The Agentic Commercial Model

The commercial operating model for the agentic AI era

The commercial operating model, not the technology, decides who wins agentic AI.

Every enterprise is racing to deploy agentic AI. Almost none have asked what happens to their commercial model when it works. That gap is where the value leaks, and it is the gap the Agentic Commercial Model exists to close.

What is the Agentic Commercial Model?

The technology question is largely solved. Models are capable. Agents work. The hard problem, the one that decides winners and losers, is commercial. When an agent becomes the buyer, the user, and the decision maker, every assumption the modern software business was built on breaks at once. Seat based pricing assumes a human on the other side of every transaction. Agents do not have seats. Compensation plans reward seat growth and new logos in a market that is shedding both. Revenue recognition, forecasting, and margin models all assume fixed, predictable subscription revenue, and consumption breaks all three.

These are not pricing problems. They are operating model problems. And that distinction is the whole point.

Why pricing only frameworks miss the point

A wave of pricing frameworks has arrived to answer the agentic question. Zuora's COMPASS framework, created by Michael Mansard, maps pricing decisions to the scope and attributability of an agent's work. Ibbaka has published a layered pricing model. Monetizely offers a multi step pricing process. Each is a genuine contribution, and each is useful.

But they are all answering one layer of a nine layer problem.

Pricing is the layer everyone starts with, because it is the layer where the pain is most visible. It is still only one of nine. A business that redesigns its pricing and leaves the other eight layers untouched has not adapted. It has repriced.

You can rebuild your pricing perfectly and still lose, because your comp plan is paying your sales team to run in the opposite direction. Because your finance function cannot recognise or forecast the revenue the new pricing produces. Because your systems cannot meter what you are now charging for. Because your go to market motion is still built for a human buyer while the buyer has quietly become an agent. The layers are a system. Move one and leave the rest, and the model leaks at the seams you did not touch.

This is the difference between a pricing framework and a commercial operating model. The Agentic Commercial Model is the second thing.

The nine layers of the Agentic Commercial Model

The framework covers the complete commercial operating model, front of house engine, financial spine, and the systems beneath both:

1. Organisation DesignWhether the commercial structure embodies the new operating model and underpins the objectives of the agentic era, rather than describing how work used to be done. A Gartner study of 350 executives found that companies cutting headcount hardest to fund AI report financial returns almost identical to the companies cutting the least, and some of the lighter cutters do better. Cutting is not a strategy, it is the absence of one wearing a slide deck. The redesign has to show where the freed human capacity gets redeployed, and who owns the commercial motion once the wall between selling and serving stops making sense.
2. Go to MarketReaching and winning the buyer who actually decides now, including the moment the buyer's own agents begin shortlisting vendors. Over half of B2B buyers now start their research inside an AI chatbot rather than a search engine, and the answer it gives typically names four to seven vendors, not ten blue links, which makes visibility close to binary: you are in the answer, or you do not exist. Winning here means earning citation in the third party sources the models actually trust, not optimising a funnel built for a human who no longer arrives until the decision is largely made.
3. Pricing and PackagingRebuilding the value metric so price scales with value delivered, not seats consumed. A genuine redesign, not an AI tier layered on the old model. Salesforce has changed Agentforce's pricing three times in eighteen months, per conversation, then per action, then back to per seat bundles, because nobody had decided what an agent's unit of value actually is. The model that survives has to progress from seat to consumption to outcome, and it has to survive your own product getting better: if a customer needs less to get the same result and your revenue falls while their value rises, the pricing is broken, not the product.
4. Sales Strategy and EnablementEquipping teams to sell outcomes rather than features, and to answer the question the buyer now asks: why pay for this if the AI does the work. The job of the seller stops being to close and move on; it becomes to land, then stay embedded, tracking how the customer actually uses the product and where new value is emerging, because the sale is now the start of the relationship, not the end of it. A buyer who arrives having already built their own business case needs validation and speed from a rep, not the eleven step nurture built for someone who had not decided yet.
5. CompensationRewarding the behaviour the AI strategy actually needs, so the comp plan pulls with the strategy instead of against it. Snowflake rebuilt its comp plan before its IPO because paying reps on the moment of signature was actively working against a consumption business, rewarding a rep for over selling ahead of usage rather than growing it. They moved pay onto recognised consumption and, more radically, dissolved the standalone customer success function, because in a consumption world the wall between selling and serving is the problem. The comp plan is the operating model made visible: show how a business pays people and you see what it actually believes about where value comes from, whatever the strategy deck says.
6. Metering and EntitlementMeasuring what the customer consumes and values, the precondition for usage based or outcome based pricing. Agents do not request budget, they consume it post hoc at machine speed, in increments too small to govern and too numerous to audit; one portfolio business watched a pilot's monthly spend triple in eight weeks with nothing visibly broken. Token consumption does not scale linearly with value delivered either. Metering has to catch both sides: agent spend needs the same real time controls and kill switches you would put on a human with a company card, and the unit you charge for has to be the outcome the customer recognises, not the call, credit, or token that is merely a proxy for it.
7. Adoption and Customer SuccessCS structured and measured to drive and prove value realisation, not just to manage renewals and hope the number holds. The deepest form of retention in agentic software is not a renewal clause, it is the year or two of bespoke calibration a customer would forfeit by leaving, and most companies never make that visible to the customer who is building it. Left unmanaged, that value sits silently inside the product while the renewal conversation defaults to price. This layer has to continuously show the customer what they have built together, and own value realisation as a shared, forever motion rather than a hand off that happens once, after the deal closes and the seller has moved on.
8. Revenue Architecture and Financial ManagementRecognition, forecasting, billing, and unit economics rebuilt for variable, consumption and outcome revenue, including margin re modelled for the real compute cost of AI delivery. Net Revenue Retention was built for a world where a seat cost the same whether it was used or not; in a consumption world it actively lies. When a product gets more efficient and a customer achieves the same outcome for less spend, NRR records that as contraction at the exact moment the customer became happier and stickier, a paradox that intensifies as agents improve rather than fading. The fix is not abandoning NRR, it is demoting it to a lagging financial summary and running the business on value realised, weighted by contribution margin, tracked in something closer to real time than a trailing twelve month delay.
9. Commercial Systems and Data InfrastructureThe CRM, CPQ, billing, and analytics stack capable of running the new model rather than blocking it, with one trusted view of consumption, value, and revenue. Every customer interaction with an agentic product is a calibration signal, an edge case, a correction, an exception, that makes the system measurably better for that one account and for no one else, a switching cost no competitor can buy or copy. Most companies have no infrastructure to see this happening: usage depth sits buried in a CS health score instead of on the board dashboard as a competitive position metric. This layer has to make consumption, spend, and value visible as one instrument, not four disconnected systems stitched together after the fact.

