Overview
An international B2B organisation was creating similar content repeatedly across markets. Translation was treated as the localisation problem, but the larger cost sat upstream: inconsistent source content, duplicated assets, unclear market ownership and no reliable way to know what should be reused, adapted or created locally.
We redesigned localisation as a content operating model rather than a translation queue.
Who This Is For
Export-oriented organisations publishing product, application, campaign and thought-leadership content across several countries or languages.
Especially relevant when:
- Every market maintains its own versions
- Headquarters cannot see what local teams have created
- Translation starts from inconsistent source material
- Product claims drift between languages
- Updates require repeated manual coordination
- AI translation is being considered without a source-content model
The Situation
Local teams wanted freedom because central content often did not reflect their market.
Central marketing wanted consistency because duplicated production was expensive and difficult to govern.
Both were right.
The operating model gave them no useful middle ground between central control and local reinvention.
Where the Workflow Breaks
Our Approach
Separate universal from local
Define which content is:
- Globally canonical
- Market-adaptable
- Market-specific
- Legally or technically controlled
Improve source content
Design source material for reuse:
- Shorter modular units
- Explicit terminology
- Controlled claims
- Fewer culturally embedded assumptions
- Structured metadata
- Ownership and review dates
Define localisation decision rights
Make explicit:
- What markets can change
- What requires technical approval
- When transcreation is justified
- When local creation is preferable
- Who owns terminology
Build a reusable workflow
Source approved, then a localisation package, machine or AI assistance where suitable, local adaptation, QA, publication, and feedback captured centrally.
Measure avoidable work
Track repeated translation, duplicate creation, review cycles, outdated variants and content with low market value.
Before → After
Where AI Helps
AI can accelerate:
- First-pass translation
- Terminology checks
- Variant comparison
- Source simplification
- Metadata generation
- Identification of duplicated material
But AI cannot decide the commercial importance of a market nuance or whether a regulated product claim is safe.
What Changes
- Less unnecessary translation
- Stronger terminology consistency
- Clearer central and local responsibilities
- Faster updates
- Better reuse
- More visibility across markets
- AI localisation built on governed source material
These are the operational changes the work aims for. Any measures are agreed against your own baseline during scoping.
What This Demonstrates
Localisation performance is often determined before translation begins.
ECM.DEV fixes the source-content and workflow problem first.
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
Multilingual publishing across 14 languages, with editorial governance, for a national tourism platform.
- Multilingual Content Operations for a National Seafood Trade Body
Original English content moved into more than a dozen languages and published in the CMS, supported continuously since 2012.
- Content Operations for a Nordic Hotel Group
Ongoing multilingual production and SEO inside the client's CMS across Swedish, Norwegian and English.
- Content Rebuild for a Paints & Coatings Manufacturer's Optimizely CMS Migration
Rebuilding a global manufacturer's corporate site and full B2B product range for a new CMS.
At a glance
Client type
Capabilities
- Localisation
- Source-content design
- Governance
- Translation workflow
- AI-assisted localisation
Most relevant to
Multilingual industrial teamsRelated patterns
Making Product Knowledge Machine-Readable
Export-oriented industrial manufacturer
Product truth was distributed across systems, documents and experts. We connected specifications, applications, buyer problems and evidence into a reusable knowledge model that could support web, sales, localisation and AI retrieval.
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.
Recognise this problem?
We can start by mapping the workflow, finding the bottleneck and defining a contained first improvement.
Discuss a first project