AI Results in Service Agreements: Why Providers Hold Uncovered Liability

[00:00:02] 19% of services buyers now have agreements that pay only when the work produces a result. On the other side of the table, 13% of service agreements actually contain one. Those are counted differently – buyers on one side, contracts on the other – but they point the same direction. The distance between them has to land on somebody, and there's a decent chance it's going to be you.

[00:00:27] This is the Business of Tech. I'm Dave Sobel. Four numbers that have come out of four different corners of the market, and nobody's put them in the same sentence. Start with OpenAI. The information reports – and this has not been independently confirmed – that the company has quietly begun letting a select group of its largest enterprise customers pay only when an AI task actually completes successfully.

[00:00:55] Pay for the result, not for the attempt. OpenAI has not announced it, has not said which customers, and has not disclosed the terms. What is known is that it exists, and that it exists for the biggest accounts in the book. Now the shape of that practice across the wider market. Gardner has counted and CIO Dive carried the figures. 19% of services buyers currently have outcome-based arrangements.

[00:01:23] On the seller side, 13% of service agreements carry them. More of the market wants this than has signed it. Hold on to that one. Then a number from a completely different body of research. The National Bureau of Economic Research surveyed nearly 6,000 senior executives across the U.S., United Kingdom, Germany, and Australia, and asked what AI had actually done inside their companies.

[00:01:48] 9 in 10 reported no impact on their own firm's employment over three years. On labor productivity, 89% said the same. 6,000 executives, three years of spending, and the effect does not show up in the measurement. The layoffs have continued regardless. The market is a big deal. And one more, this one from inside the channel. The tech aisle mapped how small and mid-market customers move through AI adoption and broke it into four stages.

[00:02:17] Demand is real at every one of those stages. What is not consistent is who can serve it. Partner capability is strongest at the first stage, the foundational estate work, and it thins as the customer moves forward until at the most advanced stage it approaches zero. The further a client guess, the fewer providers exist who can take the next piece of work. So, the largest buyers in the market are being offered payment on results.

[00:02:45] The appetite for those terms runs ahead of the contracts that carry them. The productivity that would justify any of it does not appear in the measurements. And the ability to deliver runs out exactly as the customer gets more ambitious. Four facts. One of them is producing the other three. And it's not the one you would pick. If you're listening to this and haven't hit follow yet, on Apple Podcasts search business of tech. It takes five seconds and you'll get the next episode automatically.

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[00:03:45] Start at guards.com. That's G-U-A-R-D-Z dot com. Outcome-based pricing is not what a market does when it matures. It's what an executive does when they cannot tell. Consider the position that executive is actually in. Three years ago, they stood up in front of a board or a staff meeting or an earnings call and committed to artificial intelligence. Budget moved. Headcount moved.

[00:04:13] In a lot of cases, people were let go and the reason given was this technology. That commitment is on the record and it cannot be walked back. And they cannot demonstrate the return. And they cannot demonstrate the return. Thomson Reuters put a number on it. And note where this one comes from because Thomson Reuters sells AI products to exactly the professionals it surveyed. In their research, 91% of 1800 professionals said their own organization is falling short on delivering AI value.

[00:04:40] It's not a finding that the technology does not work. It's a finding that the people inside these companies cannot see whether it did. So the buyer changes what they are purchasing. Not the work, the certainty. If I cannot prove this paid off, I will stop paying until somebody else proves it. That's the whole appeal of paying on outcomes and wanting it is completely rational. So here is what it does not do. It does not create the measurement.

[00:05:09] Forrester's Abhut Sunil, writing in Channel Dive about outcome-based contracts in sustainability work, describes a constraint that travels well past his subject. Organizations lack reliable starting baselines. Data is fragmented across clouds and suppliers. And this is the line. Results often depend on customer decisions as much as on provider performance.

[00:05:35] He says the model works only in tightly defined initiatives where the provider controls the relevant levers and the customer has a credible baseline. The instrument requires the exact thing that it is missing. So outcome pricing does not solve the measurement problem. It relocates the obligation to solve it. Think of a homeowner who cannot tell whether the roof was actually fixed. They cannot get up there. They would not know good work if they saw it.

[00:06:05] So they stop trying to evaluate the work at all and say, I will pay you when it stops leaking. That is a completely reasonable thing to say. It also hands the roofer the entire question of what fixed means. The buyer who could not prove the value now buys from someone contractually obligated to define it. Which leaves one question standing and it decides everything after it. Who in this chain is allowed to say no?

