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<\/figure>\n\n\n\nModernizing core systems and adopting AI are distinct efforts with different timelines, investment structures, and organizational implications. They are not maturing at the same pace, which directly threatens the efficacy of both initiatives, particularly as many manufacturers are still working through legacy upgrades, fragmented data architectures, and competing transformation priorities. In other cases, slowdowns are caused by cultural resistance, slow adoption of new technologies, or unclear ownership.<\/p>\n\n\n\n
The result is a manufacturing landscape in transition, where connected factories and intelligent supply chains exist in pockets, but the technical, organizational, and cultural foundations needed for AI to operate end-to-end are still being built, and which many manufacturers are not yet in a position to fully support based on the aforementioned challenges.<\/p>\n\n\n\n
From our vantage point, working alongside manufacturers every day, one thing is clear\u2026 AI is here to stay. The next year will be about making it work in practice, integrated into the wider operational model, not operating in isolation.<\/p>\n\n\n\n
The question now is where that shift will be most visible and consequential. The following predictions reflect where we expect the most meaningful progress in 2026 and beyond.<\/p>\n\n\n\n
Prediction 1<\/strong><\/p>\n\n\n\n Organizational structures will be redesigned to support AI enabled ways of working<\/p>\n\n\n\n Most manufacturing organizations were built for sequential work, fixed hierarchies, and departmental optimization. Through waves of digital transformation, systems modernized and workflows digitized, but the structure around the work stayed the same. Work changed. Tools changed. Organizational design did not.<\/p>\n\n\n\n That structure is now the bottleneck. AI can connect planning, production, supply chain, service and workforce activity in real time, but when an organization is still designed for linear, sequential work, the value stalls at departmental boundaries. Intelligence gets trapped in functions. Progress defaults to the pace of approvals and hierarchy, not the speed of what technology makes possible.<\/p>\n\n\n\n The challenge isn\u2019t just how to use AI, it\u2019s how to organize for it. How does work move when people, AI systems, and soon robots, all contribute to outcomes? How are goals coordinated when value creation spans multiple functions at once? Who is accountable when automation enters a workflow, and how do humans stay empowered, rather than constrained? These aren\u2019t technology questions, they\u2019re organizational design questions.<\/p>\n\n\n\n In 2026, we expect manufacturers to begin reassessing their design, not to reduce roles, but to remove the structural barriers that limit what people can achieve with AI. This is not about replacing humans, it\u2019s about removing the friction that holds them back. Governance will always matter, but governance is not the constraint here. The constraint is the scaffolding around the work itself.<\/p>\n\n\n\n When structure aligns with how work actually flows, AI\u2019s impact expands, and the ceiling on what\u2019s possible rises. Linear operating models cannot support nonlinear systems. To realize returns on AI investments, organizations will need to move beyond hierarchies built for a different era and build designs that enable work to move fluidly across functions. This is, admittedly, a 35,000 ft view, not because the structure is vague, but because it will differ for every manufacturer. The shift is less about adopting a new org chart template and more about designing around how work, decisions, and outcomes actually move through a business.<\/p>\n\n\n\n Every organizational model will look different, but those who redesign around how work truly happens, not how it has historically been organized, will unlock new levels of speed, clarity, and performance.<\/p>\n\n\n\n When structure and intelligence reinforce one another, AI stops being a tool used in pockets and becomes a capability that connects a business end-to-end.<\/p>\n\n\n\n Prediction 2<\/strong><\/p>\n\n\n\n Supply chain intelligence will shift from episodic analysis to a continuous internal capability<\/p>\n\n\n\n If 2025 proved anything, it is that predicting disruption is impossible, but preparing for it is not. Manufacturers now have the ability to model complex what if scenarios, simulate disruptions, and plan responses before issues reach production.<\/p>\n\n\n\n For most organizations, supply chain data remains distributed across systems and formats. That reality has not changed. What has changed is how manufacturers can work with it. Most are already familiar with AI\u2019s ability to extract and structure data, making it more coherent and useable even when it has been created or managed in siloed ways. What is new is that AI enabled supply chain modelling and simulation tools can use that data, even where gaps remain, to build and test scenarios across the supply chain.<\/p>\n\n\n\n The constraint is no longer the availability of data or modelling technology. What matters now is how effectively manufacturers bring the two together to test assumptions at different stages and levels of their supply chain. Doing so makes it possible to see where gaps remain, which parts of the supply chain are more or less resilient, and how different scenarios are likely to play out.<\/p>\n\n\n\n Over 2026, supply chain intelligence will increasingly become a core internal capability. Rather than relying on third party or consultant led, periodic analysis, manufacturers will use AI enabled supply chain intelligence tools internally on a regular basis to explore scenarios, test assumptions, and better respond to change. Over time, this embeds optimization, resilience, and value creation directly into how supply chains are managed, not as a one-off exercise, but as part of day-to-day operations.<\/p>\n\n\n\n Prediction 3<\/strong><\/p>\n\n\n\n Efficiency will turn sustainability into an operational requirement<\/p>\n\n\n\n By 2027, manufacturers will have built AI driven sustainability systems that monitor, report, and optimize environmental impact in real time, as environmental compliance becomes an operational imperative.<\/p>\n\n\n\n As global regulations fluctuate and investor expectations rise, manufacturers must now measure environmental performance with the same rigor applied to cost and quality. Expanding mandates around emission disclosure and energy transparency will drive demand for continuous, verifiable data across operations. Sustainability will become AI enabled and embedded into how factories, supply chains, workforces, and assets are managed day to day, integrated directly into planning, execution, and optimization cycles.<\/p>\n\n\n\n AI\u202fsystems unify fragmented data, monitor resource use at the source, and generate real-time insight into energy consumption, emissions, and waste. What once required lengthy reporting cycles or audits will evolve into a continuous feedback system, one that learns, flags anomalies, and guides adjustments before targets are missed.\u202f<\/p>\n\n\n\n Prediction 4<\/strong><\/p>\n\n\n\n Humanoid robots will become the new productivity engines on shop floors<\/p>\n\n\n\n Productivity challenges have been a familiar story in manufacturing for years, and they\u2019re only accelerating today. OECD data<\/a> shows annual productivity gains have fallen from 2-3% in the early 2000s to less than 1% today. After years of digital transformation investment, many manufacturers are asking, why hasn\u2019t output kept pace?<\/p>\n\n\n\n Legacy systems and fragmented processes play a role, but the deeper constraint is capacity. The global labor shortage has reached a breaking point. Skilled technicians are retiring faster than replacements enter the workforce, and open roles remain unfilled for months. In factories already running lean, every vacancy compounds downtime and lost throughput.<\/p>\n\n\n\n The next leap in industrial productivity will come from a fundamentally new workforce model, one where robots, and AI enabled systems operate side by side. Humanoid and mobile robots are no longer science projects. They\u2019re proving their value on production floors, designed not to replace people but to extend their reach, consistency, judgment, and problem-solving.<\/p>\n\n\n\n