How to Manage Content Consistency at Scale?

AI can solve the problem of scale in content marketing and translation. But not through “better prompts” or “general guidelines for ChatGPT.” Maintaining consistency and quality requires a systemic approach based on a deep understanding of processes.

At Shiftum, we build, among other things, AI tools for content creation and localization for large organizations. In this article, I share what we have learned while working with clients — and where to start if you want to introduce AI into your company’s content processes.

The Problem — Operational Chaos at Scale

If you manage content in a multinational organization, you probably know this scenario:

The product team creates a new product description and communication guidelines. Based on that, dozens of derivative assets are produced — product cards for PIM, Amazon listings, email campaigns for different audience segments, Facebook and Google ads, social media posts, and a large amount of supporting content (meta tags, SEO descriptions, etc.). Each content type has different requirements: different lengths, formats, and tone.

Then all of this needs to be translated — or rather localized — into a dozen languages. Because localization is not word-for-word translation. A marketing claim in German may sound completely different from its English original, as it must resonate with local culture and language nuances. Some technology names are not translated at all. Others require adaptation to local markets.

In practice, content creation and localization often look like this: Excel files circulating via email between teams, agencies, and local marketing managers. Scattered guideline documents. Processes that exist mostly in people’s heads.

And this is not a sign of backwardness or outdated organizational processes.

Why Do Companies Still Work in Excel?

There are tools for managing translations — Translation Management Systems such as Phrase, Smartling, or Lokalise. They are technologically mature, offer dozens of integrations, and use AI.

The problem is that they were designed for software localization — managing short strings in applications, working with developers, integrating with Git and CI/CD. They operate in a project-based model: you create a project, upload a file, wait for the translation, and import the result.

Meanwhile, marketing operates in a continuous publishing model:

• On Monday, you publish a post in English
• On Tuesday, you want it in five languages
• On Wednesday, data shows the German version is not performing — you need a variant adapted to local preferences
• On Thursday, you want to combine learnings from all versions and report results

TMS tools are not designed for this flow. Marketing needs the flexibility that… Excel provides.

According to the Nimdzi report “What Buyers Really Want 2025”, enterprise companies working with spreadsheets and email are not acting out of ignorance, but making a rational decision due to the mismatch between available tools and real needs.

What About Using AI in the Form of ChatGPT?

In many organizations, employees try to solve the problem on their own — using ChatGPT or Claude to support content creation and translation.

At a small scale, this works. At a large scale, it creates more problems than benefits:

• Each employee prompts differently, so content is inconsistent.
• Each session requires re-feeding the model with language guidelines.
• In longer sessions, the model loses context and starts hallucinating.
• There is no central place for guidelines, terminology, or translation memory.
• No one controls quality at the organizational level.

Individual use of AI is not a solution to the problem of scale. It multiplies it.

From Chaos to a System — What It Looks Like in Practice

Theory is one thing, but what does a real transformation look like? Let me describe a project we are delivering for a global consumer electronics company.

Starting Point: Two Problems — Cost and Time

The client was spending approximately EUR 400,000 per year on translating product content (PIM) through an external provider. The process itself was automated — the PIM system sent a translation request whenever product data changed. The external system completed the task in 10–15 days, after which the Marketing Manager responsible for a given language received a notification that a translation was waiting for review. After approval (or after 72 hours), the content automatically returned to the PIM.

Technically, the process worked well. But it created two problems: cost (approx. EUR 400,000 per year) and time (10–15 days per request).

This PIM translation process became the trigger — the search for time and cost savings pushed the client to test AI-based solutions.

Other translation processes in the company (email campaigns, ads, content marketing) were handled manually — Excel files and email coordination.

Stage 1: Proof of Concept — Quality Validation

To determine whether AI tools could even be considered as an alternative to existing solutions, we built the simplest possible version of the system:

• A text field to paste content
• Selection of target language and context (what is being translated)
• Selection of the model used for translation
• A database of stylistic guidelines for several languages
• A technical terminology glossary (product and technology names that should not be translated)

At this stage, we had several goals:

• Verify whether AI can generate translations of acceptable quality — equal to or better than existing systems
• Determine which model performs best in this role
• Understand the real cost of such a solution — and the scale of savings achievable at larger volumes

The test results were positive in both quality and cost terms. The client decided to build an extended version of the system.

Stage 2: MVP for Manual Translation Processes

Before touching the automated PIM process, we had to prove the value of the solution in smaller, specialized translation workflows already operating in the organization.

We built an MVP for teams manually translating three types of content:

• Email campaigns (CRM) — translation of customer email campaigns
• Google/Facebook Ads — with control over field length limits
• Culinary recipes — content marketing used in the client’s application

Here, a key learning emerged: we did not try to change the client’s processes to fit the tool.

