Agentic AI: from data to action

Artificial intelligence is entering a new phase, evolving from static, specialized systems to solutions that can manage complex processes autonomously. Agentic AI is at the heart of this transformation, leveraging advanced models to analyze data, identify decision paths and perform automated actions based on established goals.
According to a recent analysis, interest in Agentic AI has grown 500 percent since 2020, demonstrating the growing focus on systems that integrate automation and intelligence into decision making. It is not just about performing tasks, but using data and models to optimize strategies in real time.

Agentic AI is distinguished by some key features that make it particularly effective in complex environments:

• Decision Autonomy: Agentic systems use contextual data and analytical models to identify the most effective path toward achieving goals.
• Advanced Data Processing: With their ability to extract relationships and correlations from large volumes of data, these agents produce outputs that support decision making.
• Multi-Agent Collaboration: In complex scenarios, multiple agents can work in parallel, sharing information to optimize flows and outcomes.

A practical example is logistics optimization systems, where agents analyze data such as transit times, inventory levels, and demand to plan more efficient delivery routes.

The integration of Agentic AI and Decision Intelligence offers a new way to address decision complexity. Decision Intelligence relies on systematic use of data to optimize strategic, operational, and tactical decisions, while Agentic AI acts as an executive engine to translate insights into actions.
For example, an AI agent can use predictive models to simulate market scenarios, identify opportunities, and suggest strategies that minimize risk. These systems not only accelerate decision making, but also improve the accuracy of choices through deep analysis of information.
According to recent studies, companies that use Agentic AI in conjunction with Decision Intelligence report a 30 percent increase in the speed of strategic decisions while improving efficiency and adaptability

In the business context, Agentic AI represents a unique opportunity to transform complex data into concrete actions. Its application ranges from strategic planning to operational management, providing measurable benefits in terms of efficiency and accuracy.
For example, in a supply chain, an agentic system can monitor real-time data, predict changes in demand and optimize resource allocation. Similarly, in marketing, agents can analyze consumer behavior to trigger personalized campaigns and improve return on investment.
By 2030, Agentic AI adoption is expected to increase business productivity by 20 percent, providing a significant competitive advantage in sectors such as finance, healthcare, and industrial manufacturing.

Despite the obvious benefits, Agentic AI implementation comes with some challenges that need to be addressed:

• Transparency and oversight: It is essential to ensure that agentic systems operate transparently and meet strict ethical standards.
• Scalable infrastructure: Companies must invest in digital platforms that support the integration and scalability of AI agents.
• Data management: Data quality and protection remain key elements to make the most of Agentic AI's potential.

Agentic AI is a paradigm that is changing the way data is used to make decisions. Its ability to combine advanced models and automation makes it a valuable ally in addressing the complexities of a data-driven world. Organizations that adopt Agentic AI today will be better positioned to meet the challenges of tomorrow, leveraging technology to create opportunity and innovation at scale.