Make the offer worth understanding
Make the offer, fit, evidence, and next step clear and accessible, while recognizing that each AI system controls its own selection. I would begin with the offer, not with a promise to influence an outside system. Who is it for? What changes the answer? What supports the claim? Clear answers give both people and compatible systems something more useful to work with.
“Being findable is about visibility. Being chosen is about suitability.”
— Curtiss Witt, The Decision Economy, Chapter 3.
Which business facts should I clarify first?
An AI-assisted customer needs to understand what the business actually provides. Write the offer in a sentence that identifies the intended customer and the problem addressed. If the sentence could describe almost any business in the category, clarify the scope before adding promotional claims.
Audience information helps establish relevance. State the circumstances in which the service is appropriate and the important limits. Do not make the audience so broad that the description stops helping anyone decide whether the offer fits the particular situation they are trying to resolve.
Conditions such as scope, location, availability, or requirements can change suitability. Identify the conditions that actually apply to your offer. A clear conditional answer is more useful than a broad claim followed by hidden exceptions that only become apparent after the customer makes contact.
Give the organization, service, tool, and author stable names and explain their relationships. Avoid switching labels so often that a reader must infer whether two names refer to the same thing. Consistency is an editorial responsibility, not a guarantee that any system will interpret the page correctly.
Give important offer information a clear home on the website and link to it from related material. When a fact changes, update that source and review dependent pages. This reduces the risk that different descriptions continue to circulate without anyone knowing which one is intended to be current.
The comparison below describes two approaches to the work. It is a practical design contrast, not a measured claim that one approach always produces a better commercial outcome.
| Information and activity approach | Decision-support approach |
|---|---|
| Repeat broad positioning everywhere. | Maintain a precise offer and its conditions. |
| Keep proof on a distant praise page. | Attach support to the claim it addresses. |
| Promise recommendation placement. | Improve controllable facts and report bounded observations. |
How can I make important claims checkable?
A specific claim is easier to support than a broad adjective. Replace vague assertions with an explanation of what is provided, what it applies to, and what the customer should expect. Do not strengthen the wording beyond the evidence merely to make the business sound more distinctive.
Attach supporting evidence where the reader encounters the consequential statement. Explain the evidence’s scope when it could be misunderstood. A source about one service, time period, or population should not be used to imply an unrestricted conclusion about everything the business offers.
Important terms should be easy to locate before a customer takes a step that depends on them. If something requires confirmation, say so. A statement of current terms and a statement of guaranteed future availability are different promises, and the page should not blur that difference.
The owner can accurately describe what the business reports without implying an independent audit. Name the basis of a claim. If it has been checked, describe the check; if it has not, do not use the appearance of a badge, score, or technical interface to suggest otherwise.
A limitation tells the customer when an answer or offer may not apply. Place it near the relevant promise and explain the practical consequence. A generic disclaimer at the bottom of the site should not carry all the work of correcting an overly broad statement at the top.
How do I support the customer’s actual decision?
A request for the “best” provider usually needs context before it can be answered responsibly. Identify the criteria that would change the comparison. Your content can help a customer define those criteria without pretending that your business is the correct choice for every person using the category.
Show the dimensions a person should compare and which facts remain unknown. Do not invent competitor weaknesses or quietly choose criteria that predetermine the winner. A useful comparison helps the customer weigh fit, including circumstances where the offer may not be appropriate.
A decision-support tool can organize information for a person’s situation when there is genuine expert logic to apply. Begin with the choice and required inputs. Do not build a tool solely to claim AI readiness if its output adds little beyond a generic invitation to contact the business.
If an article or tool recommends an action, state what the person can do and what that action means. Distinguish learning more, submitting a request, and making a commitment. This helps both a human reader and a compatible assistant avoid inferring authority that the result does not provide.
Give the substantive answer before asking for an email address or commercial conversation. An optional next step can follow when it is relevant and clearly described. The customer should be able to judge the usefulness of the information without first becoming a lead.
The working rule I return to is this: “Being findable is about visibility. Being chosen is about suitability.” It is a way to judge the next piece of work, while keeping its evidence and limitations visible.
What remains outside my control on AI platforms?
Selection varies with the platform, task, context, and information available. A business cannot infer universal consideration from one favorable answer. Record the setting of any observation and avoid turning a single mention into a statement that every AI-assisted customer will encounter the business.
OpenAI’s shopping documentation describes product selection in relation to intent and available information. That is useful context for shopping questions, but it is not a complete rulebook for all service-business recommendations. Apply platform documentation within its stated scope rather than generalizing it to every use case. OpenAI: Shopping with ChatGPT Search; accessed September 2026
Google’s AI-search guidance retains established search requirements and does not promise inclusion for every eligible page. Improving a page can remove obstacles without securing a particular response. Keep the work you completed separate from the external behavior you hope to observe. Google Search Central: AI features and your website; accessed September 2026
A public page or callable endpoint may be reachable yet fail to answer the customer’s question. Test the contribution it makes after access succeeds. The important follow-up is what the person can now understand or do, not simply whether a technical request returned successfully.
