Build an AI Search Keyword & Prompt Strategy That Works

Key takeaways

Keywords are still useful, but AI search starts from full natural language prompts, not 2 word phrases.
An AI search keyword strategy maps topics to the real prompts buyers type into ChatGPT and other assistants.
You can mine prompts from sales calls, RFPs, support tickets, internal search, and live AI usage.
Verticals like medtech and B2B SaaS show clear patterns in how clinicians, admins, and operators phrase questions.
The goal is pages that answer both the keyword and the full prompt, so AI tools are comfortable citing you.

Why classic keyword research is not enough anymore

For years, building a search strategy meant pulling keyword lists, grouping them by intent, and mapping them to pages. It was about winning specific phrases in Google:

“remote patient monitoring software”
“sales forecasting SaaS”
“manufacturing execution system vendors”

That still matters, but it is now only half the picture.

When your buyers open ChatGPT, Gemini, or Perplexity, they do not type in “remote patient monitoring software.” They ask:

“What remote patient monitoring platforms work well for heart failure patients in a hospital at home program”
“Which forecasting tools integrate with Salesforce and help a VP of Sales see risk earlier”

If your search strategy stops at keywords, you will miss how people actually search in AI tools. Our approach is to evolve keyword research into prompt research and treat both as part of one AI search strategy.

From keyword lists to prompt maps

Keywords are still valuable because they show volume, competition, and topics that matter. The shift is how we build on them.

A keyword like “remote patient monitoring software” might sit at the center of a cluster. Around it, real prompts look like:

“Best remote patient monitoring platforms for heart failure in older adults at home”
“Remote monitoring solutions that integrate with Epic and support hospital at home models”
“How to choose a remote patient monitoring vendor for a cardiology service line”

The keyword is the shorthand. The prompts contain the real buying context: role, use case, constraints, and questions.

Our job is to turn each keyword cluster into a prompt map that captures:

Who is asking
What scenario they are in
What outcomes or constraints they care about

Then we build content and structure that can satisfy both the short keyword and the full prompt.

How we actually do prompt research

Prompt research is not guesswork. We pull from real data and behavior.

1. Mine your existing language

We start by extracting raw questions and phrases from:

Sales and demo call transcripts
RFPs and security questionnaires
Support tickets and onboarding emails
Internal site search logs and chat logs

If a medtech sales call transcript shows a cardiologist asking:

“Will this work for post discharge heart failure monitoring in a mixed Epic and Cerner environment”

that line becomes a seed prompt.

If a B2B SaaS prospect emails:

“We need a revenue analytics platform that works across Salesforce, HubSpot, and NetSuite without forcing our ops team to rebuild everything”

that becomes another.

These are more valuable than any generic keyword tool output because they come from actual buying situations.

2. Expand prompts with AI, then filter with judgment

We then use AI tools in a controlled way to expand each seed into related prompts. For example, for a medtech RPM keyword cluster we might generate variations like:

“How do hospitals evaluate remote patient monitoring vendors for heart failure programs”
“What integration questions should IT ask when selecting an RPM vendor”
“What metrics to track when launching a remote patient monitoring program”

Not all of these will be worth targeting, but they reveal how different roles push on the same decision.

We keep the prompts that:

Match your ICP and use cases
Contain language we hear in the field
Are specific enough to inform a page, not just a line of copy

3. Group prompts by role, scenario, and stage

Next we group the prompts into clusters based on:

Role: clinician, IT, finance, operations, C suite
Scenario: evaluation, comparison, implementation, optimization
Stage: early exploration, shortlist creation, final selection

For a digital health platform, that might look like:

Clinician exploration prompts
IT integration and security prompts
CFO and procurement value case prompts

Each group informs a different type of page or asset.

Real world examples in the verticals we target

Medtech and digital health

Keyword: “digital therapeutics for diabetes”

Prompt map highlights might include:

“Which digital therapeutics for diabetes have strong clinical evidence and FDA clearance”
“How do digital therapeutics integrate with Epic for diabetes population health programs”
“What should a hospital look for when evaluating digital therapeutics vendors for type 2 diabetes patients”

Content implications:

Evidence hubs that summarize trials, outcomes, and regulatory status in accessible language
Integration pages that spell out EHR workflows and data flows
Buying guides for hospital committees that map selection criteria to your strengths

B2B SaaS in revenue and operations

Keyword: “revenue intelligence platform”

Prompt map highlights:

“Best revenue intelligence platforms for a 50 person B2B sales team on Salesforce”
“Tools that help a VP of Sales forecast pipeline more accurately without changing rep behavior”
“How to compare revenue intelligence vendors on data sources, AI models, and usability”

Content implications:

Role based pages for VPs of Sales, RevOps, and Finance explaining impact in their terms
Comparison guides that outline criteria and tradeoffs rather than just feature lists
Scenario pages like “Improving forecast accuracy for mid market SaaS” that map cleanly to prompts

Industrial and complex B2B

Keyword: “manufacturing execution system vendors”

Prompt map highlights:

“MES vendors for mid sized discrete manufacturing plants looking to reduce scrap”
“How to evaluate MES platforms that integrate with existing ERP and shop floor equipment”
“What questions should operations leaders ask MES vendors during selection”

Content implications:

Use case pages tied to outcomes like scrap reduction or OEE improvement
Integration overviews that map MES to ERP, PLCs, and reporting tools
Checklists and question lists that committees can reuse directly

In each case, the keyword is the anchor. The prompts show the real buying context that AI tools will try to answer.

Turning prompt maps into AI search keyword strategy

Once we have prompt maps, we connect them back to your content and search footprint.

Map prompts to pages, not just keywords

For each prompt cluster we decide:

Do we have a page that clearly answers this prompt today
If yes, does the page use similar language in headings and copy
If no, do we create a new page or refactor an existing one

A core rule: one primary use case or scenario per critical page so AI tools have a clear, focused source to pull from.

Write for both the crawler and the model

On each page we:

Include classic keyword signals in titles, H1s, and metadata
Use the natural language of prompts in subheadings and body copy
Add structured elements like FAQs and tables that mirror the question patterns we see

This makes the page understandable for Google’s index and for large language models that need clear, quotable sections.

Close the loop with testing and iteration

Finally, we test. At a regular cadence we:

Run representative prompts in public AI tools and see who is mentioned
Compare how your product is described versus how you want to be positioned
Feed those observations into the next round of content and structure updates

Over time, you are not just moving up in Google. You are appearing more often, more accurately, and earlier inside AI generated answers.

Why this matters for CMOs and marketing leaders

If buyers are using AI tools to define categories, shortlist vendors, and frame RFP criteria, then being invisible in those answers means losing the deal before it looks like a deal.

An AI search keyword strategy does not throw out classic SEO. It extends it. You still care about keywords and rankings. You also care about the prompts that drive real decisions in ChatGPT and its peers, and whether your content is the raw material those models use.

That is exactly how we approach it at Potenture. We run AI visibility audits, build prompt maps from your real buyer language, and rebuild content so it works for search engines and AI assistants at the same time.