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AI in a SaaS business.In the product, in the team and in the unit economics.

For a software company AI is two questions at once. What should it do inside the product your customers pay for, and what should it change about how your own team builds and runs that product?

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If you lead a SaaS business, you may have said one of these.

  • “Every competitor has announced an AI feature. We need a position, not a press release.”
  • “We shipped an assistant. Usage is fine. Revenue hasn't moved.”
  • “Engineering is faster with AI tools. The roadmap isn't.”
  • “Should this be a paid add-on, a new tier, or just the product?”

Two questions, usually asked separately

Most SaaS companies treat AI in the product and AI in the organisation as different programmes with different owners. The product team ships an assistant. Engineering adopts coding tools. Customer success trials a support agent.

They are the same decision seen from different sides. What you can offer customers depends on how fast and how safely your team can build. How your team should work depends on what the product is becoming. We look at both together, starting from the economics: what customers will pay for, what it costs to serve, and what has to change in the organisation to deliver it.

The decisions that matter here.

  1. 01

    What AI is for in the product

    A feature that customers try is different from one they pay for. Decide which customer problem AI solves, and how that shows up in revenue, retention or cost to serve.

  2. 02

    How it is priced and packaged

    Add-on, tier or included. The choice changes the economics, so it belongs in the value case and not at the end of the build.

  3. 03

    How the product team works now

    Discovery, design and delivery change when AI is part of the toolkit. Decide deliberately what the new lifecycle looks like, and who owns it.

  4. 04

    Where people stay in the loop

    Your customers will ask what the product does on its own and what a person reviews. Design the answer in advance.

What tends to go wrong.

  • A feature in search of a business case

    The AI feature ships to keep pace with competitors, with no agreed measure of what it should earn or save.

  • Faster output, same outcomes

    Individual engineers and designers get quicker, but the way work moves through the organisation is unchanged, so customers see little difference.

  • Cost to serve nobody modelled

    Usage-based AI costs sit inside a subscription price. If margin was not part of the design, growth can make it worse.

How the four stages apply to a SaaS business.

  1. Stage 01

    Decide

    Choose the few AI bets, in the product and in the organisation, that could change revenue, retention or speed, each with a value case.

  2. Stage 02

    Design

    Rework the proposition, the product lifecycle, and the roles and decision rights around it.

  3. Stage 03

    Deliver

    Specify pilots and acceptance criteria, work alongside product and engineering on first releases, and judge them against agreed measures.

  4. Stage 04

    Embed

    Name owners, train teams on the new way of working, and track the result until it runs without us.

How the four stages work

Where this comes from.

$75,000
monthly revenue within six months from a machine-learning and AI subscription data product used by more than 2,000 businessesFresha, before Neurally
50%
reduction in discovery-to-development time reported for an AI-redesigned product lifecycleCompare the Market, before Neurally
97%
retention on a payroll product that reached 1,900 businesses and more than $52,000 in monthly revenueFresha, before Neurally

These are outcomes from roles Stuart held before founding Neurally. They are not Neurally client results, and the wider commercial figures reflect the work of whole teams, not AI alone.

Start with the product area or team where the question is sharpest.

The Diagnostic spends a week on one or two priority areas, for example an AI product line or the product-development lifecycle. You get prioritised opportunities, an initial value hypothesis with its assumptions, a readiness, risk and dependency view, and a recommended next step.

Talk it through first, free (opens in a new tab) Full scope on the services page

Engagement 02

AI Advantage Diagnostic

£6,0001 week

  • Prioritised opportunities for the areas in scope
  • An initial value hypothesis, with its assumptions stated
  • A readiness, risk and dependency view
  • A recommended next step

Also worth a look: AI Transformation Deep Dive (£12,500, 2 weeks).

Questions we hear.From this sector.

Do you help with AI product strategy or only internal use of AI?

Both. For a software business they are linked. We work on what AI should do in the product and what it earns, and on how the product organisation itself should work now that AI is part of it.

Have you built AI products yourself?

Stuart has, in previous roles. At Fresha he led a machine-learning and AI subscription data product that reached more than 2,000 businesses and $75,000 in monthly revenue within six months. At Compare the Market he launched the company's first consumer AI assistant. Those are career results from before Neurally.

Can you act as an interim or fractional product leader?

Yes, through Transformation Leadership. It is for a fixed term, typically three to six months, and a fixed outcome. It is not open-ended cover.

Do you write code or supply engineers?

Your teams and partners write the code, and we stay with them through delivery. We specify pilots, help choose and brief partners, and work alongside product and engineering on the first releases.

Related reading

Start with a conversation.Thirty minutes, free.

Tell us what you are trying to decide. If we can help, we will say how and what it would cost. If we can't, we will say that too.

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