“Without knowledge,” observed Abu Bakr, the first caliph of the Islamic empire following the death of the Prophet Muhammad, “action is useless and knowledge without action is futile.” That observation could be the motto of corporate planning departments. Plans are important, but their value is only realized once they are executed. And the best plans are based on the best available information. Here’s the rub: There is so much information available and so much uncertainty involved in today’s business environment that it is humanly impossible to plan without the aid of artificial intelligence (AI). However, Lora Cecere, founder of Supply Chain Insights, wonders if AI is being used to its fullest extent. She writes, “Supply chain leaders love bright and shiny objects. Today, the bright and shiny object is AI. But what is AI really? And is there value for planning processes?”[1]

Knowledge and Planning

We’ve all heard about analysis paralysis (i.e., the inability to make a decision due to overthinking and researching a problem, leading to a sense of being stuck and unable to move forward). With so many variables affecting supply chain planning, analysis paralysis is a genuine possibility. The potential to create that situation motivated Vinay P. Sharma, Vice President at Blue Yonder, to ask, “Has there ever been a more challenging time to accurately predict demand?” He thinks not. He explains, “Today, supply chain planners must contend with inflation, high interest rates and other economic forces that influence consumers’ buying behaviors and preferences. Planners must also consider extreme weather, social media trends and other unknowns that might suddenly create a demand peak — or valley. In addition, labor shortages, blocked shipping lanes and geopolitical uncertainty affect organizations’ ability to move products swiftly and profitably from Point A to Point B, which must certainly be part of the demand planning exercise.”[2]

Analysis paralysis occurs whenever a person or team seeks to obtain perfect information or data. It’s a fool’s errand. Cecere notes, “Data does not need to be perfect in this new world, and the list of new processes and capabilities to use unstructured data is endless. The starting point is to redefine your relationship with data. The next step is to throw away the conventional definition of supply chain planning. Companies need a planning system of record, but the models become multiple inputs to multiple outputs that are ever changing based on learning. While convention focuses on optimizers and engines, in this new world, the focus is on model building based on what drives value. Rules engines, learning models, and agentic AI need to be built around what drives value.” AI can bring order to chaos and help avoid analysis paralysis.

As I noted in an article introducing a new product offering at Enterra Solutions® called the Enterra Dynamic Enterprise Resiliency System™ (EDERS™), “[Today there are] myriad factors — some self-inflicted, some circumstantial — that, in concert, have contributed to a state of flux and uncertainty beyond anything I’ve ever seen in my 40+ years working at the intersection of technology, business, and government.”[3] As Cecere pointed out, today’s planning systems need to be agile enough to provide multiple outputs to “what if” scenarios and they must learn as they analyze. Enterra’s Autonomous Decision Science® was built within a Sense, Think, Act, and Learn® framework and uses integrated system diagnostics, resiliency methodology, complexity science, artificial intelligence, and data science to predict the future and inform winning strategies. Sharma states bluntly, “Change is intimidating — and it often requires significant investments. But I would argue that, given today’s extreme volatility, companies are losing more by choosing not to invest in AI.”

Knowledge and Execution

As Abu Bakr noted centuries ago, “Knowledge without action is futile.” At some point, decisions need to be made and action taken using the information garnered during the planning process. Niels Van Hove, Customer Engagement Lead at Aera Technology, explains, “The outcomes of advanced planning systems, as advanced as they may be, are plans, schedules, analytics, insights, alerts, scenarios, at best recommendations … not S&OP decisions! … It requires a whole lot of decision intelligence beyond planning to make timely, unbiased, consistently high-quality decisions, capture them and learn from them.”[4] I couldn’t agree more, which is why my company decided to focus on advancing autonomous decision science.

The world of AI-assisted planning and execution is a new paradigm for most planners. Nevertheless, Pia Orup Lund, a Senior Director of Supply Chain Planning Technology at Gartner, insists that planners shouldn’t hesitate to adapt or embrace a decision-centric planning model. She explains, “The environment in which chief supply chain officers find themselves has changed significantly in recent years, but most organizations have failed to adjust their approach to planning. The fundamentals of supply chain planning have been around a long time. They were designed in a way that makes the process the focal point for decisions — whether through strategic planning, sales and operations planning (S&OP), or sales and operations execution (S&OE). … Supply chains today need a model that aligns with the demands being placed on their organization. In response, forward-thinking supply chains are turning to decision-centric planning (DCP) for business decision-making.”[5]

Lund adds, “Being decision-centric means that processes are designed to create the best possible outcome for the business, involving decision-makers and other stakeholders. DCP requires a rethink of traditional supply chain planning. Organizations must get better at taking advantage of available data, as well as advanced algorithms and other aspects of modern technologies that promise to make them more flexible and adaptable to change.” Von Hove adds, “A digital transformation doesn’t make you decision centric.” On the other hand, he notes, “Decision intelligence technology is focused on the decision. It can orchestrate decision-making as a measurable data-to-action business process.”

Concluding Thoughts

Do you need an AI-assisted decision-centric planning system? Sharma believes you do. He concludes, “The truth is that most planning teams aren’t equipped to consider hundreds of relevant factors and arrive at an accurate forecast in a dynamic daily or intraday fashion. The scope, depth and pace of the required analysis exceed human cognition and consumer-grade tools — and historic sales data has become almost meaningless in today’s fast-changing landscape. Today successful organizations are empowering their demand planning teams with advanced, forward-looking, predictive technology solutions that are powered by artificial intelligence.” Van Hove agrees, even though he recognizes change won’t be easy. He explains, “Becoming decision centric requires a rethink of our vision, planning operating models, roles & responsibilities, our mindset & behaviors, and incentives, just to name a few.” I think Thomas A. Moore, CEO of ProvisionAi, said it best, “Good companies plan — great companies execute the plan.”[6]

Footnotes

[1] Lora Cecere, “Please Don’t AI Stupid,” Supply Chain Shaman, 3 April 2025

[2] Vinay P. Sharma, “Empowering Demand Planners With AI,” Blue Yonder, 12 September 2024.

[3] Stephen DeAngelis, “Introducing EDERS™: Convert Chaos into Competitive Advantage,” Enterra Insights, 8 April 2025.

[4] Niels Van Hove, “The ‘Decision Washing’ of Supply Chain Planning,” Supply Chain Trend, 23 August 2024.

[5] Pia Orup Lund, “Supply Chain Planning Must Become Decision-Centric,” SupplyChainBrain, 15 June 2023.

[6] Thomas A. Moore, “Good companies plan — great companies execute the plan,” IT in the Supply Chain, 17 December 2023.