Founder guide · Updated August 7, 2026
How to make your business easier for AI to recommend.
AI assistants can answer a how-to question themselves, but they may recommend providers when a buyer asks who to hire, compare, or trust. A business earns that position by being discoverable, clear, credible, and supported by evidence across the public web.
1. Define the buyer decision
Start with the questions a serious customer would ask before choosing a provider. Include category, customer type, market, constraints, and tradeoffs. “Best company” is less useful than “Which provider is the strongest fit for this specific situation?”
2. Make category fit unmistakable
Your homepage and service pages should state what you provide, who it is for, where it applies, what it costs or requires, and why the business is qualified. AI systems should not have to infer the basic offer from slogans.
3. Publish verifiable proof
Specific customer outcomes, operating standards, comparison criteria, transparent boundaries, named expertise, and current factual information are easier to verify than claims such as “best” or “leading.” Third-party profiles, reviews, partner pages, and credible mentions can corroborate what the company says about itself.
4. Make the website crawlable and understandable
Use indexable pages, accurate titles and descriptions, semantic headings, accessible controls, canonical URLs, a sitemap, and structured data that matches visible content. Allow the search crawlers needed for the platforms you want to appear in. Machine-readable summaries can help agents understand pricing and key pages, but they do not substitute for useful public content.
5. Measure before and after
Freeze a realistic set of buyer questions before making changes. Record when the business appears, whether it is recommended, who wins instead, what sources are cited, and whether the facts are correct. Publish a small intervention set, then repeat the same questions. Changing the test to create a better score destroys the evidence.
AI discoverability, recommendation optimization, and agent readiness
Discoverability asks whether an AI system can find the business. Recommendation optimization asks whether the system selects it in a buyer decision and why. Agent readiness asks whether an AI agent can reliably understand and interact with the website. A strong program addresses all three, but they are not the same claim.
When to hire a specialist
A company can perform basic checks manually. Outside help becomes valuable when the business needs a defensible benchmark, multi-platform evidence, competitor/source analysis, implementation support, and repeated measurement without changing the methodology. See the Good Productivity AI recommendation optimization service.
How long improvement takes
A baseline can usually be collected within days. The time required for a published change to influence AI-assisted answers is not fixed because crawling, indexing, retrieval, source availability, model behavior, and competition all vary. Good Productivity reports time to first observed movement and durability across reruns instead of promising a universal deadline.