In practice
Editorial Intelligence in practice
How an editorial operating system emerged from day-to-day work
Every framework on this site began with a practical problem.
Not a theory. Not a startup idea. Not a consulting framework.
They emerged while working across research, customer stories, thought leadership, product marketing and event content — most extensively in my work at Sage.
The challenge was not producing more content. It was making better use of everything we were already learning.
Working together
I work with content and marketing teams who are sitting on good research, real customer insight and genuine expertise — but aren't extracting full value from it.
The work typically involves building the narrative layer that connects those inputs: identifying the through-lines, developing the strategic argument, and creating the frameworks that make the insight reusable across teams, channels and time.
If that's a problem you're working on, get in touch.
From content production to knowledge systems
Modern content marketing often treats projects as standalone deliverables.
Research becomes a report. An event becomes a blog. A customer interview becomes a case study. Then everyone moves on.
The effort resets with every cycle. The insight from one project rarely informs the next. Nothing compounds.
Editorial Intelligence asks a different question:
What if every piece of work became part of a connected knowledge system?
Not a library of assets. A system where each new signal strengthens existing narratives, each framework makes the next easier to build, and each piece of evidence raises the quality of everything connected to it.
Examples from practice
These frameworks didn't emerge from theory. Each one began as a practical problem and developed into a reusable method.
The Winning in Small H2 editorial brief illustrates what this looks like in practice. The shift it documents — from topic-driven content to pressure-driven systems — is Editorial Intelligence applied to a live content programme.
A topic-driven, campaign-driven approach. Each asset built once and judged on reach.
- MTD content
- AI content
- Growth content
- Standalone assets
A pressure-driven, system-driven approach. Content organised around where decisions are being made.
- Where pressure builds
- Who carries it
- Connected journeys
- Compounding evidence
Through-line: complexity grows faster than visibility.
Survey data became a strategic position that accumulated more evidence with every connected piece.
Conferences became signal-gathering systems rather than one-off content moments.
Interview transcripts and notes became a structured evidence base, not isolated case studies.
Producing consistent, high-quality customer evidence at scale without losing editorial quality.
Survey data on AI use became a framework for understanding where capacity gets lost and why.
Global survey of senior finance leaders became a three-part narrative on the expanding CFO role, AI adoption gaps and wellbeing. The same structural patterns as Hidden Hours, from a different vantage point.
Why document them?
The purpose of Editorial Intelligence OS is not to describe one organisation's marketing strategy.
It is to document repeatable editorial methods that can be adapted across thought leadership, customer marketing, PR, sales enablement, content strategy and knowledge management.
Editorial teams generate enormous amounts of organisational knowledge. Most of it disappears — into completed projects, departed colleagues, outdated documents.
Editorial Intelligence helps that knowledge compound instead of disappearing.
The frameworks here are not complete or final. They are documented at the point where they've proven useful — stable enough to share, open enough to develop.
Evidence of reuse
Examples where Editorial Intelligence assets were adopted, republished or extended beyond their original context.
There are four kinds of evidence that Editorial Intelligence is working.
Framework evidence — research programmes and strategic models that hold up across multiple uses. Hidden Hours, AI Maturity Curve.
Operational evidence — experiments and workflows that work in practice. Event Intelligence, Editorial Brief Generator, AI Action Workbook.
Organisational evidence — examples where research, synthesis or editorial framing is reused by other people to shape briefs, plans or decisions. This is evidence that the value can travel upstream into strategy rather than staying attached to one writer or one asset.
Market evidence — examples where the work was adopted, republished or reused by external organisations without being commissioned to do so. That's a different signal: it suggests the ideas have value beyond their original platform.
The examples below document the first clear organisational and market signals.
Strategic planning reuse
Research synthesis and narrative thinking developed around a live B2B programme was deliberately handed to another content lead as strategic context. Several of the upstream themes — sustained use of research, practitioner proof, events as evidence sources and connected activation — subsequently carried into broader campaign planning.
This is evidence of strategic reuse, not yet evidence of commercial impact. The next test is whether the resulting campaign decisions and activation produce stronger outcomes.
Enterprise Nation republication
Three articles developed from the AI Maturity Curve framework were independently republished by Enterprise Nation — a leading small business platform — extending their reach to a new audience without requiring new content to be produced.
Republished articles
Where is your accounting or bookkeeping firm on the AI maturity curve? What to automate: 30-day AI wins for accounting firms How to try AI without breaking your businessExternal republication is one indicator that a framework has value beyond its original context. It is not the measure of success — but it is a signal worth documenting.
From personal framework to organisational capability
The next step is not creating more frameworks. It is embedding the strongest methods into real workflows.
A framework that lives in one person's practice has limited value. The same method embedded across a team becomes a different kind of asset.
When these methods make those jobs easier, Editorial Intelligence becomes operational capability rather than a personal project.
That's the direction this site is moving in.