The Margin You Already Earned

CEO Perspective · Automotive

Before we launch the next cost program, there is a more basic question worth asking: are we actually getting the value we already negotiated?

I have spent a lot of time with large enterprises over the years, and one thing has always struck me: companies work incredibly hard to negotiate value. Procurement teams negotiate savings. Commercial teams negotiate price protection. Finance builds the economics into the plan. Then the contract is signed, everyone moves on, and we assume the value will show up.

That matters even more in automotive today. BCG’s 2026 Global Automotive Supplier Study describes an industry dealing with persistent cost pressure, tariffs, higher financing costs, labor constraints and volatile demand. Suppliers are being asked to rebuild their earnings base, not just manage through another temporary disruption — and the same is true for OEMs.

I agree with that. But before we go looking for the next hundred million dollars of savings, I think there is a simpler place to start.

How much of the value we have already negotiated is actually reaching the P&L?

The gap between negotiated value and realized value

This is not a theoretical issue. It shows up in very ordinary ways.

A rebate was negotiated, but nobody claimed it. A supplier invoice still reflects the old price. Volumes crossed a threshold, but the price tier never changed. A tariff-sharing clause should have kicked in, but it wasn’t applied. A credit was due and was missed. On the sell side, a cost escalator was agreed, but the customer was never billed for it.

Each one looks small when it lands on someone’s desk. Across thousands of suppliers, customers, contracts and transactions, it is not small.

The bigger issue is that most companies don’t have a reliable way to know the total gap. Procurement may know what it negotiated. Finance knows what hit the P&L. AP knows what was paid. The contract sits somewhere else. Very few organizations continuously connect all of those things.

This becomes harder when the business moves faster than the contract

Automotive is a good example because the underlying assumptions move constantly. BCG points to significant volatility in BEV programs. Actual volumes can look very different from what was assumed when a program was priced.

When volume changes, the economics around it can change too: price tiers, tooling amortization, minimum commitments, input-cost formulas, tariff sharing and other commercial terms.

The contract may be perfectly clear. The problem is that nobody is sitting there every day comparing what the contract says with what is actually happening across millions of transactions.

And frankly, people shouldn’t have to.

We have plenty of systems. That is not the problem.

Most large companies already have the data. They have ERP, procurement, CLM, CRM, AP, billing and analytics systems. Many are now adding copilots and AI tools on top.

All of those systems have a role. But they were built to do different jobs. The ERP records the transaction. CLM manages the contract. Procurement manages the supplier process. Analytics tells us what happened.

What is still missing is the connection between the commercial promise and the financial outcome.

For me, that is where AI becomes interesting. Not because it can summarize another contract or create another dashboard. Those are useful capabilities, but they don’t change the P&L by themselves.

AI should do the work between insight and outcome

I don’t believe the answer is to ask procurement or finance teams to manually reconcile more transactions. At enterprise scale, that simply doesn’t work.

AI can do something humans cannot reasonably do: continuously compare contracts with POs, invoices and transactions; understand when the economics have changed; quantify the impact; and put the issue in front of the right person while there is still time to act.

In some cases, the action will still require judgment or a conversation with a supplier or customer. It should. In other cases, once the rules and authority are clear, the system should be able to take the next step.

That is an important distinction. I don’t think the next phase of enterprise AI is about removing people from decisions. It is about removing the enormous amount of manual work required to discover that a decision needs to be made in the first place.

The next margin pool may already be there

When margins tighten, the natural response is to go find new savings: another sourcing event, another supplier negotiation, another restructuring program.

Those things will continue. But I think executives should also ask whether part of the next margin opportunity has already been negotiated and is simply not being realized.

If that is true, it changes the conversation. The question is no longer only, “How much did procurement save?” It becomes, “How much of that saving actually reached the P&L?”

And once we can answer that consistently, there is an even better question: why should there be a gap at all?

Rivvun — System of Outcomes

This is the problem Rivvun was built to solve.

I don’t think enterprises need another system that simply records what happened, or another AI layer that only tells them what they should do. They need a system that stays with the problem until the economic outcome is realized. That is what we mean by a System of Outcomes.

Rivvun continuously connects the terms companies negotiate with the transactions that follow. When the two diverge, the objective is straightforward: find the gap, recover the value, prevent it from recurring and do it at enterprise scale.

Ultimately, I think this is how enterprise AI will be judged. Not by how many answers it produces or how many pilots a company runs, but by whether it creates a measurable business outcome.