AI Maturity Begins With Boring Data Work 

When I look at media coverage, I see that each wave of AI arrives with its own inflated expectations. As an analyst who support customers in finding the right architecture and software for their data and analytics challenges, I often encounter the same patterns in my discussions.

The rise of ML was followed by the desire for a crystal ball. The advent of GenAI created the hope that knowledge could be produced almost magically from information, and AI agents are now presented as tirelesscopilots ready to take on everything.

But the organizsations that actually create value from AI investments understand both the potential and the hard work required. They know that real progress comes from building data pipelines, monitoring data quality, discussing responsibilities, ensuring security without obstructing business, and educating users.

Our new Data, BI and Analytics Trend Monitor 2026 confirms this picture. We asked 1,579 data professionals about their most important topics. The top positions are dominated by foundational capabilities:

• Data quality management,
• Data security & privacy,
• Data driven culture,
• Data & AI governance and
• Data & AI literacy.

These five form the core of what organizations say is most important in 2026. Only after these fundamentals come technology driven trends such as AI and ML, generative AI, and agentic AI. The market may only be talking about AI, but its priorities show that it is still working on data.

How important are the trends in data, BI and analytics?

Why boring data work comes first

AI is a multiplier. It multiplies value if the data is good. It multiplies risk if the data is bad. Poor or inconsistent data leads directly to biased models, unreliable answers and manual rework. That is why data quality has returned to the number one position in 2026. Organizations have learned that AI success depends on a stable and trustworthy data foundation.

The same logic applies to data security and privacy. In a time of increasing regulation and cyber risk, nobody will scale AI without knowing where the data comes from, who is allowed to access it and how it is protected. This is often what slows projects down, but it is also what creates acceptance for and makes it sustainable inside the company.

The human part of AI readiness

One of the most striking findings this year is the strong position of human centric topics. Culture, governance and literacy remain firmly in the top group. This tells us something important. Tools do not create maturity. People do. If employees cannot interpret AI outputs, if product owners do not know who owns which data, if management does not act on insights delivered, then AI stays stuck in pilots.

AI readiness is therefore not primarily a model question. It is an organizational, process and policy question. This may sound less exciting than the latest AI feature, but it is what makes AI reliable, explainable and valuable.

Why this is good news

Some see these results and think the market is slow. I do not agree. The market is maturing. Five or six years ago many organizations tried to jump directly to advanced analytics. Now they understand that they need quality, security, governance, and literacy first. This is the correct order. If you are doubling down on strengthening your data foundation in 2025, you are not behind. You are doing the work that protects your future AI investments.

What to do with this as a data leader to insure sustainable returns on investment

• Treat data quality as a funded initiative, not as an IT side task
• Tie every AI project to governance and security from day one
• Invest in literacy so business users understand and apply AI effectively
• Explain to management that the unglamorous work is what prevents AI from going down with flying colors

This is exactly what the Trend Monitor shows. AI is rising in importance, but the organizations that will succeed in leveraging its true potential are the ones that take utmost care of the foundations.