The services businesses that will make financial sense are the ones that standardise before they automate

If you talked to twenty services firm founders this week (agency, architecture, consulting, legal, accounting), nineteen of them would tell you they’re using AI. Drafting, summarising meetings, tidying emails, speeding up research. That’s all fine, and most of it’s genuinely useful. We’d never tell anyone to stop.

What’s actually rare is a firm that’s pulled a repeatable step out of a workflow. Something that used to happen on every project, every week, and just doesn’t need a person on it anymore.

The gap between firms that have done this work and firms that haven’t is going to widen sharply over the next two to three years. We’ll cover the risks of sitting it out, but honestly we’re more interested in the opportunity if you move on it, and that’s what most of this piece is about.

Services firms were built on humans. That’s the whole reason we’re behind.

A software company can ship a feature once and serve a million customers. A creative agency can’t do that. Every brief is bespoke. Every architecture project has its own constraints. Every legal matter has its own facts. For decades, the answer to “how do we serve more clients” was “hire more people.” The Australian Bureau of Statistics classifies professional, scientific and technical services as one of the most labour-intensive sectors in the economy, and the maths behind that classification is exactly this: in services, capacity has historically meant headcount. The unit economics of a services business were humans per project, full stop.

AI is the first technology shift that genuinely changes that maths, but only if the business has been built so the work can be standardised. Most haven’t. McKinsey’s 2024 State of AI shows that while two-thirds of organisations now use generative AI in at least one function, only a fraction have redesigned the underlying workflows around it. Using AI without changing how the work runs is exactly where the financial returns get stuck. That’s not a failure of services firms; it’s a feature of the model. The firms a step ahead on this aren’t smarter or more technical. They’ve just figured out that buying AI tools won’t get you there. The work has to be standardised enough that AI can actually plug in.

This is the same lever that makes growth, profitability and cash flow legible in the first place. AI just happens to be the technology that finally pays you back for doing it.

The bit a lot of founders run into here is the realisation that nobody on the team has actually documented the most-repeated workflow in the firm. Onboarding looks different on every client. Status updates depend on which senior is running the project. Invoicing is “however Sarah does it.” If you can’t write the workflow down, you definitely can’t standardise it, and you can’t hand any part of it to software.

The last time this happened, we called them laptops.

There was a generation of services businesses in the late ’90s and early 2000s that didn’t move from typewriters and paper files to laptops and email fast enough. They didn’t go bankrupt overnight. They just got slower, more expensive and less competitive, and within five to seven years, the firms that had moved had taken their clients.

The pattern with AI looks similar. The fundamentals of running a creative agency, an architecture practice or a consultancy haven’t changed. The questions you ask about your own business have. “How long does it take a senior to write a first draft” used to be a question with one answer. Now it has two, and the firms making financial sense in 2028 will be the ones who chose the second.

What’s actually happening is just an S-curve, the same shape we’ve watched play out before with different technology in the same industries. The early movers get the steep part of the curve and the compounding capacity that comes with it. Everyone else gets the flat part. Nobody goes out of business in year one; the cost shows up over time as slower turnaround, harder hiring, and tighter margins.

Almost everyone uses AI. Hardly anyone’s actually getting paid back for it.

Here’s the distinction that does most of the work in this piece.

There’s ad-hoc AI, which is speeding up a one-off task. A senior uses ChatGPT to clean up a proposal draft. An associate runs a research summary through Claude. A junior gets help structuring a deck. All of this is fine, and it saves real minutes across a week.

Then there’s workflow AI, which is eliminating a repeatable step from how the firm actually runs. Status reports that used to take six hours of senior time across the team now take twenty minutes because the workflow is standardised and the pattern-matching parts are automated. That’s a different category of return.

Here’s how to size this for any firm, not just the example below.

Plug in your own numbers. The bigger your team and the more repetitive the workflow, the more you get back.

To see it concretely: take a fourteen-person services firm. Pulling weekly project status reports together costs the team about thirty hours a week across the mid-level roles involved, so roughly a hundred and twenty hours a month. If a standardised workflow with an AI assistant cuts the team’s combined effort down to five hours a week (around twenty hours a month), the firm has reclaimed close to a hundred hours a month. Divided by 160, that’s about two-thirds of a full-time equivalent, freed up without hiring anyone.

That reclaimed FTE-equivalent (the rough headcount unit you get back when saved hours add up across a team) now serves clients, takes on a new project, or builds the next standardised workflow. The first eliminated task funds the second one.

The point worth being clear on: this isn’t about replacing the person. The person is still there. They’re now spending those hours on the work the role was actually hired for: strategy, client conversations, judgment calls, the parts that needed a human to begin with. Standardising then automating gives the team back to itself, with the repetitive work taken off their desks.

That reclaimed capacity is also the cleanest way to lift gross profit margin (the share of revenue left after the direct cost of delivering the work) without lifting prices, and it shows up in cash flow within a quarter. This is exactly the kind of structural movement we built Visory Insights to surface for founders, since most accounting tools won’t tie a workflow change to its margin or cash flow impact on their own.

Reclaimed hours aren’t even the biggest thing. Consistency is.

This is the part that tends to surprise founders the most.

When you standardise a workflow, you also remove variance from how the service actually gets delivered. The output stops depending on which senior was on the project that week. Brand identity work doesn’t drift in quality based on whether your A-player or your B-player drove the brief. Status updates land at the same level of detail every time, regardless of who pulled them together. The financial close looks the same whether your most experienced bookkeeper closed it or someone newer did.

