For decades, enterprise planning has been built on structured forecasting cycles, spreadsheet-based analysis, and manually coordinated execution processes. These approaches were developed for a business environment characterized by relative stability, predictable demand patterns, and manageable data volumes. Within that context, periodic planning and human-driven interpretation were sufficient to guide operational and financial decisions. But that environment no longer exists.
Modern enterprises operate amid continuous volatility. Demand signals shift rapidly, supply chains face persistent disruption, cost structures fluctuate in real time, and data complexity continues to expand across systems and functions. Despite these realities, many organizations still rely on reactive, spreadsheet-driven workflows to translate forecasts into action. The result is an increasingly visible disconnect between insight and execution. By the time plans are reviewed, interpreted, and implemented, underlying conditions may already have changed.
As the scale and speed of decisions increase, traditional planning models struggle to keep pace. Enterprises require more than improved forecasting accuracy; they require the ability to continuously sense change, evaluate tradeoffs across cost, service, risk, and capacity, and execute decisions at operational speed.
This need is driving the transition toward autonomous decision intelligence (ADI). It integrates Artificial Intelligence with behavioral economics and decision science to automate complex business decisions at scale. Rather than treating planning as a periodic analytical exercise, ADI embeds prescriptive analytics, scenario modeling, and real-time decision automation directly into enterprise operations. Enabling organizations to move beyond insight and toward adaptive execution at scale.
The shift to autonomous decision-making represents a structural evolution in enterprise performance management. In the following discussion, we examine why reactive planning models are reaching their limits, what autonomous decision intelligence looks like in practice, and how leading organizations are translating continuous decision-making into measurable operational and financial outcomes.
We asked Gonzalo Benedit, Chief Revenue Officer at Aera Technology, to explain how enterprises are transitioning from reactive planning to autonomous decision intelligence.
Gonzalo Benedit is the Chief Revenue Officer at Aera Technology, the leader in decision intelligence and creator of Aera, the first decision intelligence agent. For nearly 20 years, Gonzalo has been supporting enterprises across the globe in their goals to embrace new technologies and navigate digital transformation journeys. Prior to Aera, Gonzalo served as President for Workday International covering both EMEA and Asia. He has also held executive positions at SAP worldwide, including Managing Director for SAP Mexico and Chief Operating Officer for SAP EMEA.
Question: From your perspective, what is the biggest limitation of traditional, reactive planning models today?
Gonzalo Benedit, Aera Technology:
“The biggest limitation of traditional, reactive planning models is that they stop at forecasting and insight, leaving execution and adaptation to people. Many planning tools, particularly in supply chains and operations, operate in periodic cycles, rely heavily on historical data, and require manual interpretation and follow-through.
As demand shifts, supply disruptions emerge, or costs fluctuate, plans quickly become outdated. This creates friction between analysis and action, slows response times, and limits visibility into critical tradeoffs between cost, service levels, risk, and capacity across the enterprise.”
The result is a widening gap between insight and execution. Enterprises may have powerful forecasting tools, but when human interpretation and manual workflows are required to operationalize those insights, decision velocity slows. In volatile environments, that delay can mean lost revenue, excess inventory, missed service levels, and mounting operational risk. This is the breaking point many organizations are now confronting.
Question: How does autonomous decision intelligence help organizations move from insights to action at scale?
Gonzalo Benedit, Aera Technology:
“Autonomous Decision Intelligence helps organizations move from insight to action by shifting planning from process-driven workflows to continuous, adaptive decision-making. Traditional AI and planning tools can surface insights or predictions, but they still rely on people to interpret results and take actions. Decision intelligence, and autonomous agents in particular, change that model.
At Aera Technology, we designed Aera, the first decision intelligent agent, and our purpose-built platform, Aera Decision Cloud™, to support autonomous decision-making at scale.
Instead of producing plans that must be manually implemented and tracked, Aera evaluates options in real time, recommends the best actions, executes them, and continuously learns from the outcomes to improve future decisions. The result is faster, more adaptive decision-making that keeps pace with how the business actually runs.
This doesn’t replace planners or operators. It amplifies their ability to design, govern, and scale decisions, allowing humans to focus on judgment and strategy while the system handles speed, complexity, and execution.”
