You shipped AI 6–18 months ago and bundled it into the existing subscription. Customers adopted it. ARR didn't move. Investors are now asking for the AI revenue line, and there isn't one beyond "we'll figure it out at Series B."
For Executive teams · $5M–$25M ARR · 11–50 employees
We set our pricing two years ago and haven't really touched it.
Every enterprise deal becomes a pricing negotiation that goes all the way to me.
Our investors keep asking why NRR is only 102% when the product delivers clear ROI.
The product now does the work of several FTEs for your customers. You're still charging per seat. As the AI gets better, customers need fewer seats — your best product work has started shrinking your own revenue.
Customers love the features and revenue hasn't moved. A loved feature given away is a cost centre with a real inference bill attached.
The original price was a guess anchored to competitors and intuition. ACV is now 20–40% below attainable — revenue left on the table every month, with nobody owning the decision to change it.
Pricing for AI was decided by the founding team in a 30-minute meeting. There's no data on what customers would actually pay for separately, so packaging runs on assumption.
You can articulate the product vision but not why this pricing model is the right one, or how it scales with value. At Series B that credibility gap becomes a valuation haircut.
Left unaddressed, a stale model compounds quietly — then all at once:
ARPU sits flat while the product matures — investors read it as weak differentiation and pricing power
Sales over-scopes and over-discounts to justify a price that was never anchored to value
Every non-standard deal escalates to you — the founder becomes the de facto head of pricing
NRR stagnation that is really a pricing problem gets misdiagnosed as a growth problem, and triggers a down-round
The companies winning on AI have moved to charging for the outcome the AI delivers. That is a structural change to the model, and it goes well beyond a price bump.
Identify the one feature or outcome customers value enough to pay for separately, and build the metric to capture it — before the next raise.
Put value-based positioning and a discount-authority framework in place before reactive discounting becomes culture.
A coherent monetisation story that turns NRR and AI revenue into a valuation lever instead of a board-meeting liability.
Engagements run for founders at the same stage — pricing set once, AI shipped, board asking questions.
GivePanel was in a product-market-pricing fit crisis. We ran a full PROFIT+ diagnostic, repackaged around need complexity and tied tiers to funds raised. ARR went from −35% to +106% in twelve months and investor confidence was restored ahead of Series A.
Enterprise customers were paying the same as sole traders. We ran WTP research across three segments and introduced a hybrid base-plus-usage metric. Result: 28% ARPU lift and $4.5M net new ARR from repricing existing customers alone.
Their AI scheduling engine cut customers' overtime ~18% a month, bundled free. We quantified the value, designed an outcome-informed tier priced at under 15% of the saving delivered, and 42% of customers upgraded within 90 days (~$3.2M net new ARR).
Ayon helped with important international strategy and commercialisation research. I'd recommend highly.
12–16 weeks · $5M–$25M ARR · 11–50 employees · NDA on request
Bring your current price card and a quarter of pricing-related sales notes. You'll leave with a clear view of where the AI revenue is leaking and the two or three moves most likely to close the gap fastest.
Not you? Growth view · Product view · Finance view · AI-native view