Most professional services firms have always been held hostage by individual brilliance, even if nobody calls it that out loud. The senior who runs onboarding is the only one who really knows how onboarding goes. The architect who’s done forty council submissions is the only one who knows where the traps are. When that person goes on leave, gets sick, or eventually leaves the firm, the workflow degrades and the firm absorbs the cost.

Standardising the workflow, then automating the pattern-matching parts, breaks that dependency. For the first time, the system runs the system. Your best people are still doing the strategy, the judgment, and the client conversations. They’re just not also being the single point of failure on every operational detail. That’s a different kind of business, and it’s a more valuable one when you eventually go to sell it or bring on a partner.

Nobody buying your service cares whether you used AI.

A lot of services founders worry about this one. If I use AI, will my clients feel cheated.

Think about what people actually buy. When someone buys your product or service, they don’t care which factory it was made in, what software you used, or even who the person is that completed the work. They care about the outcome. How good does the burger taste. How many leads did I get. How much time did I get back.

Translated to services: the client cares whether the brand identity landed, whether the building got approved, whether the strategy actually worked, whether the case got resolved, whether the books are clean. They care whether you delivered, on time, at the quality they expected.

It also helps to be clear about what AI is and isn’t doing here. Think about a Michelin-starred chef. Nobody walks into the dining room and tells her she can’t use the sharpest knives on the planet because that’s “cheating” and the knives are doing the work for her. The knives don’t make the food. The chef does. The knives let her do what she does, faster and with less fatigue at the back end of a fourteen-hour service. Your senior strategist still has the strategic instinct. Your architect still has the design judgment. Your lawyer still has the read on the case. The taste, the strategy, the way you deliver, all of that is still yours. AI is just a sharper knife.

There is a real way to lose this: using AI to do worse work faster. The way to win it is using AI to free senior people up to do better work, more often. The competitive question worth asking is whether your outcomes are getting better, faster, and at lower cost than the firm down the street. How AI-forward you look from the outside doesn’t actually matter.

Standardise first. Automate second. The order matters.

This is where most firms get the sequence wrong. They buy AI tools, hand them to the team, and wonder why nothing changes. The tools aren’t the problem. The work they’re pointed at hasn’t been standardised.

Here’s the order that works.

You do this for one workflow at a time, working through them in order of how many senior hours they’re costing. The compounding starts to show around the third or fourth one.

What this actually does to the numbers.

Three effects, in sequence.

Capacity unlocks first. The team finishes the same work in fewer hours. Either you serve more clients with the same headcount (revenue lifts) or you keep the same client base and reclaim margin. Most firms take a mix of both, depending on what their pipeline looks like.

Profitability follows. The gross profit margin lift compounds because the standardised workflow doesn’t just save time once. It saves time on every project from now on. The lift is structural, not a one-time bump.

Cash flow stabilises last. When cycle times shrink, billing milestones come sooner, which pulls debtor days (the average number of days between sending an invoice and receiving payment) down with them. The cash buffer (the months of operating costs you’ve got covered by available cash) starts to grow without anyone having to chase it. This is the kind of structural reporting view that most accounting tools won’t surface on their own.

Then there are the second-order effects most founders don’t price in until they show up. The same standardise-then-automate move applied to non-client workflows (HR, finance, internal sales and marketing ops, recruiting) reclaims another tranche of hours that was never on a billable timesheet but was real cost all the same. And once a few workflows are running this way, you’ll find software you used to need can come off the bill entirely. A project management tool that was holding together a non-standard process. A reporting tool whose output now gets produced upstream. An automation platform whose use case got absorbed into the workflow itself. We’ve watched firms cut five-figure annual software spend out of the SaaS line in the year after they ran two or three of these workflows through the loop. The savings on tooling alone usually cover the cost of running the AI layer in the first place.

Standardisation is doing the actual work here. AI is just what finally makes the standardisation pay for itself. Growth, profitability and cash flow all move at once, which is the only kind of financial change worth the effort.

A quick word on what happens if you don’t move on this.

We said we’d cover the risks. Here they are.

Clients start expecting faster turnaround as a baseline. Firms that haven’t moved start to feel slow without quite knowing why.

Senior people leave for firms where AI has already cleared the repetitive work off their desks. Recruitment costs rise.

New entrants (smaller, AI-native services firms) start competing at price points that used to be impossible. The squeeze comes from below.

None of this happens in a quarter. It happens over two to three years, which is exactly why founders find it easy to keep deferring.

What we’d do Monday morning

Pick the workflow that costs you the most senior hours. Write down the actual steps. Mark the pattern-matching ones. Standardise them this month. Automate the pattern-matching parts next month. Measure the cycle time. Move to the next workflow.

That’s the loop. Run it three or four times a year and the compounding kicks in. Skip it for a couple of years, and you’ll usually find yourself explaining to a board or a partner group why margins keep slipping.

If you’re running a professional services firm and you’ve never mapped your most-repeated workflow end to end, start there. Forget the new AI tools and the next consultant pitch for a minute. The unglamorous part is the leverage: writing down how the firm actually runs, so you can decide which steps deserve a human and which ones don’t.

Book a free discovery call and we’ll walk through which workflow in your firm is costing the most, and where standardising it would show up first in growth, profit, or cash.