Today, planning organizations have more visibility into their operations than ever before. Connected systems, richer operational data, and increasingly sophisticated planning tools have transformed how businesses monitor demand, inventory, suppliers, and disruptions across the supply chain.
Surely it should be easier to make faster decisions! Yet Ryan Misquita, Global Lead for Supply Chain Planning Transformation at IBM Consulting, believes the opposite is happening.
Speaking on a recent episode of The Future of Supply Chain podcast, Misquita argued that the problem has nothing to do with information at all. Planning organizations are running into "fundamental mismatches between the speed of planning processes and the real-world problems or disruptions." Every new source of visibility creates another signal to evaluate, another exception to investigate, and another decision competing for a planner's time.
That one insight reframes what's next for supply chain planning. If organizations can already see more than ever before, why is deciding what matters becoming more difficult? And if more information isn't enough, where does the next competitive advantage come from?
Visibility changed the planning problem
For years, supply chain innovation focused on helping planners see more. Better forecasting, integrated planning, connected business processes, and richer operational data gave organizations a clearer picture of demand, inventory, suppliers, and logistics.
Those investments delivered exactly what they promised. They also changed the planner's job. Every improvement in visibility expanded the number of signals flowing into the planning process.
Demand shifts, supplier constraints, weather events, transportation delays, geopolitical developments, and operational exceptions all compete for a planner's attention. Yet planners have only so much time to determine which developments deserve immediate action.
Misquita believes that imbalance is reshaping planning itself. "The volume of signals continues to increase, but our planning processes haven't evolved at the same pace."
Visibility remains essential, but the next competitive advantage comes from shortening the distance between seeing a disruption and deciding how to respond. Organizations that can close that gap will be positioned to act while decisions can still influence the outcome.
A different speed of decision-making
Planning systems have become remarkably effective at identifying potential problems. The next opportunity is helping organizations respond before those problems become business outcomes.
"We are moving beyond, for example, just alerting a planner that a stockout is coming," Misquita says. "Generative AI can analyze unstructured data like weather patterns, supplier communications, and port congestion, then automatically trigger what-if simulations that help planners evaluate options before disruptions spread."
Planning systems are beginning to participate in decisions instead of simply reporting them. They can connect information, business context, and recommended actions. Signal orchestration, decision latency, and intelligent prioritization work together to surface the decisions that matter most rather than simply adding more alerts.
The business value becomes easier to see in practice. One of IBM Consulting's aerospace and defense clients reduced its planning cycle from seven days to four or five hours.
"That outcome is not just a productivity event. It's a different speed of decision making," Misquita explains. Faster planning cycles give organizations more opportunities to reposition inventory, engage suppliers, and protect customer commitments while those decisions can still influence the outcome.
The planner's role changes as well. "Generative AI significantly reduces the latency," Misquita says. Time once spent gathering information can now be spent evaluating tradeoffs, collaborating across the business, and applying judgment where it matters most. AI doesn't replace experience. It creates more opportunities to use it.
The future starts with strategy, not new technology
Organizations eager to adopt new technology often start with the tools instead of the business problem. But one of the biggest mistakes, Misquita says, is putting "technology first and strategy second."
Successful initiatives begin with a clearly defined business problem, establish measurable outcomes, and then determine where AI can improve planning decisions. That same discipline applies to generative AI, which depends on accurate planning information to produce reliable recommendations.
As Misquita puts it, if lead times or safety stock are "hallucinating," Generative AI will hallucinate too. Strong data governance, meaningful KPIs, and integrated planning processes remain the foundation for every successful AI initiative.
Organizations that make the greatest progress don't begin by asking what AI can do. They begin by asking which planning decisions matter most.
Where planners go from here?
Looking further ahead, Misquita sees planning becoming increasingly self-orchestrating. AI will continuously sense change, evaluate options, and coordinate routine decisions while planners focus on strategy, governance, and the exceptions that require human judgment.
That vision only elevates the planner's role. The difference now is how quickly those decisions can be made.
Supply chain planning teams have spent years expanding visibility across increasingly complex operations. The next competitive advantage belongs to organizations that can turn visibility into decisions before the opportunity passes.
Don’t miss the full conversation
Listen to the full episode to hear Ryan Misquita share more use cases for generative AI in supply chain planning, including exception management, forecasting, data readiness, KPI measurement, and autonomous planning.