A provider can promise specific work, such as fixing a documented issue or delivering a tested interface. A general promise that AI will recommend the business requires a different basis. Ask what is guaranteed, under which conditions, and what evidence supports that exact claim.
How can I choose the first improvement?
List the audience, offer, conditions, evidence, and next step for one service. Mark gaps and uncertainties explicitly. This creates an actionable review without requiring a complete rebrand or an unsupported prediction about which AI platform will become most important to the business.
DERA can help prioritize owner-reported conditions across seven dimensions. It returns three starting actions and no overall numerical score. Treat it as orientation for the work, not a test of whether ChatGPT or another system will recommend your business. Decision Economy Institute: live capability manifest; accessed September 2026
When you choose an improvement, identify how completion will be checked. A revised claim needs a supporting source; a repaired route needs a functional check. Match the evidence to the task rather than using one broad readiness statement to stand in for every kind of verification.
If an AI system mentions, links to, or invokes your capability, record exactly which behavior occurred and when. Keep those observations distinct from customer purchases or improved decisions. The record becomes more useful when its limitations are preserved rather than hidden behind a single success count.
Set an owner and a review trigger for consequential information. Clear facts can become inaccurate when the business changes. Ongoing maintenance supports the usefulness of the answer, while honest measurement tells you what external behavior has actually been observed after the work.
What can I do with this today?
Start with one offer and write down the next decision this article helps you examine. Give the work a defined scope before adding a new page, tool or integration.
1. Inspect one offer for missing customer facts.
2. Identify the consequential fact or condition that remains uncertain. Name who can check it and what would establish completion.
3. If you need help ordering the business work, complete the free Decision Economy Readiness Assessment. Read its reasons and three starting actions, then choose the first task you can inspect.
Our own example is deliberately bounded. The Institute’s Method explains the owner-reported result; its capability manifest declares what the tool can do; and its assessment schema makes the question bank and data contract inspectable. These September 2026 publisher documents describe the assessment. They do not independently establish better customer or business outcomes.
Make the offer worth understanding
Before the next customer faces the same unresolved choice, identify the improvement that would make the answer more useful. Use the readiness assessment to turn your reported conditions into three starting actions, and keep the next evidence check in view.
Disclaimer
Based on your answers, this assessment suggests improvement priorities; it does not independently verify your business or predict AI recommendations, sales, or business quality.
FAQ
How do I define who an offer is for?
Audience information helps establish relevance. State the circumstances in which the service is appropriate and the important limits. Do not make the audience so broad that the description stops helping anyone decide whether the offer fits the particular situation they are trying to resolve.
Which terms need to stay current?
Important terms should be easy to locate before a customer takes a step that depends on them. If something requires confirmation, say so. A statement of current terms and a statement of guaranteed future availability are different promises, and the page should not blur that difference.
How can a comparison be fair?
Show the dimensions a person should compare and which facts remain unknown. Do not invent competitor weaknesses or quietly choose criteria that predetermine the winner. A useful comparison helps the customer weigh fit, including circumstances where the offer may not be appropriate.
Does technical access establish useful help?
A public page or callable endpoint may be reachable yet fail to answer the customer’s question. Test the contribution it makes after access succeeds. The important follow-up is what the person can now understand or do, not simply whether a technical request returned successfully.
How should a limited observation be reported?
If an AI system mentions, links to, or invokes your capability, record exactly which behavior occurred and when. Keep those observations distinct from customer purchases or improved decisions. The record becomes more useful when its limitations are preserved rather than hidden behind a single success count.
Do I need to share contact details before seeing the assessment result?
No. The complete result is available before an optional email request. If a contact commitment has held you back, try the free readiness assessment and read the plan first. Emailing it and consenting to future updates are separate choices. The result remains BY YOUR ACCOUNT.
References
Curtiss Witt. The Decision Economy, updated author-supplied manuscript, Chapter 3 for the quoted decision rule; the supplied manuscript also grounds the framework. Supplied September 2026; unpublished manuscript, so no public URL is asserted.
OpenAI: Shopping with ChatGPT Search. First-party product guidance, retrieved September 10, 2026. Documents selection context, metadata, review and price limitations, and eligible checkout. Its shopping behavior is not a universal business-recommendation algorithm.
Google Search Central: AI features and your website. First-party platform guidance. Existing SEO fundamentals remain relevant; no special AI markup is required for these Google features. Eligibility is not guaranteed inclusion. Scope is Google, not every assistant.
Decision Economy Institute: live capability manifest. Publisher’s capability declaration, accessed September 10, 2026. Describes the available routes and their limits; not an independent test of every operation.
Decision Economy Institute: How this assessment works. Publisher’s own Method; ruleset de-readiness-1.0.0, accessed September 10, 2026. Not an independent outcomes study.
Decision Economy Institute: Assessment schema and question bank. Publisher’s technical contract; accessed September 10, 2026. Data shape and published question bank, not proof of real-world decision quality.
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Tags: AI business visibility; Decision Economy; Decision Economy Institute; Curtiss Witt; customer decisions; decision support; business readiness; DERA; Better Choices; Consideration.