Research and writing on the commercial operating model for the agentic AI era
Seat pricing plus an AI tier isn't a strategy. It's a value creation risk sitting in your numbers. This is the framework, the evidence, and the nine layers that have to change.
"Every enterprise is racing to deploy AI. Almost none have asked what happens to their commercial model when it works."
From Dead Model WalkingThe commercial operating model, not the technology, decides who wins agentic AI.
Flagship Report · First Edition
34 vendors audited for real agent access, mapped to the nine layers. 53% ship an MCP server: the two layers that decide price and pay have zero.
The Framework
A structured commercial operating model for the agentic AI era: nine layers a software business must rebuild when its customers, its users, and its competitors are deploying AI agents at scale. Move one layer and leave the rest untouched, and the model leaks at the seams you didn't touch.
Whether the commercial structure embodies the new operating model, rather than describing how work used to be done.
Reaching and winning the buyer who actually decides now: including a buyer's own agents shortlisting vendors.
Rebuilding the value metric so price scales with value delivered, not seats consumed.
Equipping teams to sell outcomes rather than features, and answer why pay for this if the AI does the work.
Rewarding the behaviour the AI strategy actually needs, so the comp plan pulls with strategy instead of against it.
Measuring what the customer consumes and values: the precondition for usage or outcome based pricing.
CS structured and measured to drive and prove value realisation, not just manage renewals.
Recognition, forecasting, billing and unit economics rebuilt for variable, consumption and outcome revenue.
The CRM, CPQ, billing and analytics stack capable of running the new model, not blocking it.
Built By an Operator
Fessal Rahman is the originator of the Agentic Commercial Model and author of Dead Model Walking and Dead Gods Walking. His career spans McKinsey, Bain, Cloudinary, The Access Group, Emarsys (SAP), Exasol, and enterprise technology companies including Lenovo and Teradata.
Why agentic AI breaks the business of software, and the model that wins.
How agentic AI builds around business leaders who won't adapt.
Twenty years of operating record
$50m to $105m
Usage-priced developer platform
Doubled in 2.5 years. NRR up 15%, win rates up 10%, unit economics up 25%. Consumption model rebuilt, growth pricing built into every contract.
121% NRR
Marketing automation SaaS
NRR from the low 100s to 121%. New business ACV up 25% through a new sales methodology and packaging.
90% converted
Enterprise data platform
90% of the installed base moved from perpetual to subscription inside four years, with new comp design and territory management. Growth returned.
20 companies
PE-backed software group
Commercial function across 20 businesses, around £1bn combined. NRR up 4%, GRR up 2%, forecast accuracy improved.
As heard on Building Great Tech: How to Price Your SaaS in the Age of AI →
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