Unlocking the Potential of AI: Prioritizing Data Strategy for Business Success

In the competitive landscape of artificial intelligence (AI) adoption, many organizations are discovering that project success is contingent upon the quality of their data. Numerous ambitious initiatives have stalled, failing to progress beyond the experimental proof-of-concept stage.

To transform these experiments into profitable ventures, we spoke with Martin Frederik, regional leader for the Netherlands, Belgium, and Luxembourg at Snowflake, a leading player in the data cloud sector.

“There’s no AI strategy without a solid data strategy,” Frederik asserts succinctly. “AI applications, agents, and models are only as effective as the data they’re built upon, and without a unified, well-governed data infrastructure, even the most sophisticated models may fall short.”

This predicament is a familiar one for many organizations: a promising proof-of-concept captivates the team but never evolves into a tool that generates revenue. According to Frederik, this often occurs because leaders prioritize the technology over the business objectives.

“AI is not the destination—it’s the means to achieve your business goals,” Frederik advises.

Project stagnation can be attributed to common pitfalls: the project lacks alignment with business needs, teams are siloed, or data management is disorganized. While it may seem discouraging given statistics suggesting that 80% of AI projects don’t reach production, Frederik offers a different perspective. This isn’t necessarily a failure, he suggests, but “part of the maturation process.”

For those who get the fundamentals right, the rewards are substantial. A recent Snowflake study found that 92% of companies are already realizing a return on their AI investments. For every pound invested, they’re reaping a return of £1.41 in cost savings and new revenue. The key, Frederik stresses, is having a “secure, governed, and centralized platform” for data from the outset.

Even with the best technology, an AI strategy can falter if the organizational culture isn’t prepared for it. One of the biggest challenges is ensuring that quality data and AI tools are accessible to all employees, not just a select few data scientists. To leverage AI at scale, you need to build robust foundations in “people, processes, and technology.”

This involves breaking down barriers between departments and making high-quality data and AI resources available to everyone.

“With the right governance, AI becomes a shared resource rather than a siloed tool,” Frederik explains. When teams work from a single source of truth, they can cease debating over conflicting data and start making faster, smarter decisions together.

The significant advancement we’re witnessing now is the emergence of AI agents capable of understanding and reasoning over various types of data, regardless of structure or quality—from neatly arranged columns in a spreadsheet to unstructured information in documents, videos, and emails. Given that this unstructured data constitutes 80-90% of a typical company’s data, this is a significant leap forward.

New tools are enabling staff, regardless of their technical proficiency, to pose complex questions in plain English and receive answers directly from the data.

Frederik explains that this marks a shift towards what he calls “goal-directed autonomy.” Until now, AI has been a helpful assistant requiring constant direction. “You ask a question, you get an answer; you ask for code, you get a snippet,” he notes.

The next generation of AI differs. You can assign an agent a complex goal, and it will determine the necessary steps on its own—from writing code to integrating data from other applications to deliver a comprehensive response. This will automate the most labor-intensive aspects of a data scientist’s job, such as “tedious data cleaning” and “repetitive model tuning.”

The outcome? It allows your most talented minds to focus on what truly matters. This elevates your people “from practitioner to strategist,” enabling them to generate real value for the business. That can only be a positive development.

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. Visit Snowflake’s booth at stand number 50 to hear more about making enterprise AI easy, efficient, and trustworthy.

Additional reading: Public trust deficit is a significant hurdle for AI growth