Agent-Native Revenue

A revenue system designed from first principles around the respective strengths of humans and AI agents.

AI creates unprecedented capabilities to understand customer needs and market dynamics, then deliver recurring impact quickly and effectively.

Leaders using AI to amplify yesterday's GTM motions lack the speed, depth, and clarity of those building today's agent-native GTM.¹ That's already playing out in revenue results.²

An agent-native revenue system reimagines recurring customer impact by amplifying where humans and agents individually shine,³ with clear accountability, governance, and learning loops that systematically improve revenue growth. I develop these patterns in my own work and share what holds up here, for GTM leaders redesigning the system and revenue professionals building the craft to work inside it.

Note: My area of expertise is B2B SaaS sales, though in my experience the lessons being learned at the frontier apply to any business that transacts online.

For GTM leaders: Redesign the revenue system, not just the tooling.
For revenue professionals: Master the craft of agentic revenue work.

Principles

Revenue leaders are wise to articulate the first principles that define how they run their shops.

Here are the principles that inform everything my team, and my agents, do. These principles also define how we measure excellence.

Always add value
Respect your recipient - if you're not helping them learn about or solve a problem they are experiencing, don't waste their time. Never spam, never slop, always specific.
Understand deeply, empathize fully
Approach your customers with unreasonable care and know their world better than anyone but them.
Accountability stays human
Agents execute and advise. A named person owns the outcome and the relationship.
Every loop learns
A revenue system that cannot tell you what it learned last week is a pipeline of tasks.

Explore an old map with new eyes

The buyer's journey hasn't changed - folks still need to go from awareness through consideration to decision and implementation. How the buying committee navigates that journey, and their expectations of the seller, are radically changing because of AI.

My favorite framework to illustrate a revenue system is Jacco van der Kooij's bowtie from Revenue Architecture. The rest of this page walks through the functions I employ at each phase of the bowtie.

Note: Product-led growth (PLG) and sales-led growth motions have significant distinctions. Some companies do one vs. the other, and many companies run both. Where the difference matters, I'll call it out.

Attract

Lead gen

Any list you can describe, an agent builds overnight. So can your competitor's. The scarce thing is deciding who deserves a person.

Nurture

A drip sequence is a confession: we could not afford to pay attention to each person, so we wrote one thing to everyone.

Convert

Pipeline

Agents keep the CRM honest from the calls and the threads. The rep's job goes back to judgment about the deal.

Pilots and demos

A tailored demo cost a week of engineering, so most prospects got the generic one. At an afternoon, every serious prospect gets their own.

Close and deal desk

Security reviews and redlines are pattern matching over documents you have already answered. Let the agent draft. Keep the exceptions.

Deliver

Onboarding

Time to first value is the only onboarding metric that matters, and most of the gap is retrieval, not teaching.

Grow

Retention

The churn signals were always in the data. Nobody had the hours to read all of it. Now something does.

Expansion

Expansion is a research problem wearing a sales costume. The account already told you what it needs. The question is who read it.

Operate

RevOps

Revenue operations lived in a ticket queue because analysis was scarce. Dissolve the queue and the real bottleneck shows: nobody defined the question.

Finance

Quote to cash is rules with judgment at the seams. Automate the rules, gate the seams, and the close gets shorter.

Go a layer deeper

EssayGTM in FluxWhat AI changed about trust in B2B go-to-market, and what it didn't. The essay the map grew out of.

If you'd rather redesign the system with someone who runs one, that's what I do with executive teams.

Tell me what's stuck →

Sources
1.McKinsey, The state of AI in 2026: On the road to ROI (August 2026) The 6% of organizations seeing significant EBIT impact from AI "fundamentally redesign workflows that are enabled by AI rather than insert AI into existing ones." Nearly three-quarters of them report doing so, against one-quarter of everyone else. 1,719 respondents, May to June 2026.
2.McKinsey, The state of AI in 2026, Exhibit 7 Revenue gains from AI are "most often attributed to the use of AI in marketing and sales, followed by product and service development and software engineering." Exhibit 7 shows more respondents reporting a revenue increase in marketing and sales over the past twelve months than in any other function; the report does not print a single total for that function.
3.Kim and Koning, AI-Native Firms, Harvard Business School Working Paper 26-090 (June 2026) Across Y Combinator batches W20 to F24 and US venture-backed startups first financed 2020 to 2024, AI-native firms are 25% smaller than non-AI peers in the same industry and cohort, with flatter hierarchies and comparable valuations. Embedding AI into what the firm does, "beyond layering on AI tools into existing workflows," is how they scale knowledge work without large teams.

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