AI Discovery: What Innovation Organisations Need to Know
AI is fundamentally changing how innovation organisations are discovered, and most aren’t ready.
This article explores why current infrastructure is failing, what visibility means now, and how to structure content for both AI discovery and modern user expectations.
Recently, the CEO of an innovation organisation asked us a question we’re hearing more often:
“If people are using AI tools like ChatGPT to find us, will the website still matter?”
It’s a reasonable question. Discovery behaviour is changing quickly. More research happens inside AI tools, and decisions are shaped before anyone clicks through to a site.
But this isn’t a story about websites disappearing. It’s about understanding how AI discovery actually works, and why most organisations aren’t ready for it.
The discovery shift
When a founder asks ChatGPT “What innovation support exists for transport SMEs?” or “Which UK organisation specialises in digital twins?”, something happens before they ever reach your website:
- Options are filtered
- Assumptions are formed
- Credibility is assigned
By the time someone lands on your site, the shortlist often already exists.
This creates two visibility problems:
Problem 1: You’re not in the conversation
When AI tools answer questions about innovation support, funding, or specialist capabilities, the organisations that appear shape the entire conversation. Everyone else is invisible.
Problem 2: When people arrive, you can’t answer their questions
Someone lands on your site asking “Am I eligible?” or “What capabilities do you have in quantum computing?” They expect an answer in 30 seconds. Instead, they have to navigate through About pages, scroll through program descriptions, and piece information together themselves.
Most innovation accelerators are struggling with both. Not because their content is poor, but because their infrastructure wasn’t built for how people now search.
Why your current setup is failing
Content management systems were built for one job, publishing pages that people navigate through.
That made sense when discovery meant clicking through a site menu. But AI discovery works differently.
AI tools don’t browse. They interpret.
They look for meaning, relationships, and signals they can connect to a question. When your content is locked inside pages without structure or context, AI can’t tell what matters or how it relates.
Example: Your organisation might have excellent quantum computing facilities, but that information lives in:
- A general “Facilities” page
- Scattered case studies
- Program descriptions organised by date, not capability
This means when someone asks ChatGPT about quantum support, you may not appear. The information exists, but it isn’t legible in the moments that now shape discovery.
The same problem affects your website experience.
When someone lands asking “Am I eligible for this program?”, they shouldn’t have to:
- Read your entire About section
- Navigate to a separate eligibility page
- Cross-reference program descriptions with criteria
- Contact you to confirm what’s already published
But most websites make them do exactly that, because content is organised around internal structure (how you publish) rather than external questions (what people ask).
This is the discoverability issue: Content management systems organise content into pages. AI tools and users need it structured as knowledge in the forms of questions, capabilities and relationships.
What works instead
To work across AI discovery and on-site experience, content needs to be structured differently.
Instead of organising by page type:
- Services page
- Case Studies page
- About Us page
Structure by question and capability:
- What problems do you solve?
- Which sectors do you work in?
- What makes you different from other similar companies?
- How do I know if I’m eligible?
This isn’t about rewriting your content. It’s about adding a layer of structure that makes the same content work in more places.
When content has this semantic structure:
- AI tools can interpret and cite it (“Connected Places Catapult offers transport innovation support including…”)
- Websites can surface relevant answers based on what someone’s looking for
- The same knowledge works everywhere, not just in your CMS
A concrete example:
Right now, if you have a quantum computing program, it might be described on a facilities page, mentioned in case studies, and have eligibility criteria in a PDF.
With semantic structure:
- “Quantum computing” is identified as a capability
- It’s connected to relevant sectors (aerospace, defense, research)
- It’s linked to specific questions (“What facilities do you have?” “Am I eligible?”)
- It has relationships to other capabilities and programs
When someone asks ChatGPT about quantum support, it can find and cite you. When someone lands on your site, they get quantum-specific answers without hunting through navigat
Why a ChatGTP style search on your website may not be the answer
When organisations think about AI on their website, the first instinct is often: “Let’s add a search box where people can ask questions.”
The problem is that open-ended prompts assume users know what to ask. Most people arrive with partial intent, not clear questions. A blank input box slows them down.
What works better is guidance.
By recognising behaviour and context, websites can:
- Present relevant options automatically
- Adapt journeys based on what’s clear about their intent
- Surface answers without requiring perfect questions
AI is most useful when it interprets and responds, not when it puts another empty box in front of someone who’s already trying to figure things out.
What you need to do
The shift is already underway. Discovery has changed. Expectations have changed.
Adapting doesn’t mean rebuilding everything. It means working at three levels:
Layer 1: Immediate content fixes (this week)
- Audit your top 5 program or capability pages
- Restructure with question-based headers
- Move eligibility criteria to the top
- Make sure key information works as standalone chunks
Layer 2: Strategic planning (next quarter)
- Map your content as knowledge, not pages
- Identify the 20-30 “golden questions” people actually ask
- Define relationships between capabilities, sectors, and programs
- Build a roadmap for semantic structure
Layer 3: Infrastructure investment (6-12 months)
- Move toward knowledge-first CMS architecture
- Enable adaptive website experiences based on intent
- Build for AI readability by default
- Create systems that get smarter over time
Most organisations will need all three layers. The question is how quickly you can move through them, and whether you’ll lead or follow as this becomes standard
Why this matters now
As Large Language Models build their picture of the innovation landscape right now, the organisations that make themselves visible will be found faster by the people who need them.
This isn’t a distant trend. It’s happening today:
- Founders are asking AI about innovation support before they search Google
- Users expect websites to work like AI tools and provide immediate, relevant answers
- Organisations that adapt early will own the visibility advantage
The question isn’t whether to respond. It’s whether you’ll shape how you’re discovered, or let someone else’s content structure do it for you.
Next steps
We’re running a practical 45-minute session on exactly this on the 6th March @ 12:30.:
- How to structure content for AI visibility
- What to change on your website to answer questions faster
- Frameworks and worksheets for immediate implementation through strategic planning
Would this be valuable for your team?
We don’t charge for these workshops. Our aim is to grow the network and help innovation accelerators stay ahead of these challenges.