This distinction is critical. ADI is not about replacing human expertise; it is about augmenting it. By automating high-volume, time-sensitive decisions and continuously learning from outcomes, enterprises can scale decision-making in ways spreadsheets and static planning cycles simply cannot.
Question: What role do prescriptive analytics and scenario modeling play in improving decision speed and accuracy?
Gonzalo Benedit, Aera Technology:
“Prescriptive analytics and scenario modeling are critical because they allow organizations to move faster without sacrificing decision quality. Instead of analyzing reports after the fact, decision intelligence continuously detects changes, evaluates options, and quantifies tradeoffs in real time.
For example, when a gap emerges, an autonomous decision agent can immediately assess multiple scenarios; test feasibility across supply, finance, and operations; and recommend the best path forward with clear visibility into impact and risk. This dramatically compresses decision cycles, that once took days or weeks, into minutes.
Just as important, scenario modeling becomes part of everyday operations rather than a periodic exercise. Leaders can explore pricing, sourcing, or demand scenarios as conditions change, apply human judgment where it matters most, and act with confidence. Over time, as the system learns from outcomes, decisions become faster, more accurate, and more consistent across the enterprise.”
In practice, this means inventory planners can evaluate supply constraints instantly. Finance leaders can assess cost and working capital implications in parallel. Operations teams can act immediately rather than waiting for the next review cycle.
Question: Where are you seeing the most immediate impact of autonomous decision intelligence across enterprise operations?
Gonzalo Benedit, Aera Technology:
“We’re seeing the fastest impact of autonomous decision intelligence in areas where decisions are high-volume, time-sensitive, and constrained by cost, service levels, or availability. In these environments, even small delays or misalignment quickly turn into lost revenue, excess inventory, or waste.
Operationally, planners and inventory managers are spending far less time on manual tracking and spreadsheet work, and more time executing strategic actions. That shift to faster decisions, better outcomes, and improved ways of working is where decision intelligence delivers value first and builds momentum toward broader autonomy.
For example, a global leader in the spirits industry automated demand planning with Aera, improving forecast accuracy, cutting forecast error by roughly 50%, and enabling multi-million-dollar inventory reductions.
In another case, an industrial manufacturer recovered 10–15% of revenue, previously lost to late arrivals and missed delivery windows, within the first few weeks of using Aera, while avoiding several million dollars in working capital over six months. As one of the company’s executives shared, ‘decision intelligence expands the art of the possible, allowing organizations to run faster and leaner, with clarity at every point in the network.'”
Inventory optimization, supply chain planning, and operational decision automation stand out as high-impact use cases because they directly affect cost, service levels, and revenue realization. Even incremental improvements in these domains can translate into significant financial outcomes.
Question: What advice would you offer to enterprise leaders beginning the transition from manual, spreadsheet-driven planning to autonomous decision-making?
Gonzalo Benedit, Aera Technology:
“My advice is to start by clearly understanding the decision-making problem you’re trying to solve, rather than deploying the technology first and hoping it will simply turn insight into action without a clear definition of the problem. Focus on a specific planning or operational decision that’s slowing the business down or becoming too complex to manage manually, instead of trying to automate everything at once.
Choose cases where transparency and trust will matter in day-to-day work. Adoption is just as important as capability, so invest early in educating teams on the purpose and value of decision intelligence as part of a broader transformation. Set clear goals for adoption and outcomes, such as decision-cycle time, efficiency gains, or value delivered — not just the number of decisions automated. Start with a high-impact area like inventory or pricing, and approach this as a journey, working with a trusted partner to learn, scale, and continuously improve over time.”
This emphasis on trust, adoption, and measurable outcomes reflects a broader reality: ADI is not a plug-and-play upgrade. It is an operational evolution that requires alignment across data, processes, governance, and people.
The Future of Enterprise Planning
As volatility increases and data complexity accelerates, the limitations of spreadsheet-driven, reactive planning will only become more pronounced. Autonomous decision intelligence offers a new operating model. One where insight, action, and learning are continuously connected.
Enterprises that successfully adopt ADI are not simply forecasting better. They are architecting decision systems that can sense, decide, and act at scale, transforming planning from a periodic exercise into an always-on capability. For organizations navigating inventory volatility, supply chain disruption, and operational complexity, the question is no longer whether decisions can be automated. It is whether enterprises can afford not to.
Ready for more? Read more about Aera Technology here.
