Overview
A marketing team was experimenting with AI across research, writing, localisation and repurposing, but each person had created a different personal workflow.
We redesigned the content supply chain around explicit inputs, outputs, decision rights and quality gates so AI could remove repetitive work without creating an uncontrolled production machine.
Who This Is For
Marketing teams under pressure to produce more with limited headcount and already using multiple AI tools informally.
The Situation
The apparent benefit of AI was speed.
The hidden cost was inconsistency.
Prompts, source material, review standards and final storage varied by person. Nobody could easily explain why one workflow produced good output and another did not.
Where the Workflow Breaks
Our Approach
Map the supply chain
Plan, brief, source, create, review, approve, publish, distribute, measure, maintain.
Define stage contracts
For every stage specify:
- Required input
- Expected output
- Owner
- Quality criteria
- Tool or agent role
- Exception route
Allocate work deliberately
Use automation for repeatable transformations. Use AI assistance where interpretation is useful but review remains important. Keep judgement-heavy decisions human.
Build quality gates
Check facts, brand, legal and compliance, links, metadata, accessibility and source authority as appropriate.
Capture workflow performance
Measure bottlenecks and rework, not just production volume.
Before → After
Where AI Helps
This scenario is explicitly about allocating AI roles. The principle is simple:
Automate the predictable. Assist the interpretive. Keep accountable judgement human.
What Changes
- Less workflow variation
- Easier onboarding
- Clearer quality control
- More reusable prompts and agents
- Lower dependence on individual AI skill
- Better visibility of where time is actually lost
These are the operational changes the work aims for. Any measures are agreed against your own baseline during scoping.
What This Demonstrates
The valuable asset is not the prompt.
It is the operating process around the prompt.
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 Operations Transformation
A scalable content and measurement operation run over several years with a multidisciplinary team.
- Editorial Strategy & Global Intranet Content Migration for an Oil & Gas Company
Editorial workflows, content lifecycle policy and a managed team of freelance contributors.
- Content Operations for a Nordic Hotel Group
Ongoing production in three languages, working inside the client's CMS alongside in-house staff.
- Multilingual Content Operations for a National Seafood Trade Body
An outsourced content desk: writing, translating, publishing and turning round time-critical material.
At a glance
Client type
Capabilities
- Workflow design
- AI operations
- Governance
- Production
- QA
Most relevant to
SaaS and industrial techRelated patterns
Building an AI-Ready Content Operating Model
Mid-market or PE-backed B2B organisation
The organisation wanted more AI. The real blockers were unclear ownership, inconsistent sources and undocumented workflows. We made the process explicit first, then identified where AI could safely remove work or improve decisions.
Building a Multilingual Content System That Scales
International industrial B2B organisation
Instead of asking how to translate more cheaply, we redesigned what gets created centrally, what markets can adapt and how approved source knowledge moves across languages. AI then accelerates a controlled workflow rather than multiplying inconsistency.
Recognise this problem?
We can start by mapping the workflow, finding the bottleneck and defining a contained first improvement.
Discuss a first project