Designed signal gathering — Editorial Intelligence

Active experiment. Designed signal gathering grew from work across Sage webinars, Accountex, FAB and Leaders in Accounting. It is now being tested as a broader method for turning knowledge gaps into deliberate evidence-gathering activity.

The problem

Organisations generate useful signals constantly: through events, webinars, interviews, customer and sales calls, support queries, advisory boards, research sessions and community conversations.

Almost none of it is gathered in a way that’s deliberate and reusable.

The standard pattern is to begin with the activity. Run the webinar. Attend the event. Conduct the interview. Analyse the call. Capture a few quotes, produce an asset and move on. The intelligence that emerged disappears. Three months later, a brief asks what customers are worried about, which questions keep recurring or what the organisation can credibly prove, and no one has a structured answer.

This isn’t a content problem. It’s an intelligence capture problem.

The experiment

Designed signal gathering reverses the usual order.

It starts with the knowledge or evidence gap, then asks which questions, people and activities are most likely to produce a useful answer.

The question it is testing: can recurring questions, practitioner conversations, customer language, expert disagreement and other live signals become deliberate inputs into narratives, frameworks, research and sales enablement that compound over time?

A newer part of the experiment pushes the model one step further upstream: can GEO and AI visibility analysis tell us what evidence to go looking for before the signal-gathering activity starts?

If AI/search analysis exposes a question an organisation answers weakly, a topic where competitors have stronger evidence, or a claim that lacks first-hand support, the response does not always have to be another optimisation pass on an existing page. Sometimes the evidence itself is missing.

That turns an event, webinar, roundtable, interview, customer call, advisory board or research session into a designed evidence-capture opportunity.

The method began with webinars and live events, but it is wider than either format. It applies wherever an organisation has access to knowledgeable people, recurring questions or first-hand signals.

Activities used in this experiment so far

Sage webinar series (accounting and bookkeeping)
Accountex (London and Manchester)
FAB (Finance, Accounting and Bookkeeping Show)
Leaders in Accounting

How it works

The capture-and-learning loop remains useful:

Signal-gathering activity
  ↓
Signals
(practitioner observations, recurring themes, audience questions, speaker claims)
  ↓
Patterns
(what's consistent across conversations and sessions)
  ↓
Narratives
(the strategic argument that the patterns support)
  ↓
Content, PR, sales enablement, customer stories, future research
  ↓
Learning loop
(what to test, probe and listen for in the next session or event)

But GEO creates a second, more deliberate route:

AI/search visibility signals
  ↓
Knowledge or evidence gap
  ↓
Editorial questions
(what do we need to know, who would know, what would constitute evidence?)
  ↓
Event, webinar, roundtable, interview, customer or sales call, advisory board, research session or video shoot
  ↓
First-hand evidence
  ↓
Accessible public knowledge
(article, Q&A, transcript-derived page, customer evidence, research input)
  ↓
Optimise, connect and measure

The important shift is from “what content can we make from this?” to “what does the organisation need to know, and which signal-gathering activity could help us find out?”

This connects Designed signal gathering directly to the Evidence Engine and Editorial Intelligence and AI Search. GEO identifies where the knowledge system is weak; designed signal gathering helps choose how to strengthen the underlying knowledge rather than only optimise its presentation.

Read the Field Note: Don’t just gather content. Gather the evidence you’re missing →

Gather signals before choosing formats

The format should usually be a downstream decision, not the starting brief.

A video shoot, webinar, event or roundtable may be valuable because of the evidence it makes possible to capture: customer testimony, expert opinion, practitioner language, disagreement, recurring questions, stories and claims. The finished asset is one possible activation of that evidence, not necessarily the whole value of the activity.

This is especially useful when evaluating video. A polished film can be highly visible inside an organisation while depending on paid distribution to reach the intended external audience. Those are different outcomes and should not be collapsed into one measure of success.

It helps to separate:

Internal visibility — employees and stakeholders see and engage with the work.
Paid reach — distribution spend puts the work in front of the intended audience.
External organic value — customers, prospects and practitioners discover, consume or share it without paid amplification.
Evidence value — the activity captures distinctive knowledge that can support other work.
Reuse value — the captured evidence continues to work across articles, clips, social posts, sales material, newsletters, research and future narratives.

This does not mean video is low value, or that every shoot needs to produce dozens of derivative assets. It means production value should not be mistaken for business value.

A better starting question is not “how do we make a video?” but “what valuable thing can we learn or capture here, and what is the best way to activate it?”

The same principle applies to events and webinars. Attend or run them because there is a credible opportunity to acquire, test or strengthen useful evidence — then use editorial judgement to decide what that evidence should become.

Signal gathering and AI visibility

Designed signal gathering also changes the role of GEO.

Answer engines have limited need for another generic reformulation of information that already exists everywhere. Organisations have a stronger opportunity when they can publish distinctive, attributable knowledge: original research, customer evidence, practitioner experience, expert judgement and first-hand answers to questions buyers actually ask.

Signal gathering can create that source material. But the evidence should not remain trapped inside a recording, event recap or transient social asset. It needs to be verified, edited and made accessible in forms that people, search engines and AI systems can retrieve and connect.

The chain is therefore:

evidence gap → signal gathering → first-hand evidence → editorial judgement → accessible knowledge → distribution and AI visibility → learning

GEO is not only an optimisation layer at the end. It can help decide what the organisation needs to learn next.

What this produced

The Accountex 2026 signal note documented the shift from episodic to continuous accounting work — a pattern that emerged from multiple conversations and sessions across two days. That observation fed directly into the Episodic to Continuous narrative, strengthened the Hidden Hours evidence base, and gave the MTD Continuous Model narrative a set of practitioner-voiced observations to work with.

Webinar sessions contributed a different kind of signal: structured audience questions and poll responses that revealed which aspects of a topic were generating the most uncertainty in practice. Both sources — live events and webinars — produced material that is still being used months later.

The signal note didn’t produce a recap. It produced material that’s still being used months later.

What’s being tested

Whether starting with a named knowledge gap improves the quality and usefulness of the signals gathered
Whether a consistent capture template across different activities improves the quality and reusability of signals
How much of the pattern identification can be accelerated with AI, and how much requires editorial judgement
Whether webinar and event conversations can generate research hypotheses that feed into future survey design
How sales teams can use webinar and event intelligence as a live briefing tool rather than a post-session report
Whether GEO and AI visibility analysis can identify useful evidence gaps before an event, webinar, interview or other signal-gathering activity
Whether deliberately designed questions produce more distinctive first-hand evidence than opportunistic event capture
Whether that evidence can strengthen existing pages, narratives and AI-search authority once it is made accessible and connected
Whether high-production video activity produces distinctive evidence and reusable value beyond the hero asset itself
Whether separating employee engagement, external organic engagement, paid distribution and downstream action changes how video performance is understood

What this is not

Designed signal gathering is not simply social listening, call analysis or event capture under a new name. It is not a content repurposing system, and it does not cover webinar production, promotion or event management. The defining step is identifying what the organisation needs to learn before choosing how to gather the signal.

And the GEO extension does not assume that attending an event, publishing a transcript or creating a video automatically improves AI visibility. The hypothesis is narrower: AI visibility work can expose gaps in an organisation’s evidence base, and deliberately chosen signal-gathering activities may help acquire the missing first-hand knowledge. That evidence still needs to be edited, published accessibly, connected to the wider knowledge system and measured.

It is an attempt to make the intelligence gathered through webinars, events, interviews, customer conversations and other live signals as durable and reusable as formal research.

The experiment is ongoing. The method will improve as it is tested across a wider range of signal-gathering activities.