Here is the layer underneath that story I have not written about yet.

While AI is quietly reshaping who stays employed, a great many of those same employees are bound by contracts telling them they may not build anything else for themselves while they work there. No consulting, no advising, no paid work in their own time, even in fields with no overlap with their employer. Once, that restriction was a fair trade for real security. The company protected you, so you gave it your full loyalty in return.

That exchange is no longer remotely equal, precisely because of what AI has done to the security side of the equation. Adoption has made employment less certain than at any point in recent memory. The exclusivity clauses did not loosen to match. If anything, they tightened, as companies grew more protective of proprietary knowledge in a world where AI made it easier than ever to reconstruct or replicate what a departing employee understood. The obligation stayed. The protection went the other way.

I want to tell you about someone I have advised, through a composite, because the situation is too common to belong to just one person.

He was a senior engineer at a mid-sized technology firm, five years in, well regarded, well paid, and increasingly aware that his own AI fluency was exactly what made him replaceable on paper and irreplaceable in practice, depending on which quarter the company was having. His contract had a standard clause. He could not consult, advise, or take on any paid work outside the company, even in his own time and in a field with zero overlap with what his employer did. The company called it protecting its interests, which in an AI-enabled competitive landscape had come to mean almost anything.

Eighteen months ago, the layoffs started. Not because of anything he did. A restructuring, an AI-driven efficiency programme, the usual language. He was not among the first round. But he watched three colleagues he trusted leave within a single quarter, each one blindsided, each one walking out with a severance package that lasted a few months and a non-compete that outlasted it by a year, at the exact moment AI adoption across the industry meant fewer companies were hiring for the roles they used to fill.

He did the maths. If he was next, he would have a contractual obligation not to earn income in his own field for twelve months, in a market where AI had already shrunk the number of roles available to absorb him. The company that would enforce that clause had just demonstrated, three times over, that it owed him nothing beyond a notice period, in an environment it had itself made more precarious.

So he made a decision most people in his position quietly make. He kept doing his job well. And he stopped believing a word of the loyalty language in the town halls, especially the parts about how AI adoption was going to create more opportunity than it removed.

Here is the trap, stated plainly. Organisations increasingly ask for exclusivity, sometimes explicitly through non-compete clauses, sometimes implicitly through always-on cultures, while AI adoption simultaneously makes the underlying employment relationship less secure than it has been in decades. The obligation flows one direction. The protection does not flow back. And the very technology being deployed to increase efficiency is the same technology eroding the job security that used to make exclusivity feel like a fair trade.

This was never sustainable when both sides at least believed the old bargain still held, stay loyal, and the company would try to protect you when things got hard. AI adoption has made that belief nearly impossible to hold onto. What remains is the contractual language demanding loyalty, in a security environment the same technology helped dismantle.

Employees have noticed. They have not stopped complying. They have stopped believing, and those are very different things. Compliance without belief looks identical to engagement on a dashboard and is nothing like it in practice.

A framework for closing the gap, built for an AI-era workforce

I do not think exclusivity clauses are inherently wrong. Some genuinely protect real commercial interests, and AI has made intellectual property genuinely more portable and more valuable to protect. But if an organisation wants to ask for loyalty while deploying the very technology that makes employment less secure, it needs to offer something that makes the asking legitimate. Here is the approach I now put in front of leadership teams grappling with this.

Match the obligation to the actual AI-driven risk, not the maximum legal reach. Most non-competes are written by lawyers optimising for the broadest possible protection, not for what the business genuinely needs in an AI context. A senior engineer building proprietary AI systems and a marketing coordinator using off-the-shelf AI tools do not carry the same competitive risk, yet they are often bound by identical clauses. Scope the obligation to what AI fluency actually exposes the business to. Anything broader is asking for loyalty you have not earned.

