What a commercial lawyer told us about AI, headcount and the billable hour

Legal teams are being told to use AI with no time set aside to do it and no measure of what it should achieve. The firms and in-house teams that get results put hours against each task, settle who carries each risk, and use AI to bring work in inside its time budget. Alexandra Geelan, one of the founders of Hemisphere Consultants, sees that pressure from inside the legal functions she works in, and it matches what we see in firms.

Alexandra is a corporate and commercial lawyer, qualified in Australia and with experience in the UK, and she calls herself a generalist who can work across industries, businesses and sizes. Much of her work is fractional, which means she acts as part of the legal function for several companies at once. Her clients range from global tech businesses to finance consultants, and a good number of them already work in an AI native way.

Legal teams are told to use AI with no time to do it

Most of Alexandra's fractional clients are in-house legal teams, and she described the pressure on them plainly. "They're under immense pressure to implement AI to be more efficient, but they're not legal engineers, they're not developers, so they're kind of having to muddle their way through."

"What we're seeing is that legal teams aren't getting budget to increase headcount," she said. "They're kind of being asked to implement AI, but they're not given any time away from their business as usual stuff to do it."

A mandate like that sets no target and no measure, and the team asked to deliver it is already at capacity. We start by breaking the team's work into the tasks that create value, asking each person which ones take up their week and putting a number of hours against each. Each solution then goes on the roadmap fully costed, so a vague instruction to be more efficient becomes a plan the team can follow with the people it already has.

For a firm that bills by the hour, AI pays through fewer write-offs

The measure that mandate lacks is already in a firm's time records. "Lots of people are very keen to make sure that their businesses stay ahead of the curve," Alexandra said, "particularly because you do see so much 'AI means the death of the billable hour' and all that kind of stuff."

Most firms that charge by the hour work to a time budget for each job, and the fee is set or agreed from it. When a job runs over, the extra time is written off, so the firm bills less of the time it recorded. The share it does bill is its recovery rate (some firms call it realisation). When AI gets the work done inside the budget, less is written off and the recovery rate goes up, which lifts fee income and gross profit without anyone billing an hour they didn't work. The hours it frees go back into chargeable work, so fee earners hit their chargeable time targets and the same team takes on more clients without adding headcount. For an in-house team, the same hours are capacity it would otherwise have to hire for.

That's why every solution on the roadmap shows the time it saves at the charge-out rate of the person who does the task, so the firm can see what each one returns before it spends anything.

To know where your team's hours are going over budget, and which work AI should take on first, take the quiz below. It takes a few minutes.

Who carries the risk has to be settled before AI does the work

Those gains hold only while the work is right and someone answers for it. Because so many of her clients are AI native, Alexandra has spent time on how responsibility is shared once AI comes in. She told us she has "a good understanding of where you want the risk to kind of be allocated between you as an implementation partner and the actual underlying solution that you're kind of helping them to bring in."

Inside a firm, the same question becomes who answers for each solution. Every solution we put forward has its risks, how each one is mitigated and a named owner written down before it is implemented, and that owner checks what it produces before it reaches a client, the way a partner reviews a junior's work.

Alexandra also has clients who sell to law firms, and she knows how closely a legal team reads an agreement before it signs. A firm's own clients may read its AI policy just as closely, so it needs one it can hand over with confidence.

The firm should own its knowledge, whichever AI it pays for

The risk question runs on to the tools. Alexandra asked us whether a solution uses the AI the company already pays for, and pointed out that most companies now have an enterprise AI plan of some kind.

If a firm already has a business plan with one of the big providers, there's no point moving it off. Individual paid accounts with client data going into them are a real risk, and that use comes inside the AI policy first. Either way, the firm owns its knowledge and any of the main AI models can run each solution, so it isn't tied to one vendor if prices go up. Each solution runs where its data allows: the provider's own cloud when nothing sensitive is involved, the firm's own server where the provider never trains on what it sends, or a machine in the office where the data never leaves.

What firm leaders should do now

Staying ahead of the curve, to use Alexandra's phrase, comes down to an order any firm or legal team can follow, whoever does the work.

Start with the team's own work, broken down into the individual tasks and processes that create value for its clients: who does each one, how long it takes, what data it uses and who signs it off.

Before any solution, check the layers those tasks sit on: the business and each department, the tech stack, the quality, completeness and security of the data, compliance and governance, and the people who will work alongside the AI. Where a layer isn't ready, fixing it comes first.

Then set out a roadmap of solutions, each fully costed with the hours it frees up, what it costs to implement, its first-year return and what it does to the recovery rate on the jobs it touches.

Implement the solutions one by one, in the roadmap's order, each run where its data allows, with its risks designed out, covered by the AI policy and owned by a named person who checks what it produces, so the standard of the work is kept or raised. Once they are live, keep managing them as the models change, so a solution that drifts is caught before the time spent correcting it is written off.

Done in that order, the team gets more capacity without hiring and brings more jobs in inside their budget, lifting its recovery rate, fee income and gross profit in a way that is compliant and keeps client data secure.

If you want us to do it for you, book a free call and we'll find you at least $50,000 a year in new business that AI can unlock. It's compliant, data-secure and has governance built in.

Know exactly where AI would free up your team's billable hours first

Our quiz asks where your team's hours go, how often jobs run over their budget, what systems and data you already have, where client data ends up and how you would know whether AI was paying off. Your report shows where AI would free up your team's time first, the capacity it could give your firm in hours, and a 30-day plan for what to do.

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