From AI Assistance to AI Action: Why Data Foundations Will Define 2026

Alexey Sidorov, Data Science Guru and Evangelist at Denodo, explores how well-governed, reusable data products are becoming the backbone of trustworthy AI, why many initiatives stalled due to weak foundations, and how expectations are resetting around value and accountability.

What opportunities do you foresee for 2026, and how do you plan to leverage them?
Looking ahead to 2026, the biggest opportunity lies in how AI is expected to evolve from a support tool into something that can take action on its own. Instead of simply responding to prompts, AI systems will increasingly handle routine tasks, coordinate processes, and support decision-making across teams. This opens the door to faster operations, fewer manual bottlenecks, and better use of human time.

However, this opportunity only becomes real if AI has access to accurate, timely, and well-governed data. Another major opportunity is the growing use of data products – clearly defined, trusted data assets that different teams can reuse. Together, these shifts allow organizations to move faster while staying controlled and accountable.

What major challenges did you encounter this year, and how did you address them?
One of the main challenges this year was the gap between AI expectations and actual results. Many projects struggled to move beyond testing stages because the underlying data was scattered, difficult to manage, or lacked clear governance. At the same time, leadership became more cautious, asking tougher questions about cost, value, and long-term impact.

This made it clear that progress wasn’t just about better tools, but better foundations. Addressing these challenges required a shift in mindset, less focus on speed and experimentation, and more emphasis on data quality, accountability, and measurable outcomes. By prioritizing realistic use cases and tightening governance, organizations were better positioned to turn AI from an idea into something useful.

Can you elaborate on your strategic partnerships this year and plans for next year?
This year showed that relying on a single platform or environment is no longer practical. Strategic partnerships increasingly focused on flexibility, working across different cloud setups, regions, and regulatory environments without forcing everything into one system. These collaborations helped reduce complexity while allowing data to stay where it makes the most sense.

Looking ahead to 2026, partnerships are expected to become even more practical and outcome-driven. The focus will be on shared standards, smoother integration, and the ability to move workloads or data without disruption. Rather than chasing scale for its own sake, partnerships will aim to support interoperability, compliance, and real-world operational needs.

What will be your primary focus areas and strategic priorities for 2026?
In 2026, the focus will shift toward making AI reliable, understandable, and genuinely useful in day-to-day work. That starts with ensuring data is ready for AI use – accurate, current, and governed from the start. Another priority will be improving how insights are delivered, moving away from static reports toward automated analysis that people can interact with through simple, conversational tools.

Just as important will be investing in people. As technology advances quickly, many teams struggle to keep up. Supporting skills development and making AI tools easier to use will be critical to closing that gap and ensuring adoption across the organization.

Are there plans to explore new markets or introduce new products/applications in 2026?
It’s important in areas where automation and real-time data can drive immediate impact. Manufacturing and supply chain operations are expected to change significantly, with systems that can adjust quickly to shifts in demand, costs, or regulations. These environments benefit most from AI that reacts in real time rather than relying on historical data alone.

There is also growing interest in more focused applications—tools designed for specific business areas like finance, operations, or risk, rather than broad, general-purpose solutions. These targeted applications tend to deliver clearer value and are easier for teams to trust and adopt.

How is your company approaching sustainability, digital transformation, or AI adoption in preparation for 2026?
The approach is becoming more grounded and responsible. Instead of moving or copying large volumes of data, organizations are prioritizing smarter access using what already exists while reducing unnecessary processing and storage. This supports sustainability goals by lowering infrastructure use and energy consumption.

Digital transformation is also becoming more disciplined, with a strong link between technology decisions and measurable outcomes. AI adoption in particular, is tied closely to governance, transparency, and risk management. At the same time, there is growing recognition that people need support to work confidently with these tools. Training and usability are now seen as essential, not optional.