AI writing benchmarking reports for procurement teams

A marketing director at a mid-size company is renewing their AI writing software contract and suspects they're overpaying. Their vendor has raised prices twice in 18 months. The marketing director has no structured way to evaluate whether what they're paying is in line with what comparable teams at comparable companies pay for comparable output quality — so they either renew out of inertia or spend weeks manually trialing competitors. That's the moment this matters.

This gap exists because the people who feel the pricing pain most acutely — end users — are not the same people who control the buying decision. Procurement and finance teams sign the contracts but have no domain expertise to evaluate whether 'pricing is higher than market standards' (as users consistently report) is actually true in their specific use case and volume tier. Vendors exploit this asymmetry deliberately: pricing pages obscure true per-unit costs behind word credits, seat counts, and feature tier bundling.

The complaints about high pricing are consistent across products — 'the price is a bit high compared to some of the other providers' and 'its a little costly for its offerings compared to competitors' — but they're coming from users who evaluated the tools themselves, not from procurement teams who have apples-to-apples cost data. That data doesn't exist in structured form anywhere. There's no independent source that tells a procurement team: for a team of 8 content writers producing 60 articles per month with SEO optimization, here's what three leading tools actually cost per article delivered, factoring in realistic overage and feature usage.

This recurs because software contracts renew annually, pricing changes constantly, and teams' content volume grows over time — meaning the analysis that was valid at contract signing is stale within 12 months.

What to build

Build a benchmarking report service where a buyer submits their team size, monthly content volume, and current tool spend, and receives a structured cost-per-output comparison across major AI writing tools at their specific usage tier — including realistic overage estimates based on typical usage patterns for that team profile.

Where to start

Start by publishing a free annual benchmark report for the SaaS marketing vertical specifically, building SEO traction and inbound from procurement teams actively searching for this comparison — then offer custom analysis as a paid service for teams outside the standard profile.

The hard part

Getting accurate, current pricing data across vendors at different volume tiers requires either direct vendor relationships or continuous manual research — and vendors actively obscure enterprise pricing, so the benchmark is only as reliable as the freshness of your underlying data.

How it makes money

Charge a flat fee per custom benchmarking report ($500-1500 depending on complexity), with an optional ongoing monitoring subscription ($200/month) that alerts the buyer when their tool's pricing tier changes or a competitor drops below their current per-unit cost.