Teams worked in Excel — pasting source content, receiving translations, pasting them back, and sending them to local marketing managers for review. We adapted our system precisely to this working style:

• Support for structured translations — multiple fields translated together to preserve context
• Control of maximum translation length, as ad platforms do not accept overly long text
• Consistency across related elements within a single translation — headlines, leads, CTAs must sound coherent within an ad or email
• An interface optimized for Excel workflows — structured copy, paste, and CSV export

If we had tried to “revolutionize” the process at this stage and force teams to work differently, the tool would likely not have been adopted.

Stage 3: Automated PIM Integration

Only after the system proved its value in manual workflows did we move to automatic PIM integration.

We replaced the previous vendor with our solution — in a “plug and play” mode, without any changes on the client’s side. The process remained identical:

• The PIM system automatically sends a translation request when product data changes
• Our system detects and translates only changed fields (not the entire product description)
• The Marketing Manager receives a notification that a new translation is waiting for review
• Reviews and optionally edits it (with internal AI support tools such as “re-translate with X considered”)
• Approves — the translation automatically returns to the PIM

The same process. A different vendor. Radically different time and cost.

Stage 4: Changing Manual Processes (in progress)

Now that the system is established and proven, we are beginning to change processes.

We are using the moderator panel concept from the PIM integration to replace manual Excel file exchanges in remaining workflows. Email campaigns, ads, content marketing — everything moves to an internal translation flow with moderation in the system.

This is the moment when process change actually begins — not earlier.

Result: From 10–15 Days to 3 Minutes and Massive Cost Reduction

The entire project — from PoC to production deployment of automated PIM translations — took approximately 12 months, filled with testing, refinement, and close collaboration with individual teams.

Looking at the results, it was worth it:

For a single PIM translation process, we achieved real cost savings: from approximately EUR 400,000 per year to around EUR 4,000 per year. PIM translation turnaround time was reduced from several days to an average of 3 minutes per translation request.

These figures apply only to the single, most measurable translation process in the organization. In reality, there are many more, resulting in significantly greater — though harder to quantify — savings.

More important than the numbers: quality remained at an acceptable level thanks to native-speaker moderation, and the organization can now scale content at a pace previously impossible. The system currently handles around 8,000 translation requests per month, and this number continues to grow as new processes are integrated.

Where to Start — Practical, Step by Step

The case study above shows that transformation with quality preservation is possible. But where should you start if you want to introduce AI into your organization’s content processes?

From our experience, a sequence of four steps emerges. The order matters.

Step 1: Map One Process.

Not all of them. One.

Choose the process that hurts the most — costs the most, takes the longest, causes the most frustration. For our client, it was PIM. For you, it may be something else.

We map processes in two stages:

Stage A: Management Interviews

We start at a high level. What does the workflow look like? Who is responsible for what? Which systems are involved? This provides a general picture.

Stage B: Workshops with Operational Teams

This is where the real work begins. We talk to the people who actually perform the tasks daily. They show us how they work, with what tools, and which rules they follow. We receive concrete examples of content and formats. We enrich the process map with details that managers often lack — because in companies, processes usually exist in people’s heads, not in documentation.

An additional benefit: having real content examples allows us to initially test how AI handles this type of material — before building the system.

Step 2: Understand the Limitations of AI

Before deciding where to plug in AI, you need to understand what AI cannot do.

We often encounter assumptions driven by the massive hype around AI:

Expectation: “We’ll define guidelines once and the system will work.”

Reality: AI model guidelines require regular updates as new products, campaigns, and ideas emerge. This is not “set and forget” — it is a continuous process of updating and quality evaluation.

Expectation: “The system will learn automatically from moderator corrections.”

Reality: AI models do not learn like humans — automatically from corrections or feedback. You can build programmatic mechanisms that simulate this, but it is not magic. It requires deliberate system design around the AI model.

Expectation: “AI will always apply all guidelines we provide.”

Reality: AI models are non-deterministic. Given the same instructions, they may produce slightly different results each time. This means the model may occasionally omit a guideline. Human validation of AI output is therefore essential, not optional.

There is one more limitation that regularly surprises people: AI is bad at counting. Length control (e.g. “maximum 150 characters”) requires programmatic support, as language models are not reliable in this area.

Step 3: Decide Where to Use AI

A key principle to remember: AI excels at transforming content, but performs poorly at creating it from scratch.

