Sam Brown · Prometheus Prometheus

Fractional AI and RevOps Leadership

Fractional AI-driven revenue optimization leadership for companies that already have a business.

I sit with your leadership team and run AI and machine learning programs aimed at concrete results: strategy, sequencing, decision rights, and the hard cuts. My work is done when the systems are live in product, go to market, sales, operations, and financial planning and forecasting, measured, owned by your people, and tied to revenue.

Not a lab. Not a token budget. Not a vendor that delivers a deck and leaves.

Fractional Chief AI Officer / Fractional AI Executive

For companies that want AI in the workflow, not on a slide (or even worse, just an invoice).

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Audience

Who this is for

Companies with a product, revenue, and real operations, typically SaaS and mid-market commercial businesses, that want to accelerate and optimize what already works.

If you need someone to “explore generative AI,” stand up a research group, or add another SaaS line item, this is the wrong conversation.

If you need a seasoned operator at the leadership table who will own the strategy, align the teams, and take the IC work off your plate while managing the people who do it, that is the engagement.

The engagement

Fractional AI leadership, not a vendor wrapper.

I come in as a fractional executive. I run the program with the real team. I do not sit outside the company as a faceless vendor.

What that means in practice:

  • Once leadership has set objectives, I own AI strategy and the operating cadence with product, engineering, revenue, and finance.
  • I design the business-process changes, scoring, routing, assistive selling, retention, forecast, not just the models underneath them.
  • I sequence the work so the first systems that go live move pipeline quality, mid-market throughput, retention, or forecast accuracy.
  • I manage the people doing the build. Your team ships. I am not the hands-on-keyboard, and when our objectives are complete your team will be fully capable of maintaining and improving whatever systems we need to build.
  • I stay until the output is in the workflow, instrumented, and reproducible without me.

The job is live systems and commercial results. A roadmap you already knew is not the deliverable.

Scope

What I do

  • Integration leadership

    I sit with product, engineering, and revenue leaders and run the program: scope, sequencing, decision rights, and the cuts that keep the work honest. The output is operating systems, not a strategy memo.

  • Business process design, then models

    I do not start with a model. I start with the motion, demand, qualification, selling, expansion, retention, planning, and design the process change first. Models, evals, fallbacks, and human review are what make that process hold up in production.

  • Commercial systems

    Scoring, routing, assistive workflows, and instrumentation wired into acquisition, selling, and retention. Built so the team you already have can close more and keep more, without a headcount spike.

  • Translation for the people who fund the work

    Technical machine learning and artificial intelligence only matter if the board and the investors can see the economics. I turn the work into the language of pipeline quality, sales lift, gross revenue retention, and forecast variance, including the slides a CEO can take to the board or to a VC/PE partner.

Outcomes

Outcomes I target

Illustrative capability ranges for the commercial results this work is built to pursue, not audited client case studies or named references. The point of the work is pipeline quality, mid-market throughput, revenue that stays on the books, and forecast accuracy.

  1. Raise MQL / SQL quality

    Demand and qualification systems that lift the quality and volume of pipeline, not vanity traffic dressed up as interest.

  2. Mid-market sales lift with the existing team

    Assistive selling and better routing so the same people close more, without a headcount spike. Illustrative range: about 30-50% lift when the motion fits. When we have done this before, no business has reduced SDR head count; they end up adding people because of the revenue uplift.

  3. Gross revenue retention

    Retention and expansion systems that keep revenue. Illustrative target: gross revenue retention into the mid-nineties (~95%). Pre-empting churn the computer can find lets the best people focus on relationships with the largest and most important accounts.

  4. Accuracy in FP&A

    In businesses where sales look unpredictable, ML forecasting can help build budgets and, in strong fits, keep actuals within an illustrative 1-5% of forecast.

These are RevOps and commercial-operating problems. The models are the mechanism. The product is the business process.

How I work

Diagnose. Integrate. Hand off.

  1. Where it is stuck

    A short working session on the product, the data, and the commercial motion. I say no to projects when a slide deck would be more honest than an engagement.

  2. Build it into the company

    Models, workflows, and instrumentation land in the systems your people already use. I do the integration work with your team, not around them. I run the program; they own the build.

  3. You own it

    Runbooks, evals, and a clear operating cadence. When I leave, the system is yours, including the numbers it is supposed to move.

About

Meet your fractional AI executive.

For more than 13 years I have been at the technical forefront of machine learning and AI. The work now is not writing the code. It is the economics of AI: where it belongs in the business, what it is allowed to change, and how you prove it paid for itself.

I partner with SaaS and operating companies as a fractional leader, typically as a Fractional Chief AI Officer or Fractional AI Executive, to put AI and machine learning into product, go-to-market, and revenue operations. I bridge technical engineering and commercial outcomes: building the teams, the decision rights, and the systems that move sales lift, pipeline quality, and gross revenue retention.

Prometheus is the practice. The hire is me. Companies bringing on fractional leadership want a person at the table, not a brand on an invoice.

For leadership

For CEOs, boards, and investors

AI work dies in two places: in engineering, where it never becomes a process, and in the boardroom, where no one can explain the economics.

A large part of this role is translation. I take deep technical ML work and put it in the terms venture and private-equity firms actually underwrite: contribution to pipeline, throughput of the existing sales team, revenue that stays, and forecast you can plan against.

When it is useful, I produce the high-level explanation, a short set of slides a CEO can walk a board or an investor through, covering what changed in the motion, what was instrumented, and what the commercial ranges are. That is part of the job, not an add-on.

If your investors are asking what AI is doing to the operating model, this is the conversation.

Fit

Who should not write

  • Teams looking for a staff ML engineer or a prompt vendor.
  • Companies whose real problem is “we should be doing something with AI” through SaaS.
  • Anyone who wants a lab, a demo day, or a larger inference bill.

I reply to serious inquiries.

Contact

Start a conversation

Tell me what you are trying to automate or optimize, where it is stuck, and whether you are hiring a fractional leader or looking for technical advice on a single project.

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