Desk to Boardroom

Where you sit determines what you can do. After enough time in and around middle management, directing agents came naturally to me, because it isn’t that different from managing people. A coding agent and an offshore team leave you with the same job: someone else makes the mistake, and you’re the one who answers for it.

The same repricing, and the same decisions about what to stop and who keeps the gain, look different from the desk that does the work, the team leader’s chair, the executive floor, and the adviser’s side of the table. Each seat can change something the others can’t.

You may occupy more than one of these positions in the same week, sometimes in the same meeting. The forces are the same in each, but the instruments for doing anything about them are not.

Doing the Work

If you do the work, the transition looks like a local problem. You handle the cases and keep the workflow moving when the formal process fails. You may well understand the problem better than the people redesigning it, and have less power to change it than anyone in the redesign workshop, assuming you were invited.

Start by separating the tasks from the responsibilities. The tasks are easy to list: drafting the brief, pulling the Friday numbers. The responsibilities are harder: noticing that the customer’s explanation doesn’t match the form, or deciding when the policy should bend for a long-standing customer. AI comes in through the tasks, because those fit in a requirements document. Your exposure and your bargaining power are in the responsibilities.

A few questions locate you more precisely than your title. Which part of my work produces something a dashboard can count? What breaks when I’m on holiday? The org chart describes authority. For responsibility, look at who gets the call when something goes wrong, which may not be who the org chart would suggest.

Some people are valuable because they are the duct tape on a broken system. You know which field is always wrong, and whom to call when the workflow says no and the situation needs a yes. That knowledge can make you essential, and it can trap you. If the new system captures the formal process and misses the workaround, you become the person silently repairing the gap, except that the gap now opens at machine speed. If it captures the workaround as data, your knowledge becomes your employer’s infrastructure, which is nice for them, and your position stays exactly where it was. So document enough to make the defect visible, but don’t become the private patch for a public process.

You can’t restructure the compensation model or sponsor an operating-model change from your desk. But you can change the conversation with evidence, which gets further than complaint, even if it is less satisfying. Record where the tool saves time and where the work comes back: the Friday afternoon spent fixing the classifications it got wrong, or the decision that moved while the responsibility stayed with you. Find the people in IT and operations who see other parts of the same gap.

Leading a Team

For a team leader, the change comes on particular days. Someone asks in a one-to-one whether their role is safe. A workflow that worked last quarter is now slower than what AI can do. The instinct is to schedule training or draft an adoption roadmap, because both can be reported upwards by the end of the month. Make a map first.

Map every role by what people spend their time on rather than by job description, and mark what each recurring item is for: producing something, or proving a rule was followed. “Product Owner” is a title. “Writes and refines user stories for the backlog” is an activity.

Workflow redesign is the level between tool adoption and operating-model change. It answers specific questions. What gets delegated to AI, and under what conditions? Who reviews, and how? A generic “review for quality” catches the obvious errors and misses the subtle ones. Review against the ways AI fails in your domain catches more.

A well-designed AI workflow can still fail at scale in review. A review step says “senior person checks output.” A review system also needs an up-to-date list of the ways AI gets things wrong in your type of work, and a sampling strategy matched to risk.

Once a dashboard lists the freed hours as available capacity, somebody will want them filled, and depth is hard to defend on a spreadsheet that only has a column for volume. So reclaiming the hours is a fight, and the team leader has to decide whether to pick it.

Reclaiming time takes tactics, and each of them makes the team leader visible as the person arguing that the freed time belongs to the team, which is not always the reputation you want with your own manager. Staying invisible keeps your standing intact, but you lose the time. Start with pre-commitment: before deploying AI on a workflow, write down what the time will buy, and route the document through your own manager into the roadmap. Hours with an owner and a destination are harder to take away than hours listed as “available capacity.” It doesn’t guarantee the hours stay where the team promised. But it does mean that taking them is a decision someone has to announce, instead of a quiet drift towards “the team has free capacity”.

And don’t let the gain be booked silently. Claim it in writing: what the team delivered, what it would have taken before, and what share of the gain the team wants back as protected practice time or lower throughput targets. The claim won’t always succeed. But if the organisation books the whole gain anyway, somebody had to decide that on record.

Meanwhile, nobody else will manage the manager’s own exposure. Where a manager’s seniority is calibrated to the size of their team, AI that lets a smaller team produce the same output shrinks the seniority with the team, and a team that size may not need a director. If your seniority depends on team size, the question is what justifies your level independent of headcount.

Setting Strategy

At the top, the forces come in as budget and headcount decisions, and as the political negotiations that decide whether anything changes at all. The one diagnosis you can’t delegate is whether the AI investment is a cost cut or a response to a market that is moving with or without you. If the business model is sound and AI makes you better at executing what you already do well, the investment case is easier. If the competitive environment is changing, the signs are visible already: competitors delivering at a fraction of your cost. And no amount of internal efficiency fixes that.

An executive team can tell which situation it’s in. The discomfort is political, because describing the second means admitting, in front of the people who built it, that what worked will stop working. The test runs on revenue. If overhead is being commoditised by AI, that is a cost reduction opportunity. If revenue-generating activities are being commoditised in the market, the problem is strategic, and the revenue model is breaking. Sometimes the time hasn’t come yet, and you have a window to prepare deliberately.

Some metrics measure the wrong thing. Headcount as a proxy for capacity means budgeting for the wrong unit. Billable hours come under pressure when AI cuts expert hours without changing the output, and moving early towards outcome-based pricing beats negotiating later from a worse position, once the client has worked out what the hours were for. High tool adoption with no operating-model change is still tool adoption. And operating-model change is the executive’s job, which can’t be delegated to HR or to the AI team.

The executive’s part is to cut the artefacts the governance process requires instead of letting production balloon because it got cheap: a document that used to take a month and now takes an afternoon is no reason to ask for more of them. So is deciding which artefacts must come from a named human. A strategic recommendation or a risk assessment is only as credible as the person who signs it, and only if that person has read it.

Advising

Run the diagnosis on your own practice first, incentives included. Research and deliverables have become faster, and billing for them as if nothing changed gets harder to defend once clients have their own baselines. That leaves candour to sell, and helping a client own a decision under uncertainty, neither of which is measured in pages.

Clients ask for help with tool selection or training. The job is to test whether the frame hides a different problem: broken economics, or a workflow that should be deleted instead of automated. It is easy, and well paid, to help a client produce a transformation story out of pilots and adoption numbers. The harder work is adding up the full cost across every ledger it was booked to and saying what disappears. If nothing disappears, say so, at the cost of the follow-up engagement.

What an adviser has that nobody inside the client has is distance from its hierarchy. From outside, you can recommend stopping a piece of work when everyone inside has a reason to keep it, and that recommendation is the part of the job a client can’t do for itself.

Checklist

If you lead a team: have you mapped every role on its actual activities?
Is your review a step or a system: an inventory of how AI fails in your work and sampling matched to risk?
Before deploying, have you written down what the freed time will buy and sent it through your own manager into the roadmap?
If your seniority rests on team size, what justifies your level without it?
If you run the organisation: is AI commoditising your overhead, or what clients pay you for? Run the test on revenue.
Which of your metrics track activity rather than change: licences, training completion, headcount?
If you advise: which piece of the client’s work would you recommend stopping, and what would saying so cost your next engagement?