Overview
The organisation wanted to scale its use of generative AI across marketing.
The first assessment showed that AI was not the main constraint.
Ownership was unclear. Content existed in multiple versions. Approval rules depended on institutional memory. Metadata was inconsistent. Performance data rarely changed what was produced next.
We redesigned the operating model so AI could participate in explicit, governed processes.
Who This Is For
Leadership teams asking:
- Where can we safely use AI in marketing?
- Why is AI output inconsistent?
- Which content can an assistant trust?
- How do we avoid accelerating low-value production?
- What should remain a human decision?
- How do we govern this without creating bureaucracy?
The Situation
Different employees were already using AI.
But there was no shared model defining approved source material, sensitive content, review requirements, decision authority, acceptable automation, escalation or lifecycle ownership.
Adding more AI risked making an already inconsistent operation faster rather than better.
Where the Workflow Breaks
Our Approach
Map the real process
Document stages, actors, inputs, outputs, decisions, exceptions and hand-offs.
Define content authority
Identify:
- Canonical sources
- Owners
- Review requirements
- Expiry and review dates
- Evidence standards
Classify work by judgement
Separate:
- Deterministic, repetitive tasks
- Assistive tasks
- Judgement-heavy decisions
- Sensitive or high-risk decisions
Introduce AI at the right points
Automate or assist where inputs and expected outputs are sufficiently explicit.
Build measurement into the loop
Make performance, search demand, sales feedback and content health inputs to future decisions.
Before → After
Where AI Helps
Potential roles include:
- Classification
- Summarisation
- Metadata
- Duplication detection
- Source retrieval
- First drafts
- Transformation
- Localisation assistance
- QA checks
- Performance synthesis
AI should not silently assume authority it has not been given.
What Changes
- Clearer AI use cases
- Fewer uncontrolled experiments
- Explicit human accountability
- Better source reliability
- Easier automation
- More consistent quality
- A roadmap based on process value rather than tool novelty
These are the operational changes the work aims for. Any measures are agreed against your own baseline during scoping.
What This Demonstrates
AI readiness is an operating-model problem as much as a technology problem.
Relevant experience
Named projects that show parts of this approach in practice. They evidence the underlying experience; none of them is the scenario above, and each case study says how it was delivered.
- Content Strategy & Editorial Governance for a Nordic Pension & Insurance Group
An editorial governance model to run a group's digital content as one system.
- Redesigning Content Taxonomy for a Global Paints & Chemicals Manufacturer
Taxonomy, metadata and a governance model for sustained classification quality.
- Content Operations Transformation
Editorial governance, analytics and a major platform migration within one content operation.
- Content Strategy & CMS Migration for a Professional Association
Content strategy alongside a CMS migration for a large membership organisation.
At a glance
Client type
Capabilities
- Content governance
- Process architecture
- AI readiness
- Taxonomy
- Measurement
Most relevant to
PE-backed B2B companiesRelated patterns
Redesigning the Content Supply Chain Around Human + AI Work
Resource-constrained B2B marketing team
People were already using AI, but every workflow was different. We defined the inputs, outputs, owners, AI tasks, human decisions and quality gates needed to turn individual experimentation into a repeatable operation.
Creating One Knowledge Layer for Marketing, Sales and AI
Complex B2B organisation
The organisation had plenty of systems but no shared view of which information was authoritative. We created the model connecting content, product knowledge, customer questions and proof without requiring everything to move into one new platform.
Recognise this problem?
We can start by mapping the workflow, finding the bottleneck and defining a contained first improvement.
Discuss a first project