Solution businesses were priced on tools the customer lacked. AI hands the customer the tools.

Jesper E. Thomsen · 29 October 2026

Manufacturers moved from selling products to selling outcomes because they held the software, the data and the expertise to run them, and customers accepted the dependency. AI makes much of that layer cheap and adaptable on the customer's side. The part of the offer that solved a problem the customer could never solve alone still holds. The part that was convenient because the customer lacked the tools is repriced first, and the operating model has to follow.

For most of the industrial century the way to grow was scale. Bigger plants and broader distribution paid, but producing more still meant consuming more material, labour and capital. Software and cloud broke that link for the companies that sold software, and manufacturers tried to borrow the effect by selling solutions instead of products. Rolls-Royce sold engine hours rather than engines. SKF managed the customer's bearing inventory rather than shipping bearings. The pitch was recurring revenue and a closer relationship, and customers went along with it because the supplier held the software, the data and the expertise needed to run the service.

AI changes the second half of that sentence. It changes what suppliers can produce, which gets most of the attention, and it changes what customers need to buy, which gets less.

Why the dependency was accepted

A solution contract bundles three things that are usually discussed as one. There is the physical asset and the network that services it. There is the risk the supplier takes on, availability guaranteed, downtime paid for by the supplier. And there is the information layer, the monitoring, the optimisation, the recommendations and the reporting that tell the customer what to do and when.

Customers did not accept the bundle because they loved dependency. They accepted it because the information layer was expensive to build and needed specialists they did not employ. A plant manager could in principle have run his own bearing analytics. In practice the model, the data pipeline and the people to interpret it cost more than the premium SKF charged for doing it. The convenience was priced in, and nobody had a cheaper option to compare it with.

What AI makes cheap

Capabilities that once needed a large budget and a specialist vendor are becoming cheap and adaptable on the customer's side. That does not mean every customer will build its own systems. Most will not want the burden or the risk, and many should not. But the option now exists where it did not, and an option changes a negotiation even when it is never exercised.

Three uses are already visible. A customer can optimise across several suppliers instead of inside one supplier's platform, which removes the quiet advantage of being the system the customer looks at every morning. A customer can test the supplier's recommendations, replacing the service interval and the spare parts forecast with its own model and seeing where the two disagree. And a customer can build partial substitutes, enough of the information layer to know what the rest should cost.

The customer may well keep buying from you after all three. It buys with better information than before, and the margin that sat in the convenience layer goes with it. The relationship the solution model promised turns out to have rested partly on an asymmetry, and the asymmetry is closing.

The three parts do not move together

This is where the two standard examples stop behaving alike. In the engine business the lock-in was never mainly informational. The airline cannot certify an engine, cannot stand up a global maintenance network and does not want the availability risk back. AI gives it a better view of what it is paying for and a stronger hand on scope and price at renewal. Leaving gets cheaper on the information part of the contract and stays as expensive as before on the rest, so the contract gets renegotiated rather than replaced.

Bearing inventory management carries more of its value in the information and logistics layer. The customer owns the machines, the sensors can be anyone's, and the logistics are close to a commodity. What the supplier keeps is the knowledge of how bearings fail in that installed base and the reliability of the service itself. The analytics premium is the part AI reprices first, because the problem it solved was a lack of tools rather than a lack of possibility.

The split runs inside each company's offer, not between companies. The question for a leadership team is which parts of its own offer solved problems the customer truly could not solve alone, and which parts were convenient mainly because the customer lacked the tools. The second category is repriced at the speed model costs fall, the first at the speed of certification, service networks and liability, which is far slower. Most solution portfolios contain both, priced as one.

Where the data sits

One asset is left out of the above and should not be. AI gives the customer the model. It does not give the customer the data if the data is generated by the supplier's sensors, held on the supplier's platform and licensed back under the service agreement. In many installed-base businesses that is the real moat, and it was built almost by accident.

Expect it to stop being accidental. Data access, portability and the right to run your own models on your own operating data will move from the appendix of the service agreement into the first negotiation round. A supplier that has quietly relied on holding the data should assume that position is now visible to the customer, because the customer has the tools to notice what it is missing.

The same shift works in the supplier's favour on one axis. AI lowers the cost of the model for everyone. It raises the value of the data the model is run on, and a supplier with ten thousand installations has operating data no single customer can assemble from its own plant. Failure patterns, the effect of a maintenance regime across hundreds of sites, what a reading means for this machine given what it meant for the other nine thousand. A customer can build the model. It cannot build that. So AI does not simply remove margin from the solution business. It moves margin out of the convenience layer, which the customer can now reproduce, and into the cross-installation layer, which it cannot. Suppliers that treat their data as something to hold will defend the first and lose it. Suppliers that offer the customer its own data freely and compete on what the whole installed base teaches will give up a small position to take a larger one.

What the argument misses

The argument treats the information layer as something the customer can now reproduce. That is true of the model and not yet of the judgment. Knowing what a reading means for one plant, with its history and its maintenance habits, is still mostly in the heads of the supplier's engineers, and a customer running its own model without that context will be wrong in ways it cannot see. How long that lasts differs by industry, and in the industries where the supplier's engineers are also the ones who turn up when something breaks it may last a long time.

It also assumes customers want the option. Many procurement organisations are built to buy outcomes, and the people in them have no incentive to take back a risk they successfully pushed out. The repricing described here will come from customers with operations people in the room, and it will come unevenly, so a supplier will see it first in a few accounts and can mistake that for a few difficult customers.

The operating model has to follow

The sorting above is a pricing exercise before it is anything else. A solution priced as one bundle hides which part the customer is paying for, and when the customer can price the information layer itself the bundle unravels from the inside. Pricing the asset, the risk and the information separately, at least internally, shows where the margin actually sits and how much of it is exposed.

The service organisation follows the pricing. A service unit sized for a world where the supplier interprets everything is too large for a world where the customer interprets half of it. The people who remain need to be the ones who handle what the customer still cannot, the exceptions, the physical intervention, the liability.

Sales changes last and hardest. Part of solution selling was the creation of dependency, and sales organisations were paid to deepen it, even where the contract also transferred real risk and improved the asset. Selling to a customer that can check your numbers is a different job. The claim has to be verifiable, the outcome has to be one the customer can measure with its own tools, and the premium has to be explainable in terms of the first category rather than the second.

The economics above describe the direction, not the timing. Which year a given industry's convenience layer is repriced depends on model costs, on who owns the sensors, and on how many customers have the people to use the option. A supplier that reprices itself before the customers can is giving away margin. One that waits for the customers to do it is giving away the terms.