Kwame Zaire has spent decades on the front lines of manufacturing, evolving from the precision-driven world of electronics assembly to becoming a leading voice in high-stakes production management. His expertise lies at the intersection of predictive maintenance and operational safety, where he advocates for a disciplined approach to technological adoption. As supply chains face unprecedented global instability, Zaire emphasizes that the real revolution isn’t found in the software itself, but in the structural integrity of the organizations using it. In this discussion, we explore the stark reality behind why many manufacturers are struggling to see returns on their digital investments and how the industry must pivot toward a more mature, decision-led planning model to survive the complexities of the coming years.
Organizations with high maturity levels achieve 25% greater forecast accuracy than their lower-level peers. What specific structural changes define a “mature” planning environment, and how should a company measure its progress toward this level of sophistication?
To move the needle toward that 25% accuracy boost, a company must first dismantle the silos that keep data trapped in individual departments. A mature environment is defined by a shift from regional, disconnected operations to a globally synchronized framework where every department operates from the same playbook. We measure progress by the ability to implement a disciplined planning process that prioritizes data reliability over sheer volume. For instance, companies that show the most substantial advances are those in the Middle East, Europe, and Africa that have successfully embedded new behaviors at scale rather than just installing new software. Milestone success is seen when the planning cycle moves away from reactive corrections and toward a system where structural redesigns allow for seamless cross-functional working methods.
Despite widespread investment in advanced tools, 78% of leaders still identify inaccurate demand forecasting as their primary struggle. Why does layering AI onto existing planning systems often fail to fix these core inaccuracies, and how can teams better align their technology with operational priorities?
The frustration for that 78% of leadership stems from the fact that AI is often treated as a “plug-and-play” solution for broken processes, which simply accelerates existing inefficiencies. Layering expensive algorithms onto a shaky foundation is like putting a high-performance engine in a car with a cracked frame; you might go faster, but the crash will be more spectacular. Teams fail when they apply technology with misaligned priorities, focusing on the tool’s bells and whistles rather than addressing fundamental operational friction. To align technology with reality, leaders must ensure that AI is used as a powerful catalyst for specific, designed use cases rather than a broad-spectrum fix for poor data quality. It requires a hard look at why 70% of companies are spending on advanced systems but still feel like they are guessing what their customers will want next month.
Transitioning to exception-based workflows requires establishing a single version of truth across all departments. What practical steps can a global manufacturer take to synchronize cross-functional data, and how does this shift specifically reduce the “firefighting” typically seen in manual planning cycles?
Synchronizing data starts with the brutal task of ensuring every stakeholder—from procurement to the shop floor—is looking at the exact same numbers in real-time. Practical steps involve a workflow redesign that forces the retirement of fragmented legacy systems in favor of a unified data foundation. This “single version of truth” effectively kills the “firefighting” mentality because the system highlights anomalies before they become crises, allowing planners to focus only on significant deviations. When you move to exception-based workflows, you aren’t just saving time; you are creating a faster decision cycle that replaces the frantic, sensory overload of manual checking with a calm, strategic oversight. It transforms the atmosphere of the planning office from a high-stress emergency room to a controlled, proactive command center.
Many organizations struggle to move away from manual spreadsheets even after implementing AI-driven tools. What specific upskilling initiatives help teams embrace AI-enabled planning, and how should decision rights be redefined to ensure clear accountability when the technology provides the primary recommendations?
The emotional attachment to manual spreadsheets is a major hurdle because these tools represent a sense of control for the planners who built them. To break this habit, organizations need targeted upskilling initiatives that focus not just on how to click buttons in a new interface, but on interpreting the logic behind AI-enabled recommendations. We must also clarify decision rights and forums to eliminate the fragmented accountability that occurs when people don’t trust the machine’s output. By explicitly defining who owns the final call—and where the technology’s “advice” ends and human judgment begins—teams feel empowered rather than replaced. This transition requires a cultural shift where the goal is no longer to manage a spreadsheet, but to manage the strategic outcomes that the AI identifies.
Only about 20% of organizations report gaining meaningful value from AI, with even fewer seeing results from generative or agentic tools. What distinguishes the successful few who have achieved performance at scale, and what specific use cases should leaders prioritize to move beyond the pilot phase?
The successful 20% are those that treat AI as a core strategic capability rather than a peripheral IT project. These organizations distinguish themselves by embedding AI into disciplined, existing processes and ensuring they have a reliable data foundation before they even think about the pilot phase. For those looking to see value beyond the mere 7% reported by users of generative AI, the priority should be on decision-led approaches that target specific pain points, like inventory optimization or lead-time prediction. They avoid the trap of “pilot purgatory” by scaling use cases that have clear, measurable impacts on the bottom line from day one. It is about being surgical with the technology—using it to accelerate decisions that are already part of a robust operating model.
What is your forecast for AI-enabled supply chain planning?
By 2026, the divide between the leaders and the laggards in manufacturing will become an unbridgeable chasm. I foresee a landscape where planning is no longer a back-office function but the central nervous system of the enterprise, driven by agentic AI that anticipates disruptions before they even appear on a radar. We will see the end of the “experimentation phase,” as companies that failed to integrate their data foundations will be forced to consolidate or exit the market entirely. The future belongs to those who recognize that while AI is the catalyst, the human elements of process design and cross-functional discipline remain the true engines of sustained performance. Success will be defined by how quickly a company can turn a “single version of truth” into a competitive weapon in an increasingly unstable global economy.