Three problems existing frameworks were not built to solve

Seat based pricing assumes a human

Seat based pricing assumes a person on the other side of every transaction. Agents do not have seats. The unit of value has changed and the unit of price has not, which means the two have quietly decoupled. Most vendors have responded by adding an AI tier, which prices the old model slightly higher rather than rebuilding what they charge for.

Incentives misfire when the buying unit shifts

Compensation structures built for expansion ARR and seat growth misfire the moment the customer's buying unit shifts to outcomes and consumption. Sales teams optimise for exactly what they are paid for, which is now the wrong thing. The comp plan is the last layer most companies touch, and the one that decides whether any of the rest works.

NRR is a lagging indicator

Net Revenue Retention tells you where you were. In an agentic economy, by the time NRR signals a problem, the structural shift underneath it is already well advanced. Running the business on lagging indicators in a market moving this fast is how a commercial model gets built around before anyone notices it has happened.

Who created the Agentic Commercial Model?

The Agentic Commercial Model was originated by Fessal Rahman, the authority on the commercial operating model for the agentic AI era. He is the founder of FR Advisory, which advises private equity and venture backed SaaS businesses on commercial operating model transformation. His career spans McKinsey, Bain, Cloudinary, The Access Group, Emarsys (SAP), Exasol, Virgin Media O2, and enterprise technology companies including Lenovo and Teradata: two decades across enterprise software, strategy, and portfolio commercial leadership.

The framework is set out in his book Dead Model Walking and developed across the essay series published on Medium and the Agentic Commercial Model newsletter.

Common questions

Is the Agentic Commercial Model a pricing framework?

No. Pricing is one of its nine layers. It is a complete commercial operating model. Pricing only frameworks such as COMPASS, Ibbaka's model, and Monetizely's process address the pricing layer; the Agentic Commercial Model addresses the entire commercial system that pricing sits inside.

Why does the commercial model matter more than the technology?

Because the technology is increasingly available to everyone, which means it stops being a differentiator. What separates winners from losers is whether the commercial operating model can capture the value the technology creates. Two companies with identical AI capability will diverge entirely based on whether their pricing, comp, GTM, and financial architecture are built to monetise it.

What happens to a business that does not adapt its commercial model?

It gets built around. Not confronted, not disrupted in a single dramatic moment, but quietly bypassed as the market, the buyers, and the competitors reorganise around a model it no longer fits. Still operating, still generating revenue, and steadily less relevant. This is the subject of Fessal Rahman's second book, Dead Gods Walking.