work at high quality).
If you’re wrestling with how your own team’s sense of ownership is holding up under AI, I’d be glad to talk it through. Drop me a line.
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