AI rarely fails because of technology - but almost always because of data

“According to Gartner, 85% of all AI models and projects fail due to poor data quality or a lack of relevant data. Many companies train their (generative) AI models on incomplete, disorganised or outdated datasets, resulting in substandard results.”

This observation is confronting, but for many CIOs and CDOs it is especially recognisable. The ambitions are great, the tools impressive - and yet real impact often fails to materialise. Not because AI doesn't work, but because the basics are missing.

AI speeds up what is already there. If data is messy, AI mostly speeds up the mess.

Why AI projects are stalled

In practice, Improven sees the same pattern recurring:

AI is started when technology experiment, independent of strategy and processes
Data quality and definitions are inadequate
Ownership of data is diffuse
The organisation is not included in the use and change

The result: pilots not scaling up, models not trusted and employees dropping out. AI lingers on promise, not results.

Improven's belief: data first, then AI

Improven deliberately chooses a different order. Successful AI does not start with the model, but with the data. Our Data and AI proposition therefore focuses on the foundation of AI and data-driven working:

Data quality as a prerequisite We bring insight into data flows, definitions and quality and demonstrably improve them. No AI on polluted or fragmented data, but working from one clear truth.
Data strategy and governance before tooling Together, we determine what role data and AI play in organisational goals. With clear governance: who owns it, which rules apply, how do we ensure privacy, compliance and quality? In this, governance is not a brake, but an accelerator.
Structure and ownership Data only gets value when it is clear what it means and who is responsible for it. Improven helps organisations make that ownership explicit - across departments.

Data management as missing link under AI

The often overlooked factor in AI success is strong data management. Companies want to ‘do something with AI’, but do not realise that you first have to get a grip on your data. Data management is more than managing databases; it is an overall picture of vision, organisation and discipline around data. And let that be just the link that makes innovation sustainable.

Building blocks of data management:

Data strategy (direction) - Make sure there is a plan for the use of data in the organisation, linked to business goals. Which data is really important? Where are opportunities for value creation with data and AI? A clear data strategy prevents AI projects from becoming a wild guess become.
Data governance (agreements) - Define who for which is responsible and how decisions about data are made. For example, appoint a data owner per domain and make agreements on privacy and compliance. Clear governance means that there is oversight and steering so that everyone knows what can and cannot be done with data. Improven ensures that governance workable and “with your feet in the clay” is set up - no thick manuals in the cupboard, but concrete agreements that people can work with on a daily basis.
Data quality & definitions (basics in order) - AI is as smart as the quality of its input. Data management includes the data cleaning, enrichment and monitoring. Important here is that core concepts are unambiguous: if the concept “customer” in the CRM means something different from what it means in the billing system, then you have noise. One source of truth is the goal, with clear definitions and metadata so that everyone talks about the same data and AI models learn the right patterns.
Continuous process (culture) - Good data management is not a one-off project, but an ongoing journey. Business strategies change, market data evolves, new data sources are added. That is why Improven helps organisations to embed data management as an ongoing process - e.g. through data stewards, periodic data quality checks and employee training. It is about cultivating a culture in which data is managed and improved as a strategic asset

Why good data management makes all the difference

Those who take their data management seriously build a head start. The reward is that AI does not remain stuck as a trial balloon, but grows into a structural value creator. Unlike organisations bogged down in loose experiments, companies with their data in order are able to Roll out AI more widely and achieve lasting results.

With Improven's integrated approach - connecting business, data and IT - we ensure that AI initiatives do not founder on data quality or resistance, but are instead supported by a clear data compass. This is how we help organisations make the step from ‘we want something with AI’ to ‘AI that works’ - From hype to sustainable benefit through informed data management.

Generative AI: sober, targeted and controlled

Improven is decidedly realistic about generative AI. The technology offers opportunities, but it is not a panacea. That's why we follow one simple principle: goal before tool.

We start with concrete use cases where generative AI really saves time or increases quality. Small, manageable and measurable. At the same time, we recognise the risks: hallucinations, bias, privacy and dependence on source data. We address these upfront - not afterwards.

AI only works when people work with it

Technology and data are just two sides of the coin. The third is people. Improven guides organisations in adoption, change and embedding in processes. Training, clear agreements and integration into daily work ensure that AI does not remain a loose experiment, but becomes part of how the organisation works and decides.

From AI ambition to AI that works

The Gartner figures make one thing clear: anyone serious about using AI needs to invest seriously in data. Grip on data, processes and people determines the success of AI.

Improven helps organisations exactly there. Not by selling AI, but by making it data foundation, governance and organisation in order. Thus, AI grows from hype to tool - and from experiment to structural value.

Improven helps organisations move from ‘we want something with AI’ to ‘AI works for us’.