By Robbie Jameson, CEO & Co-founder, Tale of Data
At Techinnov 2026 in Paris, I had the same conversation about a dozen times.
Different people. Different industries. Same fundamental problem.
Everyone is investing in AI. But the question nobody is really asking is: what happens when the data going into those models is wrong?
Here is the honest answer: AI does not fix bad data. It amplifies it.
There is a widespread belief that AI will somehow clean up, correct, or compensate for data quality issues. It will not.
AI amplifies the errors that already exist in your data. It repeats them, reformulates them, and presents them with the confidence of a system that has no way of knowing the source was flawed to begin with.
"AI will not solve the quality problems that exist in the data. It will amplify the errors — repeat them, reformulate them. But AI is not going to correct that information." — Robbie Jameson, CEO, Tale of Data
For data to be useful to AI, it fundamentally needs to be of the right quality before it reaches the model. Not after. Not "good enough." Right.
This is not a technical opinion. It is a structural reality. Gartner projects that 60% of AI projects will be abandoned by 2026 — not because the algorithms are wrong, but because the data feeding them is not ready.
This is the question I asked at Techinnov. And the answer I heard most often was both honest and alarming.
Everyone. Which means no one.
In most organizations, data quality is nobody's job title and everybody's problem. The data engineer handles it when there is time. The business analyst works around it. The DSI inherits the scripts from three years ago that nobody fully understands.
Data quality is currently the number one problem preventing organizations from properly using their data. It is the topic that creates roadblocks, makes projects fail, and is — at the same time — the most poorly addressed issue across companies of all sizes.
When ownership is shared by everyone, accountability belongs to no one. And that is exactly how you end up with 70% of enterprise data sitting unused, inaccessible, and unfit for purpose.
Tale of Data did not start from a product idea. It started from a failure.
We experienced major difficulties on a business data project — inconsistencies, duplicates, missing values, formats that made no sense. The kind of data problems that are invisible until they are catastrophic.
What we found was that the technical solution alone was not sufficient. The tools existed. The problem was that the people who understood the data — the business teams — had no way to participate in fixing it. Data quality was locked inside the IT department and the hands of engineers.
"That was the north star of the company from the beginning: enable business teams to participate in data quality — which was previously the exclusive domain of IT." — Robbie Jameson, CEO, Tale of Data
Tale of Data enables business teams and data teams to collaborate on data quality in a no-code environment. It opens the black box of enterprise data — making it traceable, correctable, and ready for use by AI and by humans.
AI tools are multiplying. Agents, new architectures, new products every week. The market is in constant motion.
But the foundation does not change.
Without reliable data, none of it works. The involvement of business teams in data quality remains necessary regardless of what the model underneath looks like. The platform we provide today is built precisely to address that constant — not the trends on top of it.
I was asked at Techinnov to define AI-ready data in one sentence. Here is the full version.
AI-ready data meets three conditions:
1. It is accessible. About 70% of enterprise data is not exploited, not used, and not accessible. If AI cannot find the data, it will never use it. Availability is the first condition — and it is more often a governance problem than a technical one.
2. It is aligned with a use case. Data does not need to be perfect in the abstract. It needs to be compatible with a well-defined use case. That requires reflection on what you actually want to do with the data, and preparation that serves that specific purpose.
3. It is clean. No duplicates. No inconsistencies. No missing values. No errors that will propagate through the model and surface as confident wrong answers. The data needs to be at the right quality level to do the work planned.
AI will not save a project built on bad data.
But good data — accessible, aligned, and clean — will make every AI initiative you run significantly more likely to succeed.
That is not a prediction. It is what we see every day working with organizations like TotalEnergies, BNP Paribas, and France Travail.
→ Want to know where your data stands? Request a free Data Quality Flash Audit
Robbie Jameson is CEO and Co-founder of Tale of Data, an AI-powered Data Quality platform adopted by leading organizations across Europe. This editorial is based on a live interview recorded at Techinnov 2026 in Paris.