Time for a digital spring clean: AI can't scale with messy data
Our brain has an elegant protective mechanism: the blood-brain barrier. It allows nutrients to pass through, but consistently blocks toxins. Without this filter, our central control organ would be defenceless. The opposite can currently be observed in the AI transformation: Companies are flooding their language models unfiltered with toxic data rubbish.
After the initial euphoria, many companies are now stuck in the so-called "messy middle" of AI implementation.
The licences are paid for, but there is no measurable business impact. The cause is mundane: AI does not magically make bad data better. The chaos is merely multiplied at the speed of light.
To avoid this mistake, it is worth taking a look at the B2C world. Innovative consumer brands have already learnt that AI success does not depend on the best algorithm, but on consistent groundwork.
Here are three lessons that B2B SMEs can learn from:
Lesson 1: Data liberation - cleaning up existing information and publishing it in a targeted manner
The Revelyst brand (parent company of Bell and CamelBak) didn't just blindly install its own chatbot, but wanted to increase its visibility in the well-known AI apps. The team first analysed all of its unstructured content - video tutorials, PDFs, instructions. In a massive effort, this information was extracted, converted into clean, machine-readable structures and then made openly accessible. When AI-based answer engines such as Perplexity search the web, Revelyst now delivers perfectly prepared answers instead of imprecise fragments.
B2B learning:
Thousands of technical data sheets and complex product specifications lie as "PDF corpses" on servers. This is precisely where SMEs in industry often fail. But if you want to be visible in AI models, you need to free up this data. Digital platforms such as websites or customer portals must no longer be seen as mere page repositories. When fed with the right data, they act as highly available data hubs that provide finely structured information and thus become an essential data source for OpenAI and the like.
Lesson 2: Proprietary data sets instead of generic hallucinations
The fashion brand J.Crew wanted to use AI to intelligently summarise customer reviews. But instead of letting the model loose on the existing, thin data, the company first generated a massive amount of new, detailed buyer reviews. The realisation: an AI summary based on seven reviews is worthless; it needs at least seventy detailed experiences to identify real patterns. The technology is secondary if the proprietary data set is missing.
The B2B learning:
Since generic AI models know nothing about a company's highly specialised niche products, the crucial groundwork is to build up this specific company knowledge as a clean data set. In many cases, this means documenting internal expertise from scratch. But this painstaking work quickly pays off. With technologies such as RAG (Retrieval Augmented Generation), this information can be injected directly into AI models. The result is precise answers without hallucinations and absolute data sovereignty.
Lesson 3: Eliminate internal friction instead of chasing gimmicks
AI projects only work where they alleviate real pain. Revelyst formulated this as a maxim: AI is not only built for consumers, but explicitly for the company's own employees in order to eliminate friction losses in day-to-day work.
B2B learning:
An isolated chatbot on the homepage that hardly any buyers use does not create any business value. In the B2B environment, the real leverage often lies deep in the internal processes. If your specialists need hours to find specifications in grown legacy systems, this is where your greatest ROI lies. Build systems that relieve your employees of these administrative time wasters.
Data readiness is the foundation on which AI transformation must be built
AI is engaging the entire economy, whether B2C or B2B. Consumer brands are often more agile in adapting to such new technologies, and SMEs can learn a lot from this. The examples show: AI success is not a question of the best prompt, but of a clean architecture. Invest in structures, not in short-lived vapourware. Data quality is the foundation on which your digital value creation stands or falls.
What we can do for you
Let us evaluate your data infrastructure before you start your next AI experiment. In a non-binding discussion, we will check your data readiness and outline the architectural path to scalable AI value creation. We look forward to the dialogue with you.
