The SIGNAL Framework
A best-practice framework for AI-era discovery, visibility and trust, inspired by a conversation between Neil Wilkins and Stephan Bajaio.
The core idea from Stephan Bajaio’s narrative is that marketers should stop obsessing over “how do we rank in AI?” and instead ask:
How do we become a genuinely useful, credible, human-validated answer wherever our audience is trying to solve a problem?
SIGNAL works because it connects search behaviour, audience intent, AI-mediated discovery, human expertise and trust.
S — Set the commercial context
Before doing anything with AI, content or visibility, the marketer needs to understand the reality of the organisation they are operating in.
This step stops the business from chasing shiny objects and forces clarity around what is actually possible.
The marketer should ask:
What kind of business are we in?
Are we B2B, B2C, local, service-based, enterprise, SaaS, professional services or e-commerce?
Who controls the agenda?
Is marketing led by executives, sales, product, the founder, a CMO or a solo marketer?
What are we really trying to improve?
Visibility, trust, lead quality, sales enablement, retention, customer education, referrals, authority or market understanding?
What is our appetite for experimentation?
Are we allowed to test, learn and iterate, or does everything need to be signed off before it sees daylight?
The output from this stage is a clear opportunity statement.
For example:
“We need to become more visible and credible around the problems our best-fit customers are already trying to solve, without relying on generic AI-generated content or premature optimisation for LLM rankings.”
I — Investigate real human intent
This is the heart of the framework.
Stephan’s central belief is that people reveal the truth through the way they search and ask for help. The marketer’s job is to listen to that signal before creating anything.
This means looking beyond keywords as SEO data and treating them as evidence of human need.
The marketer should investigate:
Sales-call recordings
Customer-service chats
Support tickets
Search-query data
On-site search data
Review sites
Reddit, forums and community discussions
LinkedIn comments and questions
Questions asked in demos, webinars and events
Objections raised during sales conversations
Reasons customers churn or hesitate
Language customers use when describing pain, risk, fear or confusion
The key question is:
How do real people describe the problem before they know what the solution is called?
This step should produce a problem-language map, showing what:
Customers are trying to do
They are worried about
They misunderstand
They compare you with
They ask before buying
They ask after buying
Language they use naturally
and Where they go for answers
This prevents the business from producing polished but empty marketing fluff. It also protects against one of the big AI risks Stephan highlights: creating content that looks impressive but fails because it does not understand the customer’s real-world problem.
G — Gauge the answer landscape
Once the marketer understands the audience’s problems, the next step is to find out where those problems are currently being answered.
This is where Stephan’s idea of Web Presence Intelligence fits neatly into the framework.
The marketer should audit the answer ecosystem across:
Google search results
AI-generated answers
YouTube
Reddit
LinkedIn
Industry publications
Comparison sites
Review platforms
Partner websites
Forums and communities
Competitor content
Influencer or expert content
Sales enablement material
Offline sources of influence
The aim is not simply to ask, “Are we ranking?”
The better questions are:
Where are answers being provided?
Who is currently trusted?
Are the answers accurate?
Are they complete?
Are they biased?
Are they out of date?
Are we present directly, indirectly or not at all?
Do we deserve to be part of this conversation?
Could we build, buy or borrow our way into it?
This stage helps the marketer avoid the trap of assuming the brand always needs to be the direct answer. Sometimes:
The right move is to create the answer on your own site.
It is to contribute expertise to an existing community.
Advertise against a useful YouTube video.
Equip salespeople with better follow-up material.
The output is an answer landscape audit showing:
Where the audience looks
Who currently answers
Where trust is being formed
Where the brand is missing
Where the brand is misrepresented
Where the brand can credibly participate
Which opportunities should be built, bought or borrowed
N — Narrate with credible human expertise
This is where content is created, but with a very different mindset.
The goal is not volume or “AI content”.
The goal is to turn genuine expertise into useful, findable, trustworthy answers.
The marketer should identify the internal knowledge already sitting inside the organisation:
Salespeople who understand objections
Customer-success teams who understand adoption problems
Product experts who understand use cases
Founders who understand market shifts
Board members with credibility
Practitioners with lived experience
Legal or compliance experts who understand risk
Customers with powerful stories
Support teams who know where users get stuck
Then the marketer should turn this expertise into assets that solve rather than sell.
