As businesses rush to harness artificial intelligence, many are discovering that flashy prototypes aren’t enough to deliver long-term value. The real obstacle? Poor data quality. Without strong foundations, even the most advanced AI systems struggle to make the leap from experimentation to production.
That’s the view of Martin Frederik, regional leader for the Netherlands, Belgium, and Luxembourg at data cloud company Snowflake.
Frederik put it simply: “There’s no AI strategy without a data strategy. AI apps, agents, and models are only as effective as the data they’re built on.”
Why Many AI Projects Stall
Frederik sees a common pattern: an AI proof-of-concept excites teams but fails to generate revenue. The problem, he says, often lies in treating AI as the end goal rather than a tool to achieve business outcomes.
“AI is not the destination – it’s the vehicle,” he explained.
Projects tend to falter when data is fragmented, departments don’t collaborate, or business goals aren’t clearly aligned.
While studies suggest that as many as 80% of AI projects never reach production, Frederik frames this as part of the natural learning curve. Companies that persist—and invest in data governance—are seeing strong rewards. Snowflake research shows that 92% of firms are already generating returns, with every £1 invested in AI bringing back £1.41 in savings and new revenue.
Beyond Technology: The Human Factor
For Frederik, AI success depends as much on culture as it does on infrastructure. The key is democratizing access to data so it doesn’t sit in silos or remain locked away with data scientists.
“With the right governance, AI becomes a shared resource rather than a siloed tool,” he said.
A single, trusted source of information allows teams to move past internal disputes and make decisions faster, together.
This requires aligning “people, processes, and technology” so that AI becomes woven into the fabric of daily operations, rather than an isolated experiment.
The Rise of Autonomous AI Agents
Looking ahead, Frederik points to a new frontier: AI that can reason across all types of data—structured or unstructured. Since unstructured information like documents, emails, and videos makes up as much as 90% of corporate data, this represents a major leap forward.
Emerging AI agents can now process these formats and answer complex queries in plain language. Frederik calls this shift “goal-directed autonomy.” Unlike earlier AI systems that needed constant direction, these new agents can take a business objective, determine the steps, and deliver results independently—whether that means generating code, pulling data from apps, or synthesizing insights.
By automating repetitive tasks such as data cleaning and model tuning, these tools allow data scientists and business leaders to focus on strategy and innovation. As Frederik puts it, the future of AI is about elevating talent “from practitioner to strategist.”
The Bottom Line
AI’s potential to reshape industries is real, but its success depends on more than algorithms. Strong data governance, cultural readiness, and the right infrastructure remain essential for turning AI hype into measurable business impact.
For Frederik, the message is clear: the road to AI-driven growth begins with trusted, high-quality data. Companies that get this foundation right will be best positioned to capture the next wave of intelligent automation.
