Automation has undoubtedly been the tipping point for many companies because of the opportunities it has offered and continues to offer to improve efficiency and reduce operating costs. However, the most recent technological evolution has led to a paradigm shift: simply automating processes is no longer enough. It becomes increasingly necessary to make them intelligent and capable of generating decision value.
It is in this context that Artificial Intelligence, integrated into enterprise information systems, assumes a strategic role in creating and enhancing true decision intelligence, an extended capacity for real-time analysis, prediction and action.
From data to competitive advantage: this is Decision Intelligence
The starting point of decision intelligence is data.
Gartner defines it¹ this way:
Decision Intelligence (DI) is a practical discipline that improves decision making by explicitly understanding and elaborating how decisions are made and how outcomes are evaluated, managed and refined through feedback.
Its genesis is, so to speak, a natural consequence of the marketplace: companies today collect huge volumes of information from ERP, CRM, IoT platforms and supply chain systems.
However, data availability does not automatically translate into useful knowledge. Information fragmentation and poor data quality limit managers’ decision-making ability. With this in mind, a supply chain must also be built for decision support, ranging from data collection to data organization.
Today there are digital solutions that make it possible to build a unified data-fabric infrastructure that can integrate heterogeneous sources and return a consistent view of the enterprise. This approach creates a Single Source of Truth, a single shared information base that becomes the prerequisite for any intelligent automation process.
The goal is both simple and ambitious: improved reporting is the starting point, but the ultimate goal is to create an ecosystem in which data can be directly used to simulate scenarios, optimize resources, and support strategic decisions with predictive and prescriptive analytics tools.
From operational automation to decision intelligence
The next step is to transform automation from an executive mechanism to a cognitive lever. Decision intelligence combines machine learning, process modeling, and real-time analytics to provide concrete answers to complex problems, such as production planning, demand management, or dynamic allocation of financial resources. In fact, it is theapplication of some of the main paradigms of Artificial Intelligence to a new context: that of using data to support strategic decisions.
In the SAP context, this evolution is embodied in solutions integrated into the Business Technology Platform, enabling data, processes and artificial intelligence to be orchestrated in a unified environment, even in the presence of third-party applications or environments. The goal is to enable a continuous cycle of learning and improvement: data feed predictive models, models produce decisions, and decisions generate new data that further refine predictions.
The role of generative AI and intelligent co-pilots
The introduction of interactive side-by-side tools such as generative AI co-pilots marked a further leap forward. These tools are designed to enable managers to talk directly with business data in natural language, gain predictive insights, formulate simulation scenarios, and receive operational recommendations. Their progressive side-by-side with other tools is demonstrating how access to disintermediated data can lead to important decision-making benefits, moving progressively away from the static logic of reports and reports.
A simplified interaction, in short, that turns AI into a natural extension of decision making. Thanks to contextual processing and predictive capabilities, agents do not just respond, but anticipate needs, highlight critical issues and reveal hidden opportunities in information flows.
This is a model of collaborative AI, in which technology does not replace human expertise, but amplifies it, reducing analysis time and improving the quality of decisions.
Decision intelligence in business functions
The impact of decision intelligence extends horizontally to all departments and business functions. Let’s look at some concrete examples.
In finance, predictive analytics algorithms support CFOs in financial planning and risk management by enabling them to simulate the effects of strategic decisions in real time.
In manufacturing, artificial intelligence improves production planning, supply chain optimization, and predictive maintenance.
In HR, new management tools integrate advanced analytics to analyze organizational climate, predict turnover rates and optimize career paths.
Sustainability also benefits from decision intelligence: it enables monitoring ESG metrics, calculating carbon footprints, and correlating sustainability data with operational and financial performance.
Data, AI, and integrated governance
Turning decision intelligence into concrete value requires interoperable systems and robust data governance. Indeed, artificial intelligence requires complete, accurate, and up-to-date datasets. Automation of decision-making processes must not compromise transparency: every AI-supported decision must be able to be explained, tracked, and verified.
This principle is consistent with the most recent European legislation on Artificial Intelligence and sustainability, which promotes the adoption of “reliable” and controllable intelligent systems. Cite, for example, the AI Act, which includes guidelines within it regarding all these issues.
Toward the cognitive enterprise
The combination of automation, generative AI and integrated platforms is leading companies toward a new organizational form: the cognitive enterprise, capable of Interpret real-time data and adapt to changes in context.
In this model, decision intelligence becomes the engine that links strategy and operations. Decisions are no longer made based on hunches or retrospective reports: instead, they are made through continuous analysis and up-to-date predictions. The ability to correlate financial, production and environmental metrics enables managers to optimize results while maintaining a sustainable and compliant approach.
Decision Intelligence: automation gets smart
Moving from automation to decision intelligence means making a cultural leap before a technological one. It is a process that requires robust data infrastructures, integrated AI models, and a business mindset geared toward continuous learning.
Regesta supports companies on this journey with solutions that combine data management, artificial intelligence and process expertise, turning data complexity into a tangible competitive advantage. Decision intelligence is the future becoming the present: the new standard for companies that want to govern change rather than undergo it.
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Source¹: Gartner.com
