Worth is whether AI represents your price, value, and return accurately at the decision point. It is the ANSWER category closest to the sale, and a wrong number or a "too expensive" framing here ends deals that were nearly closed. This playbook is the practical guide to fixing it. For the background, see the Worth pillar guide.
The principle is simple: an engine can only state your price and value as accurately as its sources allow. So improving Worth is the work of giving engines correct pricing and a clear value story to read. This is answer engine optimization (AEO), also called generative engine optimization (GEO), applied to your price and value. One caution before the tactics: keep it accurate. The accuracy cap means an answer built on an inflated value claim is not counted as a win, so the goal is a true, well-supported value story, not spin.
1. Find Out How AI Prices You Today
You cannot fix a number you have not seen. Run price and value prompts about your real offering, logged out, in your target markets, across ChatGPT, Perplexity, and Google's AI answers. For each, record whether the price is accurate, outdated, or invented, whether the framing is fair or "too expensive," and whether any value story is present at all. The pattern is often a single stale number, repeated across engines, that traces back to one outdated source.
2. Make Your Pricing Readable
The most common reason AI misstates your price is that it cannot read your real one. Pricing hidden behind "contact sales," a gated page, or an image the engine cannot parse forces the model to guess or reuse an old figure. Where your model allows, publish clear, current pricing on a public, text-based page. If you genuinely cannot show exact prices, publish the structure, the tiers, the starting point, what drives cost, so the engine has accurate shape to work with instead of a guess.
3. State Value in Concrete Outcomes
A price with no value story always looks like a lot. Give the engine a value story it can repeat: the outcomes you produce, the time or cost you save, the return customers see, stated in concrete terms rather than vague benefits. "Cuts onboarding time from weeks to days" is something an engine can weigh against a price. "Delivers transformational value" is not.
4. Provide Real ROI and Cost Context
When customers ask "what's the ROI" or "is it worth it," the engine answers best when credible context exists. Publish honest ROI framing, total-cost-of-ownership comparisons, and value-per-tier explanations that a model can cite. Third-party evidence, real reviews that mention value for money, analyst notes, case results, carries more weight than your own claim and is harder to dismiss as marketing.
5. Correct the Stale and Hostile Sources
When an audit ties a wrong price or an "overpriced" framing to a specific source, an outdated comparison, an old pricing page, a competitor's takedown, fix what you can at the source and publish a clear, current counter-source where you cannot. Engines update as their sources do, so correcting the source is what eventually corrects the answer.
6. Re-Measure After Pricing Changes
Pricing moves, and AI lags. Every time you change tiers, run a promotion, or adjust packaging, the old numbers linger in the sources for a while. Re-run your price and value prompts after each change and on a regular cadence, so you catch stale figures before they cost you deals.
Where the Work Gets Hard
Any single price is easy to correct. The difficulty is finding every wrong number and unfair framing, across every engine and market, tracing each to its source, and re-checking after each pricing change. A few spot checks barely scratch it, because answers vary from ask to ask and a stale price hides in specific phrasings. Running the full set properly is a real undertaking, which is the honest reason most teams have it measured for them even when they fix the content themselves.
A HiBot audit gives you the baseline and the ranked list of pricing errors, so your effort goes where it moves the score the most. You can pressure-test how AI reads your pricing with The Pricing-Page Readiness Checklist for AI, and see the full arc in Correcting a Costly Price Error. Download the ANSWER whitepaper to see the method, or request an AI visibility audit at hibot.com.
