As companies race to implement AI, many are finding that project success hinges directly on the quality of their data. This dependency is causing many ambitious initiatives to stall, never making it beyond the experimental proof-of-concept stage.
So, what’s the secret to turning these experiments into real revenue generators? AI News caught up with Martin Frederik, regional leader for the Netherlands, Belgium, and Luxembourg at data cloud giant Snowflake, to find out.
“There’s no AI strategy without a data strategy,” Frederik says simply. “AI apps, agents, and models are only as effective as the data they’re built on, and without unified, well-governed data infrastructure, even the most advanced models can fall short.”
Improving data quality is key to AI project success
It’s a familiar story for many organisations: a promising proof-of-concept impresses the team but never translates into a tool that makes the company money. According to Frederik, this often happens because leaders treat the technology as the end goal.
“AI is not the destination – it’s the vehicle to achieving your business goals,” Frederik advises.
When projects get stuck, it’s usually down to a few common culprits: the project isn’t truly aligned with what the business needs, teams aren’t talking to each other, or the data is a mess. It’s easy to get disheartened by statistics suggesting that 80% of AI projects don’t reach production, but Frederik offers a different perspective. This isn’t necessarily a failure, he suggests, but “part of the maturation process”.
For those who get the foundation right, the payoff is very real. A recent Snowflake study found that 92% of companies are already seeing a return on their AI investments. In fact, for every £1 spent, they’re getting back £1.41 in cost savings and new revenue. The key, Frederik repeats, is having a “secure, governed and centralised platform” for your data from the very beginning.
It’s not just about tech, it’s about people
Even with the best technology, an AI strategy can fall flat if the company culture isn’t ready for it. One of the biggest challenges is getting data into the hands of everyone who needs it, not just a select few data scientists. To make AI work at scale, you have to build strong foundations in your “people, processes, and technology.”
This means breaking down the walls between departments and making quality data and AI tools accessible to everyone.
“With the right governance, AI becomes a shared resource rather than a siloed tool,” Frederik explains. When everyone works from a single source of truth, teams can stop arguing about whose numbers are correct and start making faster and smarter decisions together.
The next leap: AI that reasons for itself
The true breakthrough we’re seeing now is the emergence of AI agents that can understand and reason over all kinds of data at once regardless of structure quality; from the neat rows and columns in a spreadsheet, to the unstructured information in documents, videos, and emails. Considering that this unstructured data makes up 80-90% of a typical company’s data, this is a huge step forward.
New tools are enabling staff, no matter their technical skill level, to simply ask complex questions in plain English and get answers directly from the data.
Frederik explains that this is a move towards what he calls “goal-directed autonomy”. Until now, AI has been a helpful assistant you had to constantly direct. “You ask a question, you get an answer; you ask for code, you get a snippet,” he notes.
The next generation of AI is different. You can give an agent a complex goal, and it will figure out the necessary steps on its own, from writing code to pulling in information from other apps to deliver a complete answer. This will automate the most time-consuming parts of a data scientist’s job, like “tedious data cleaning” and “repetitive model tuning.”
The result? It frees up your brightest minds to focus on what really matters. This elevates your people “from practitioner to strategist” and allows them to drive real value for the business. That can only be a good thing.
Snowflake is a key sponsor of this year’s AI & Big Data Expo Europe and will have a range of speakers sharing their deep insights during the event. Swing by Snowflake’s booth at stand number 50 to hear more from the company about making enterprise AI easy, efficient, and trusted.
See also: Public trust deficit is a major hurdle for AI growth
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