Usually, the problem is not the lack of data. On the contrary. Data exists, in abundance. The challenge lies in how it is used. Or, rather, in how it’s not always connected, contextualized, or on time to support a relevant decision.
For years, supply chain teams have learned to compensate for this situation with experience, intuition, and a lot of manual effort. But the current context demands something muchbetter like informed decisions, shared and aligned with the reality of the business. Today, AI can turn connected data into early warnings, scenarios and recommendations, but only if it rests on reliable data and well-understood processes. Gartner’s April 2026 survey of senior supply chain leaders points the same way: 56% of chief supply chain officers say that integrating AI with legacy systems and processes is a major challenge, and Gartner notes that the greatest friction point in scaling AI is not the technology itself but the environments in which it is deployed.
In the majority of companies, sales data lives in SAP S/4HANA, production is managed in specific systems, logistics depends on external platforms, and demand is influenced by signals coming from multiple channels. All of this coexists, but is not always integrated.
The result is already established: inventories that grow “for safety,” late reactions to market changes, or meetings in which each area works with different figures. It is not a technical problem; it is a problem of trust in the information.
This is where the conversation should start: not with the tool, but with the decision. Which decisions have the greatest impact? What information do they need? What stands in the way? Only then does technology makes sense.
A shared data foundation changes the conversation. Platforms such as SAP Business Data Cloud are designed to connect SAP and non-SAP data while preserving its business context. A shared data foundation changes the conversation. Platforms such as SAP Business Data Cloud are designed to connect SAP and non-SAP data while preserving its business context. Instead of spending time reconciling figures, teams can focus on understanding what is happening and what decisions have the greatest impact.
The potential of this approach becomes especially visible in core processes such as the supply chain, where anticipation makes the difference. When data is integrated and contextualized, the organization gains a common base that allows:
The goal is not to take people out of the decision, but to give them better information, earlier, and to automate only what is routine, low-risk and governed. Gartner’s September 2026 outlook confirms this direction: by 2030, only 5% of organizations implementing supply chain planning automation will make at least 10% of their planning decisions autonomously, because human oversight remains necessary, especially for strategic decisions such as network design and inventory policy.
Connected data is the foundation; AI is what turns it into action. Applied on a reliable data layer, AI can detect weak demand signals earlier, run scenario analyses continuously, flag risks before they materialize and recommend actions, explaining the reasoning behind them. The planner spends less time collecting and reconciling data, and more time interpreting insights, challenging assumptions and making decisions.
Not every decision deserves the same level of automation. Gartner recommends starting from the decision itself: classifying it as strategic, tactical or operational, and determining whether AI should support, augment or automate it based on its value, complexity, risk and need for human judgment. It is the same logic we follow at Avvale: we analyze the process and the decision first, design the data and architecture foundations it requires, and only then apply AI, with clear governance and with people firmly in charge of the outcome.
As we know, demand planning is one of the clearest examples of this change. In sectors such as consumption, retail, or industry, it is still common to rely on historicals and manual corrections to anticipate the future. However, market behavior no longer responds to stable patterns; that is why when data is connected intelligently, planning stops being a purely retrospective exercise. Teams can incorporate new signals, contrast them with real production and logistics capacities, and evaluate different scenarios before deciding. The planner’s experience remains fundamental, but now it is backed by coherent and shared information.
Consider a consumer goods company present in several countries, where demand planning has traditionally relied on historical sales and manual adjustments. With a connected data foundation and AI-driven forecasting , the organization can:
The planner stops working with isolated spreadsheets and starts evaluating scenarios:
What happens if demand grows by 10% in a region?. Where is the greatest impact generated if a supplier is delayed?
Technology does not replace human experience, but it powers it with reliable and contextualized data.
In industrial and consumer organizations with distributed operations, the pattern is familiar: sales, production, purchasing, and logistics data managed in different systems, with manual reconciliations and limited visibility of the real impact of a decision upstream or downstream. In this context, planning becomes defensive, working capital becomes immobilized, and the service level remains vulnerable.
When these organizations consolidate demand, productive capacity, and inventory information on a unified, governed data layer, scenario analysis and impact simulations become part of everyday planning. The benefits to look for are more accurate forecasts, leaner inventory without compromising service, and faster responses to disruptions, and they should be measured on the quality of decisions rather than on the deployment of technology, as Gartner recommends. For the supply chain director, the value is not only in direct savings but in moving from putting out fires to making anticipated decisions, based on reliable data shared by the entire organization.
As it occurs in any data initiative, the platform by itself does not generate value. Gartner’s latest research makes the point: 83% of the large organizations it surveyed had already spent at least $3 million on automating supply chain planning, including AI, yet “investment alone does not create AI readiness”, and decisions, talent and technology foundations have to come first. The real impact appears when organizations identify the processes where data can make a difference, prioritize clear use cases, and accompany people in a new way of working.
The supply chain is usually one of the areas where this change is perceived sooner, because the benefits are immediate and tangible. But the approach is extrapolatable to many other key business processes.
In a context where uncertainty has become part of the day-to-day, the companies that move forward are not those that accumulate more information, but those that manage to transform it into timely decisions.
Connected data and AI help take that step, bringing data closer to the business and putting it at the service of the people who make decisions every day. Because, in the end, transformation is not just about technology. It is about people, processes, and data that, finally, begin to work in the same direction.
In an environment where uncertainty is the constant, making decisions based only on intuition is no longer enough. Therefore, at Avvale we accompany organizations to transform their data into reliable and timely decisions, connecting systems, processes, and people so that information stops being a problem and becomes a strategic advantage.
Our approach combines process analysis, solid data foundations, AI and enterprise platforms, including SAP, with real experience in operations, helping supply chain teams to anticipate, reduce risks, and optimize results. Because beyond implementing a platform, our value lies in ensuring that data works for the people who make decisions every day.