The age of agentic AI is nearly here. Representing a new evolution in AI technology, agentic AI enables systems to perform tasks with greater autonomy and freedom of action. This means that the need for human oversight is reduced, and human workers can instead focus on approving, validating, improving or fine-tuning AI actions, rather than executing them from scratch.

Imagine an AI system that can autonomously manage supply chains to prevent stockouts, or streamline customer service inquiries by acting on real-time data, without needing constant guidance. These and many other use cases are being enabled by Agentic AI.

A recent Capgemini survey found that 10% of enterprises already use some form of agentic AI, and over half plan to roll it out within the next year. But for these integrations to succeed as businesses move toward more autonomous AI-driven processes, the technology behind agentic AI must be integrated and flexible, and not confined by closed systems.

Mike Capone, CEO of Qlik, a leading provider of data integration, data quality, analytics and AI, underscores that agentic AI represents a turning point in how businesses handle data and extract insights. Instead of requiring specialized data science knowledge, agentic AI can put actionable intelligence directly into the hands of decision makers.

“Historically, AI was the domain of what we call ‘white-coat data scientists,’” says Capone. “Now, with agentic AI, the goal is to harness the power of AI to build these agents, then bring their output into workflows in real time, inside day-to-day work patterns, so it becomes incredibly useful.”

An ‘any source, any target’ approach to data

For agentic AI to work effectively, data cannot be siloed. Picture an AI-driven demand forecasting system for a retail chain. To make accurate predictions and manage inventory, the AI needs data from diverse sources: seasonal trends, past sales records, social media sentiment and even weather forecasts. If these data points remain isolated, the AI agent can’t see the full picture, leading to inaccurate forecasts. But when integrated seamlessly, the AI can effectively anticipate demand shifts and automate restocking, ensuring that products are available when customers need them.

Qlik’s approach to this challenge is what Capone calls “any source, any target”: collecting and connecting data from any source, regardless of where it’s generated. “That can be on-premises, in the cloud, on social media platforms—wherever the data resides, we can bring it in,” he explains.

The Qlik Talend Trust Score™ for AI assesses proprietary data for trustworthiness, timeliness and impartiality, ensuring that companies receive reliable outputs when their data is fed into AI systems—such as Qlik’s own AI-powered analytics—delivering better AI outcomes.

The power of partnerships

For agentic AI to reach its full potential, it must be built on strong partnerships and open integrations that support seamless data flow across systems. Many people spend much of their workday communicating both internally and externally to act on information and make decisions. In the same way, agents need the right environment to thrive—one with strong connectivity and interoperability, and an open approach to collaborating with diverse technologies.