[00:06:32] OpenAI can hand risk back to a customer, absorb the occasional failure across thousands of accounts, and still be the only place that customer can go. Not everybody has that. The further down the chain you go, the less of it there is. Here's where that lands on you. You are the end of the chain. There is nobody underneath you to hand it to. When a client asks to pay on results, you cannot pass that along to Microsoft,

[00:07:01] or to your RMM vendor, or to the model provider. Every one of whom you sold their product with the outcome carefully excluded. Whatever you agree to, you hold. That's not a warning. It's just the shape of the seat you occupy. TechIsle put a name on this when they mapped those four stages. At the far end, they wrote, Being accountable for what an autonomous system does means underwriting an outcome.

[00:07:27] And the overwhelming majority of the channel has no mechanism to price risk. Not no appetite for it. No mechanism. That is a gap somebody is going to sell into, and it will not be sold to you on your terms. Which makes one thing decisive, and it's not whether you accept outcome terms. It's whether you were in the room early enough to define what the outcome was. Look at who's getting that seat right now.

[00:07:54] The next web reports demand for fractional engineering talent up 9% in 90 days. And the arrangement named in the piece is the outcome-based retainer. Daniel Krzyzewski, who runs one of these practices, puts it plainly. Senior technology expertise creates value through decisions rather than hours logged. And an outcome-based structure gives the client a more direct way to measure what that leadership attributed. Notice what that person is really selling.

[00:08:24] It's not a deliverable. A judgment priced against a result. And they can price against a result because they sit next to the decision. They are in the room when the goal gets set, so they help set it. Then look at Okta's most recent quarter. The company says partners were involved in all 20 of its largest deals, driven by demand for AI agent security. 20 out of 20. Those partners are not in those rooms because they resold a license.

[00:08:53] They are there because they brought a capability into a conversation that had not been decided yet. Those are the same position described two different ways. Present before the definition is written. So here's the choice. And you'll make it whether or not you make it on purpose. Decide now which outcomes you are willing to be paid against. And pick only the ones where you control every input that produces them. Write them down. Put your own number next to each one.

[00:09:23] Take them to clients before a client brings you theirs. Or wait. And let the first client who asks write the definition for you. And find out you have guaranteed a result that depends on their staff, their process, and a model that neither of you owns. There's a much smaller way to start than the one you're picturing. This episode is brought to you by Control Map. Growing MSPs are using Control Map to build recurring revenue by expanding their GRC services.

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[00:10:24] Why do we care? Because the first outcome you price on should be the smallest one you are certain of, not the most impressive one you can imagine. Pick a single line, a response time, a recovery objective, an onboarding window. Attach money to it and run it for a year with a client who already likes you. You are not repricing the business. You are finding out what it costs you to be wrong while the number is still yours to pick.

[00:10:54] So what to consider? Price the first one off your worst month, not your average one. Pull your own last 12 months of data on whichever line you pick and find the month you performed badly. Because that is the month that will decide whether this was profitable. Most providers set the number against a typical month and discover the variance for the first time in the month it costs them. The point of a small pilot is to buy that discovery cheaply.

[00:11:22] So price it to survive the bad one. Write the client's input into the agreement as conditions at signing. Every outcome you could sell depends on something the client does. Approving changes on time, replacing the hardware you flag, keeping somebody in the seat on their end. Name those in the contract as conditions of the guarantee while the conversation is friendly and hypothetical.

[00:11:49] If you leave them out, you'll be raising them for the first time in the month that you missed the number, where they will sound like excuses rather than terms. Keep it as one line, not as the shape of the whole contract. Sell the outcome line inside an otherwise ordinary agreement, so that if it goes badly, it costs you a line item rather than a relationship. It also leaves you a control group. The rest of the contract price is the normal way.

[00:12:17] So at the end of the year, you can see exactly what the outcome line did to your margin instead of guessing. A provider who can say what one guaranteed outcome cost them is in a materially different negotiating position than one who's only ever guessed at it. And picture the provider who ran the small version of this a year ago. They have one line in one contract priced against a result for four straight quarters, and they know exactly what it cost them. The two months it went badly.

[00:12:45] What those months did to the margin. What the client said afterward. So when a larger prospect turns up next spring with an outcome number already written to a new draft, that provider is the only one in the running who can say from their own books whether the number is survivable. Everyone else is guessing. And it shows. If this trend continues, by the middle of next year, the outcome number will be arriving in the client's first draft of the agreement rather than yours.

[00:13:13] And the providers who never picked one will be negotiating it's a figure somebody else chose for them. This is the business of tech. Want to go deeper than the news? The Small Biz Thoughts community is where MSP owners and operators work on the business, not just in it. Member meetings, a deep resource library, and courses through IT Service Provider University. Everything you need to run the practice you actually want.

[00:13:42] Join us at smallbizthoughts.org. Interested in advertising? Head to mspradio.com slash engage. The Business of Tech is written and produced by me, Dave Sobel, under ethics guidelines posted at businessof.tech. Thanks for listening. I'll see you on the next episode. Part of the MSP Radio Network.