For years, data and analytics leaders have focused on helping their organizations make better decisions by learning from the past.
But in 2026, that may no longer be enough.
According to Luis Felipe Guzmán, Data & AI Leader at IBM in Mexico, the next stage of maturity will be shaped by the ability to capture data in real time, act on it quickly, and scale AI in a secure, governed way.
Guzmán says organizations have already invested “significant time, money, resources, and talent” into analytics based on historical data. The next differentiator will be the ability to combine that foundation with real-time data streaming, AI platforms, stronger security, and governance that can support increasingly autonomous systems.
“Capturing data in real time and acting on it immediately can make the difference between a good customer experience and an exceptional one,” Guzmán says.
1. AI Security Becoming a Core Data Leadership Priority
The priority Guzmán highlights is AI security. When AI is central to business performance, securing AI is a business continuity issue.
As organizations embed AI into more workflows, decisions, and customer-facing processes, the risk profile changes. Data and analytics leaders will need to think beyond traditional data protection and consider how AI systems themselves can be secured.
That includes protecting the data that feeds AI models, the platforms used to deploy them, and the decisions or recommendations they generate. It also means preparing for a world where AI systems are connected to operational processes, customer interactions, and, increasingly, autonomous agents.
For Guzmán, this is not a peripheral issue. Organizations are facing growing threats, and securing AI systems will be critical as adoption accelerates.
In practice, this means AI security can no longer sit solely with technical teams, it will require cross-functional collaboration between data leaders, security teams, risk leaders, legal teams, and business executives.
2. Scaling AI Requires Unified Platforms
The second priority is the ability to expand and scale AI adoption across the enterprise.
Many organizations have already experimented with AI in isolated use cases. Guzmán points to a common challenge: organizations are often working with different AI solutions for different use cases. That fragmentation can make it harder to govern AI consistently.
“Organizations need platforms that allow them to securely adopt new use cases and scale them effectively,” he says.
For data and analytics leaders, the next phase of AI maturity will depend less on experimentation and more on the operating model around it. Organizations will need platforms that make it easier to deploy AI securely, reuse capabilities, monitor performance, and extend successful use cases across functions.
This also has implications for talent. Guzmán identifies skills and reskilling as one of the major challenges facing the industry in 2026. People across the organization will need to understand how AI works, how it can help them, and how it can be scaled responsibly.
3. Governance Turns AI Into Sustainable Value
Organizations cannot improve what they do not measure. If leaders do not understand what is happening inside their AI systems, they cannot manage risk, evaluate performance, or ensure AI is delivering value in a responsible way.
“If we are not measuring what is happening with our AI systems, we have no way of understanding how to improve them,” Guzmán points out.
Guzmán notes that relatively few organizations are seriously addressing AI governance today, although some in highly regulated industries have made more progress. In his view, adoption of AI governance capabilities remains at an early stage, but organizations will need to strengthen these capabilities significantly in the years ahead.
For senior data and analytics leaders, governance should not be seen as a brake on innovation. Done well, it is what allows innovation to scale and gives organizations the confidence to deploy AI more broadly.
Real-Time Data Will Connect the Next Wave of AI
Although Guzmán identifies AI security, scale, and governance as the three trends that will take data and analytics to the next level, real-time data runs through the entire conversation.
He argues that data is now understood as a strategic asset at the center of the business. The next stage will involve incorporating not only historical data, but also real-time data from systems, people, and connected devices. Combined with agentic AI, this will allow organizations to respond faster and act on insights as they emerge.
“Data sits at the center of the business. It is a differentiator and a valuable strategic asset,” Guzmán says.
But the same capabilities that make real-time AI powerful also make security, governance, and scale more important. The faster systems act, the more confidence leaders need in the data, the models, and the controls behind them.
For data and analytics leaders, 2026 will be about turning maturity into momentum. Historical analytics will remain essential, but connecting real-time data, secure AI adoption, scalable platforms, strong governance, and skilled teams will take leading organizations to the next level.
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