AI works well for transforming one piece of content into another:

• Translation and localization are high quality (with good guidelines)
• Derivative content — turning product descriptions into titles, meta descriptions, tags, Amazon bullet points, ad variants
• Format adaptation — the same message, different length, different channel

AI requires caution when used for:

• Niche languages (e.g. Baltic countries) — less training data means lower output quality; strong moderation and very detailed guidelines are essential
• Creative content — AI has more freedom here, but marketing claims often must be standardized and reused consistently across formats and markets, without “creative” interpretation by the model; this requires an additional control layer

AI is not suitable for:

• Creating content from scratch without solid input data — output quality will be very low
• Situations with limited or poor-quality source content — poor input equals poor output, e.g. when product descriptions are based on weak, short “masters” from external providers

Process design takeaway: if you cannot ensure sufficient quantity and quality of input content (guidelines, examples, product data, target groups, content context, channel specifics), you must design the process so that this information is available before AI is used. Otherwise, results will be poor.

Step 4: Detailed Guidelines (Only Now)

Guidelines for AI models are the result of understanding the process in which they operate — not the starting point. That is why this step comes last.

AI guidelines should be built modularly — as separate, specialized blocks that the system dynamically combines depending on the task.

Example guideline structure:

• General brand guidelines (tone, style)
• Product-group guidelines (e.g. coffee machines vs vacuum cleaners — different language)
• Regional guidelines (Europe, Asia, South America)
• Language variants within a country (e.g. Brazilian vs European Portuguese)
• Content-type guidelines (PIM, ads, email campaigns)
• Terminology and marketing claim glossaries
• Translation memory (previous translations and selected examples)

Sounds like a lot of work? It is.

The good news: not everything requires maximum quality and ultra-precise guidelines.

PIM product content reused across many channels — yes, this needs detailed guidelines. Translating user reviews in a store? It must be understandable, not perfect.

Prioritization by content importance helps avoid paralysis in the “guideline preparation” phase and endless evaluation.

There is also a cost aspect: more detailed guidelines mean more input tokens processed by the model, which increases content creation or translation costs. At scale, this has a significant budget impact.

What We Learned

To conclude — a few lessons from real implementations. Some were painful.

1. Augmentation, Not Revolution

The biggest mistake when implementing AI is trying to change organizational processes to fit a new tool.

People do not want to change habits. If a team works in Excel and email, and you introduce a “revolutionary system” that requires a completely new way of working, adoption will fail. The pilot will fail. Everyone will go back to the old way.

That is why our MVP for manual workflows was designed to support Excel, not eliminate it. Only after the tool built value and trust did we begin to gradually change processes.

First, augment existing work. Revolution — if ever — later.

2. General Guidelines Do Not Work

Initially, we thought one set of guidelines would work everywhere.

It didn’t.

Each context requires its own rules. Translating product cards follows different principles than translating creative ads. German-market communication requires different nuances than Brazilian. Coffee machines use different language than vacuum cleaners.

Attempts to apply “universal” guidelines reduced quality relative to team expectations. We had to go much deeper than anticipated.

Yes, we use some universal guidelines in the form of glossaries, but the level of reusability across processes is much lower than we initially assumed.

3. The Hidden Cost of the System

AI in content is not “set and forget.”

There is a cost rarely discussed: maintaining model guidelines. New products, new campaigns, new markets — everything requires updates. Someone must do this. And then validate output quality.

There is the cost of working with native speakers. Moderation requires people who know the language and culture. Sometimes they are available internally, sometimes not.

There is the cost of quality oversight. AI is non-deterministic — outputs must be reviewed, at least in processes where quality and consistency are critical.

This does not mean it is not worth it. It is — we saw cost reductions of over 90%. But expectations about post-implementation effort must be realistic.

4. We Rebuilt the System Three Times

We did not build the final system on the first attempt. Nor the second.

Each time we moved to a new stage (PoC → MVP → PIM integration), new guidelines and expectations emerged that had not existed before. The previous architecture could not support them.

If we were starting again, we would gather very deep guidelines from multiple teams before writing the first line of code.

But also: maybe this is natural. Maybe not everything can be predicted upfront. Maybe it is better to start with a simple PoC and iterate than to try to build a “perfect system” from day one.

Summary

AI can solve the problem of content consistency at scale. We have seen cost reductions of over 90% and turnaround times reduced from days to minutes.

But not through “better prompts” in ChatGPT. Not through “general AI guidelines.” Not through tools that try to revolutionize organizational processes.

What works is a systemic approach:

1. Start with one process and map it thoroughly.
2. Understand AI limitations before building.
3. Consciously decide where AI helps and where humans are needed.
4. Only then create detailed, process-specific guidelines.

And remember: first augment existing work, then — if ever — change processes.

This is not a magic bullet. It is an infrastructure investment that requires upfront effort and ongoing maintenance. But for organizations producing content at scale and in many languages — the investment pays off.