If AI adoption increases your organisation’s need for exclusivity, price that increase honestly. A restrictive clause has real economic cost to the person carrying it, and that cost has grown as AI has made specialised knowledge more valuable and more portable. If your organisation genuinely needs tighter protection because of what AI has changed, pay for it directly, through compensation that acknowledges the constraint, or through a defined severance commitment that activates specifically when AI-driven restructuring is the reason someone is let go.

Tie the restriction’s duration to the security you are actually providing in an AI-disrupted market. A twelve month non-compete paired with a two week notice period was never balanced. It is even less balanced when AI adoption has shrunk the pool of roles available to someone bound by that restriction. If the organisation cannot commit to security anywhere close to matching the restriction, the restriction should shrink to match what can honestly be offered.

Say the quiet part in the room, specifically about AI, not just in the contract. Most organisations avoid ever discussing this openly, hoping nobody connects their AI transformation programme to the erosion of the security their contracts still assume. Leaders who address it directly, acknowledging that AI has changed the deal and being honest about what the organisation can and cannot promise in return, earn more real loyalty than any clause could enforce. People do not expect certainty anymore. They expect honesty about exactly what AI has changed.

He never got that honesty from his employer. So he did what most people in his position now quietly do. He complied with the letter of his contract and gave nothing beyond it. Not out of malice. Out of rational self-protection, learned the hard way, from watching what AI-driven restructuring did to the people who trusted the company before him.

The organisations that retain real loyalty through this next decade of AI adoption will not be the ones with the broadest contracts. They will be the ones honest enough to ask only for what they are willing to earn, in a world their own technology has already changed.

She was the best engineer on the team. Not the flashiest, the most useful. When the organisation announced its AI transformation, she did what she always did. She took it seriously. While others attended the workshops and quietly went back to working the old way, she rebuilt her team’s entire deployment pipeline around the new tools. Work that took the team a week began taking a day.

Leadership noticed. Her work went into the town hall deck. The transformation office used her pipeline as the case study that proved the programme was working.

Six months later, her team of eight became a team of four. The logic was impeccable. The pipeline she built meant the same output needed half the people. She survived the cut. Her four closest colleagues did not.

Then something interesting happened, and it is the part most leaders never see.

She stopped. Not visibly. She still used the tools, still attended the sessions, still said the right things. But the next three automation opportunities she spotted, she kept to herself. The clever fix for the testing bottleneck stayed in her head. When asked in a retro whether anything else could be streamlined, she said she would think about it.

She was not bitter. She was rational. The organisation had taught her, precisely and unforgettably, what happens to the surplus her ingenuity creates. It does not flow to her. It flows out the door wearing her colleagues’ badges.

Multiply her by every engineer, analyst, project manager, and finance professional who has watched this sequence play out, and you understand why AI transformations stall in ways no dashboard can explain. The tools are deployed. The training is complete. The adoption metrics look fine. And the compounding gains that the business case promised simply never arrive, because the people who could deliver them have quietly withdrawn from the bargain.

Leaders often describe this as resistance to change. It is the opposite. It is a workforce that understood the change perfectly.

The uncomfortable truth is that she would have kept building if the deal had been different. And the deal is the part leadership actually controls.

So let me talk about the deal.

The Adoption Compact

After watching this pattern repeat across enterprise clients in media, technology, and the public sector, I stopped treating it as a change management problem and started treating it as a design problem. What follows is the framework I now use when advising leadership teams. I call it the Adoption Compact, and it has four parts. Each one exists because its absence is precisely what taught our engineer to stop.

Part one: Name the destination before asking for the journey.

Most organisations ask people to adopt AI without ever saying what happens on the other side. The silence is not neutral. In the absence of an answer, people assume the worst, and they are often right to. The compact starts with leadership stating clearly what the efficiency gains will fund. Growth into new markets. New products. Redeployment into work that was never resourced. Or yes, in some cases, a smaller organisation. Any of these answers is workable. No answer is not. Our engineer never heard one. She filled the silence with the evidence in front of her.

Part two: Separate the efficiency decision from the headcount decision, visibly.