These might include:
Problem-led articles
Named expert pages
Founder or practitioner perspectives
Customer education guides
Comparison content
Use-case pages
Industry-specific pages
Sales follow-up content
Interactive tools
Calculators
Checklists
Explainers
Short videos
Webinars
FAQs based on actual customer questions
Post-purchase support content
The important point is that the content should be authored, attributable and human-validated.
Stephan’s warning is that generic content, especially content that looks like it was written by “the team”, will become less credible as AI increases the volume of average-quality material online.
In an AI-mediated discovery environment, trust comes from signals such as:
Who wrote this?
Why should I believe them?
What experience do they have?
Is this specific or generic?
Does this answer my real problem?
Can I verify it?
Does it feel like it understands me?
The output from this step is a human-led content and expertise plan.
A — Activate useful experiences, not just content
This is where the opportunity expands beyond traditional SEO or blog publishing.
Stephan’s narrative points towards a future where marketers create more interactive, personalised and problem-solving experiences, not just static content.
The marketer should ask, could:
This answer be more useful as a tool?
This become a calculator, diagnostic, checklist or guide?
Sales use this after a discovery call?
Customer success use this after onboarding?
A partner share this with their audience?
This work offline as well as online?
This support referrals, retention or adoption?
AI help us personalise or accelerate the experience without damaging trust?
Examples might include a:
Personalised landing page for a target account
Diagnostic tool for choosing the right service
Post-demo resource based on a prospect’s concern
Customer onboarding guide tailored by industry
Sales enablement article answering a common objection
Local partner resource that helps people before they are ready to buy
Comparison tool that helps buyers make a confident decision
Grief-support guide for vets to share with pet owners
Board-member interview that explains why credible people believe in the business
The crucial warning is that AI should not be treated as an “easy button”.
AI can help draft, organise, summarise, prototype and personalise, but the marketer remains responsible for accuracy, relevance, ethics and trust.
The output is an activation plan that defines:
What should be created
Who it is for
Where it should live
Who should use it
How it supports the customer journey
How it will be checked
How it will be distributed
How it will create trust
L — Learn, govern and optimise continuously
The final step is where the framework becomes a habit rather than a campaign.
Stephan is clear that optimisation is not a one-off task. It is a continuous process of improving how well the business understands, serves and earns trust from its audience.
The marketer should monitor:
Search visibility
AI answer visibility
Brand mentions
Referral traffic
Sales usage of content
Lead quality
Conversion quality
Customer-service themes
Customer complaints
Retention signals
Churn reasons
Content engagement
Community questions
Accuracy of third-party answers
Changes in customer language
Changes in competitive positioning
This is a measurement step and also a governance step.
The marketer should create rules for how AI is used, including:
What AI can draft
What must be human-reviewed
What needs subject-matter expert validation
What requires legal or compliance review
What should never be automated
How claims are checked
How sources are verified
How customer data is protected
How errors are corrected
How learning is shared internally
The marketer should also secure internal sponsorship. That does not always mean budget. It means getting people inside the business to understand why this work matters.
The marketer needs to show this is not just:
Content production
SEO
AI experimentation
This is a trust-building system that connects audience insight, expertise, visibility, sales and customer experience
The output is an optimisation loop: listen, audit, create, activate, measure, improve.
“It’s not what AI can do, but what it changes about how humans find and trust information.”
Stephan Bajaio, is one of the original co-founders of Conductor, one of the world’s leading SEO platforms. With 25 years in digital he has spent his career at the intersection of search behaviour and human intent, and the belief he keeps coming back to is that no one lies to their search bar.
When someone types a query, they’re not performing. They’re asking. Stephan believes it’s one of the most honest signals a brand has access to: fear, desire, curiosity, need, all compressed into a few words. Mindful marketing, at its best, is just the practice of actually listening to that signal instead of broadcasting over it.
Connect with Stephan Bajaio This link takes you directly to VibeLogic, where you can explore how SEO and AI are reshaping how businesses get found online. Whether you are a marketer, founder, or business owner trying to figure out what search looks like in the age of AI – this is where you go to stop guessing and start winning.
For more content like this, visit Neil Wilkins Online
