The integration of artificial intelligence into business intelligence platforms is driving a new era of augmented analytics, where automation, predictive models, and real-time decision-making are redefining business competitiveness.

Augmented analytics is establishing itself as one of the strategic pillars of digital transformation, thanks to advances in artificial intelligence applied to data analysis. Organizations are adopting new tools capable of automating analytical processes, discovering hidden patterns, and offering actionable recommendations, which accelerates decision-making based on reliable, real-time information.

In this context, the evolution of AI-driven business intelligence is enabling companies to move from a descriptive approach to a predictive and prescriptive one. Modern platforms integrate machine learning, natural language processing, and automation capabilities to democratize access to data and reduce dependence on specialized technical profiles.

Automation and democratization of data analysis

The combination of advanced algorithms and self-service tools is making it easier for business areas to explore information independently. AI enables data preparation, anomaly detection , and automatic visualization generation, reducing time and improving the quality of analysis.

In addition, augmented analytics promotes collaboration between teams by providing understandable, contextualized insights that help align strategies and optimize operations. This approach also improves data governance by integrating controls and traceability into analytical processes.

From descriptive intelligence to predictive decision-making

The use of predictive models makes it possible to anticipate market trends, identify risks, and optimize resources. Organizations are using AI to simulate scenarios, improve financial planning, and personalize the customer experience, which translates into sustainable competitive advantages.

Likewise, the ability to analyze large volumes of data in real time opens the door to more agile responses to changes in the environment, which is key in highly dynamic sectors.

Challenges: data quality, talent, and governance

Despite the benefits, adopting augmented analytics poses challenges related to data quality, legacy system integration, and the need for profiles with analytical skills and AI knowledge. Companies must invest in data culture and governance frameworks that ensure the responsible and secure use of information.

Augmented analytics on the industry agenda

Advances in artificial intelligence applied to data analysis will be discussed and debated during Big Data & AI, part of Tech Show Madrid 2026, which will be held on November 4 and 5. This event will bring together experts and industry leaders to analyze how AI is redefining data strategy and the future of business intelligence in organizations.

An AI-driven paradigm shift

Augmented analytics not only improves operational efficiency, but transforms the way businesses understand and use information. As artificial intelligence continues to evolve, data analytics is becoming a key driver of innovation, resilience, and sustainable growth in the digital economy.