These are two different decisions made by two different logics, and when they collapse into one, trust collapses with them. In her story, the pipeline win and the team cut arrived in the same quarter, from the same logic, signed by the same hand. That proximity was the lesson. The organisations that navigate this well create explicit distance between the two. Efficiency gains are measured and celebrated in one forum. Workforce planning happens in another, on its own timeline, with its own rationale. When people can see that automating their work does not directly trigger a review of their role, they stop hiding their best ideas.

Part three: Make redeployment real, not rhetorical.

Every leadership deck says people will be freed up for higher value work. Almost none of them can name what that work is, who will manage it, or what budget it sits under. If the higher value work does not have a name, a leader, and a line in the plan, it does not exist, and your people know it before you do. The test I give leadership teams is simple. Show me the job that the freed up capacity flows into. If you cannot, you are not redeploying. You are queuing people for exit. Her four colleagues were never queued for anything else. Everyone on the team understood that, even before the announcement.

Part four: Let the people closest to the work own the automation agenda.

The deepest version of the paradox is that the people best placed to find automation opportunities are the ones with the most to lose from finding them. You cannot fix that with reassurance. You fix it with ownership. When the team that automates a workflow keeps a stake in the gains, whether through scope, advancement, or genuine input into what happens next, the incentive flips. The threat becomes an asset they control. The three opportunities our engineer kept in her head were worth more than the entire pipeline she built. Under a different compact, she would have brought them forward within the month.

The part leaders control

None of this is soft. It is the hard mechanics of keeping a workforce willing to build the thing that changes their own jobs. The organisations that get this right will compound. Their people will surface automation opportunities faster than any consultant could find them. The organisations that get it wrong will get one round of easy gains, and then silence.

The next time an AI transformation stalls somewhere in your organisation, resist the urge to ask what is wrong with the adoption metrics. Ask instead what your best people learned the last time they automated something. Their answer is running your programme now, whether you can see it or not.

The technology was never the problem. The compact is.

Not about AI tools. Not about frameworks or playbooks. About the thing nobody in the room says out loud. That the biggest obstacle to every AI transformation I have been part of is not the technology. It is the people holding it, the leaders avoiding the conversation, and the organisations that confuse efficiency with growth.

The response surprised me. Not because people agreed. But because of who agreed. Senior leaders, operators, practitioners who have been in those rooms too. People who recognised the silence I was describing because they had sat inside it themselves.

That told me something. The conversation needed more space than a LinkedIn post allows.

So this is that space.

What COOaaS is about

I have spent twenty years operating at the intersection of AI transformation, cultural intelligence, and organisational design, across Deloitte, AWS, McGraw-Hill, and advisory work for global enterprise clients spanning media, healthcare, technology, public sector, and education.

The through-line across all of it has never been the technology. It has always been the people. This space covers three things I keep coming back to. AI and the human cost of adoption. Cultural intelligence as an operational discipline. And the changing role of the COO as guardian of how people and AI actually work together. You can read more about all three on the About page.

Not as a consultant who parachutes in with a slide deck. As a trusted No.2. Someone who has sat in enough SteerCos, transformation programmes, and difficult people conversations to know that the hardest problems are never on the agenda.

What to expect

Each article here will go deeper than what I post on LinkedIn. The posts are the observation. The articles are the thinking behind it. The context, the nuance, the parts that do not fit in fifteen paragraphs.

The first series is called The Human Problem. It starts with AI adoption and goes somewhere most AI commentary does not. Into the psychology of displacement, the economics of automation, the responsibility of leadership, and ultimately the question of what meaningful contribution looks like in a world where the machines are getting very good at the work.

It is not a comfortable series. But I think it is a necessary one.

If you have found your way here from LinkedIn, welcome to the longer conversation.

If you are new, start with the first article when it lands. It will tell you everything you need to know about how I think.

Either way, I am glad you are here.

Suresh S Kartigaysu