AI’s Biggest Winners Have the Lowest Margins

The biggest winners of AI need not be the companies with the most engineers, the largest data teams, or the highest software budgets.

They may be the companies with the lowest margins. The manufacturers, trucking carriers, distributors, staffing agencies, and field-service operators that have run on thin, single-digit margins for decades – the businesses nobody would ever call AI companies.

AI transformation creates value through three levers: revenue, cost, and risk. Most of the attention has gone to revenue through better products, faster sales, and more productive employees. But for low-margin businesses, the biggest lever to pull is cost – when profit margins are already thin, even small reductions in operating expense can create an outsized increase in earnings.

A software company running at 30% margins can use AI to become more efficient, but that efficiency gain usually does not change the trajectory of the business. A business running at 3% margins is different. Even a <1% cost reduction can lead to >25% profit increase.

Low-margin industries have historically been trapped in structural, low-margin environments. Properly implemented AI changes that equation. It gives low-margin businesses a way to attack costs that were previously treated as permanent – and the companies that move first are able to capture that gain as margin before competitors force it back into lower prices. Efficiency spreads across a commoditised market eventually, but the early movers are the ones who bank the earnings uplift and reset their cost position ahead of the field.

By the end of this article, you should understand how the lowest-margin businesses can finally attack the coordination costs that have kept them structurally low-margin for decades – and why the companies that move first will pull away from the rest of their industry.

The providers who solve this will build billion-dollar companies – and the businesses they transform will be the ones that escape the margin trap first.

The structural barriers low-margin companies face

For most low-margin businesses, there were structural barriers that have kept them locked into this position. They usually compete in commoditised markets, have limited pricing power, and carry large operating cost bases that were previously impossible to reduce without hurting service quality. Because they cannot move the market price – the market sets it, not the individual company – cost is effectively the only lever they control.

A meaningful share of that cost base is labour – and beyond the physical work itself, these companies also carry the cost of coordinating it.

There is a long list of coordination work that erodes the margin of these companies over time. For instance, scheduling, dispatching, approvals, exception handling, and countless administrative loops are incurred by labour-intensive companies, and thus, eat away at a company's bottom line. That coordination work is where AI has the clearest opportunity to move the needle for labour-intensive, low-margin businesses.

In these types of companies, labour costs typically account for nearly 25% of revenue. Roughly a quarter of this labour spend is tied to managing, coordinating, and administering the work, equating to ~6% of revenue. For a company operating at a 3% margin, easing the coordination burden by 10% can improve earnings by ~20%, changing the entire earnings profile of the business.

As a result, AI does not just make them slightly more efficient. It gives the companies that adopt it early a chance to open a structural cost advantage over their competitors – and to run as a genuinely higher-margin business, perhaps for the first time.

The problem is that the companies with the most to gain from AI are often the least able to adopt it

Most solutions sold in enterprise AI today have the assumption that employees will adopt a new tool, use it correctly, and slowly turn usage into value that gradually gets realised in the P&L. Considering that this assumption doesn’t hold even inside tech-forward companies, it is only worse inside a manufacturing company, logistics business, or any other labour-heavy company where the workforce is not used to adopting a new software product. These businesses are often the least susceptible to change management.

The real question is how to get AI-driven margin expansion without relying on employee adoption – or at least without enforcing new interaction surfaces. That is the challenge, and its solution might be the most addressable trillion-dollar opportunity in AI right now.

Three steps to solve the trillion-dollar low-margin challenge

1) Find the hidden coordination cost

Most people think about AI cost savings too narrowly. They imagine replacing a task, reducing headcount, or making an employee faster. That can matter and will likely happen in the future, but where AI capabilities are today, a significant piece of the opportunity is the work behind the work: the overhead required to keep messy human operations moving.

The frontline employee does the job, but behind the execution of the task is a system of managers, supervisors, analysts, finance teams, operations teams, and back-office staff making sure work gets completed and routed to the right division in the company.

That coordination layer exists because human work is inherently messier than AI. Humans naturally make judgment calls differently, and each person carries their own context of the company and the task at hand. Over time, this becomes a massive operating cost to coordinate inside the organisation, and thus, the coordination layer emerges.

Take a logistics company we recently worked with. The visible labour cost was the drivers, but the company was also paying for the coordination infrastructure around them: dispatch teams, routing changes, customer updates, claims, invoices, exceptions, and back-office reconciliation. That additional coordination expense added up to nearly 10% of revenue, and it became the spend we were able to attack in the transformation.

After doing several other transformations for low-margin businesses, we realised this was not an edge case. The same pattern shows up across logistics, manufacturing, facilities management, field services, staffing, healthcare clinics, and other labour-heavy businesses where the service is hard to differentiate, pricing power is limited, and the operation depends on constant human coordination. These companies cannot simply raise prices to escape the problem. Their margins stay compressed because they need a large coordination layer to deliver a relatively commoditised service reliably.

2) Remove employee adoption as a bottleneck

If tech-forward companies struggle to get large-scale AI adoption, it is unrealistic to expect a different outcome for a non-technical workforce. Most enterprise AI products still depend on that behaviour change. They ask employees to open a new interface, remember when to use it, decide which tasks it applies to, and then translate the output back into the workflow they were already doing. In practice, that turns AI into another place work has to happen, instead of a system that actually removes work.

That is why adoption fails. Employees do not want another tool that helps them do the work. They want the work to be done. The ideal solution is not a better interface for employees to use, but a system that runs inside the existing workflow and removes the need for most of that interaction in the first place.

3) Embed AI at the infrastructure level of a company

What we have found across these deployments is that the best AI deployments make agents part of the operating layer of the company. It is layered on top of existing systems, inboxes, files, approvals, and workflows where work already happens today.

If accounts payable runs through NetSuite, email, PDFs, and spreadsheets, the agent should run across NetSuite, email, PDFs, and spreadsheets. It should extract the invoice, match it to the purchase order, flag the exception, prepare the approval, and route the issue to the right person only when judgment is needed. It should then go beyond that to learn from the approval feedback to refine the agent as time goes on. Value should be realised without an employee adopting and using a new system – it should be engineered into the AI deployment.

The million-dollar lesson we’ve learned is that in order to engineer value from AI into a business, you need to sell AI as infrastructure. Software asks the employee to adopt a tool, but infrastructure changes the operating layer underneath the employee. The employee should still know what happened, and the process owner should still be able to pause the workflow, change a rule, approve an exception, or pull a person back in when needed. But the value should not depend on someone remembering to use the AI every day.

The biggest AI opportunity is hiding in the least obvious place

This is why low-margin businesses are the biggest margin expansion opportunity in AI.

They have the strongest economic incentive because small margin improvements create massive profit increases. They have large labour and coordination-heavy cost structures that AI is uniquely suited to reduce. And they operate in industries where becoming even slightly more efficient can change the competitive position of the company.

The market has focused on software companies, tech-forward enterprises, and knowledge workers because those companies adopt tools faster and have the budgets to experiment. But the largest profit impact may come from the businesses that are least likely to describe themselves as AI companies.

These are not obvious AI winners because they do not look like AI companies from the outside. But that is exactly why the opportunity is so large.

Their margins are thin because their operations are heavy. Their operations are heavy, often because labour has to be coordinated. And AI is the first technology that can remove a meaningful amount of that coordination work without requiring the entire workforce to change how it works.

The next wave of AI winners will come from putting agents behind the workflows of low-margin businesses and letting the savings show up quietly in the operating model.

If your business wants similar results, akin to our other manufacturing and logistics clients that have seen 8-figure margin uplifts, find us at varickagents.com