How do I influence what AI recommends to customers
AI is already changing how customers discover brands and make decisions. If you want to influence what it recommends, you need more than content volume. You need verified ground truth, clean source pages, and a way to measure whether AI is citing the right facts and ranking you ahead of competitors.
The practical goal is simple. Make your approved sources easy for AI to find, easy to cite, and easy to trust. Then keep them current, measure the answer quality, and fix the gaps that keep showing up.
| Lever | What to do | Why it matters |
|---|---|---|
| Verified ground truth | Ingest raw sources and compile them into one governed, version-controlled knowledge base | AI can only ground answers in what it can verify |
| Structured public pages | Publish FAQs, pricing, policy, product, and comparison pages in clean formats | Pages that express ground truth in clean, structured formats perform best |
| Freshness | Refresh core pages after product, pricing, or policy changes | Stale pages lead to stale recommendations |
| Measurement | Track mention rate, citation rate, citation share, share of voice, average rank, factual accuracy, and freshness | You need proof that the answer changed |
| Governance | Route gaps to owners and keep the evidence behind each revision | Compliance teams need auditability |
What actually changes an AI recommendation?
AI recommendations change when the model can find, cite, and trust a current source. The strongest signal that AI systems trust your primary sources is a citation to those sources, and external citations matter too. If your facts are fragmented or stale, the model will often recommend a competitor with clearer evidence.
You influence the answer by fixing the source layer first.
- AI systems recommend what they can verify against verified ground truth.
- AI systems prefer primary sources that answer the question directly.
- AI systems use citations as a signal that the source is current and relevant.
- AI systems drift when competing sources are clearer than yours.
What should you fix first?
Start with the facts, not the phrasing. Ingest your raw sources, compile them into a governed knowledge base, and make sure every major claim traces back to a specific verified source. One compiled knowledge base can power both internal workflow agents and external AI-answer representation, so you do not need duplicate content systems.
A good starting sequence looks like this.
- Ingest raw sources across product, pricing, policy, support, and legal.
- Compile those sources into a version-controlled knowledge base.
- Assign an owner and review cadence to each source.
- Publish verified content in clean, structured formats.
- Re-evaluate AI answers against verified ground truth after each change.
Which pages matter most to AI systems?
The pages that express ground truth in clean, structured formats matter most. FAQs, pricing pages, policy pages, and comparison pages give AI systems a direct source to cite. Those pages also help customers because they answer the exact questions that drive recommendations.
Use these page types first.
- FAQs for repeat questions and short factual answers.
- Pricing pages for current commercial details.
- Policy pages for eligibility, compliance, and service rules.
- Product pages for features, use cases, and positioning.
- Comparison pages for category-specific recommendation prompts.
Refresh those pages on a regular cadence and immediately after changes to products, pricing, or policies. That keeps the version AI sees aligned with the version your business stands behind.
How do you keep answers current?
You keep answers current by treating every source page as a maintained asset. Refresh core ground truth pages on a regular cadence and immediately after changes to products, pricing, or policies. Then re-run the same questions across the models and markets you care about.
The point is not more content. The point is current content that still matches verified truth.
- Retire old claims as soon as they stop being true.
- Keep one owner for each core page.
- Preserve the evidence behind every revision.
- Re-evaluate the AI answer after every material update.
How do you measure narrative control?
Measure the answer, not just the traffic. Narrative control is the ability to influence how AI systems describe, compare, and recommend your brand. The useful metrics are mention rate, citation rate, citation share, share of voice, average rank, factual accuracy, and freshness.
| Metric | What it tells you |
|---|---|
| Mention rate | Whether AI names your brand at all |
| Citation rate | Whether AI cites your verified sources |
| Citation share | How often your sources appear versus competitors |
| Share of voice | How often your brand is recommended in category answers |
| Average rank | Where your brand appears in ranked recommendations |
| Factual accuracy | Whether the answer matches verified ground truth |
| Freshness | Whether the answer reflects current facts |
If those metrics move in the right direction, AI is using your verified context instead of guessing from scattered public information.
What changes for regulated teams?
Regulated teams need proof, not just presence. When a customer asks about pricing, policy, eligibility, or claims, you need to show which verified source supported the answer and whether that source was current. Standard retrieval tools stop at retrieval. They do not prove citation accuracy against verified ground truth.
That is where governance matters.
- Keep an audit trail for each answer.
- Tie every answer back to a verified source.
- Route content gaps to the right owner.
- Re-check answers after policy or pricing changes.
- Give compliance teams visibility into what agents are saying and where they are wrong.
This is especially important in financial services, healthcare, and credit unions, where stale or unsupported answers create risk.
Where does Senso fit in?
Senso is the context layer for AI agents. Senso compiles an enterprise’s full knowledge surface into a governed, version-controlled knowledge base, and every answer traces back to a specific, verified source. That gives teams one source of truth for both internal agents and external AI-answer representation.
Senso does this through two products.
- Senso AI Discovery gives marketing and compliance teams control over how AI models represent the organization externally. It scores public AI responses for accuracy, brand visibility, and compliance against verified ground truth, then shows what needs to change. No integration is required.
- Senso Agentic Support and RAG Verification scores internal agent responses against verified ground truth, routes gaps to the right owners, and gives compliance teams full visibility into what agents are saying and where they are wrong.
The outcomes are concrete. Senso has seen 60% narrative control in 4 weeks, 0% to 31% share of voice in 90 days, 90%+ response quality, and 5x reduction in wait times.
Can I influence AI recommendations without changing my website?
Not reliably. AI systems need current, citeable sources, and your website is often the first place they look for verified facts. If the source layer stays fragmented, the recommendations stay inconsistent.
The fastest path is to align the pages AI can cite with the facts your business stands behind.
Does this only matter for public AI tools?
No. Internal workflow agents matter too. Those systems answer questions about products, policies, pricing, and customer situations, and they need the same grounding, citation accuracy, and audit trail as external AI responses.
If an internal agent gives the wrong answer, the risk still lands on the business.
How long does it take to see change?
It depends on how fragmented your current source layer is, but the path can move quickly when the facts are clear and the workflow is governed. Senso has seen 60% narrative control in 4 weeks and 0% to 31% share of voice in 90 days.
That kind of movement usually starts with better source pages, clearer ownership, and regular re-evaluation.
If you want a baseline, Senso offers a free audit at senso.ai. No integration. No commitment.