In the current manufacturing landscape, leaders are moving beyond experimentation and instead doubling down on the practical adoption of AI. A global survey of more than 100 COOs at manufacturers with revenues of more than $1 billion found that 93% plan to increase investment in AI and digital technologies over the next five years. The challenge now is not whether to invest, but where to start, how to scale and how to deploy AI in ways that deliver measurable impact across production, supply chains, workforce operations, field services and customer experience.
The pace of change is accelerating. AI is poised to reshape the shop floor — especially across four critical areas — but success will require more than ambition. The year ahead will demand meaningful shifts in organizational structures, workflows and business priorities, separating those who experiment from those who execute.
1. It’s time to say goodbye to structural bottlenecks
Most manufacturing organizations were built for sequential work, fixed hierarchies and departmental optimization. Through previous waves of digital transformation, systems have modernized and workflows have been digitized, but the structure around the work stayed the same.
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 and redesign organizations around the new flow of work.
This year, manufacturers will 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’s 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.
When structure aligns with how work actually flows, AI’s impact expands, and the ceiling on what’s possible rises. 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. 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 in order to unlock new levels of speed, clarity and performance.
2. Hardwire sustainability into every operation with embedded AI
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.
AI systems 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.
3. Robotic colleagues step up to fill workplace shortages
Productivity challenges have been a familiar story in manufacturing for years, and they’re only accelerating. Recent OECD data 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’t output kept pace? 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.
Task clarity: key to cross-team collaboration.
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’re proving their value on production floors, designed not to replace people but to extend their reach, consistency, judgment, and problem-solving.
For most, that won’t mean overnight automation. It will mean rethinking how people and robots collaborate day to day, clarifying which tasks are best handled by each, updating safety protocols, and redesigning workflows so teams work confidently alongside intelligent machines.
Success depends as much on change management and trust as it does technology. Those who hesitate, risk being constrained by a workforce model that can no longer scale with demand.
4. Eliminate guesswork with intelligent testing
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.
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’s ability to extract and structure data, making it more coherent and useable — even when it has been created or managed in siloed ways. What has changed is that AI-enabled supply chain modelling and simulation tools can now use that data, even where gaps remain, to build and test scenarios across the supply chain.
Control supply chain management from the factory floor
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.
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.
Fast action and learning will define the 2026 winners
The manufacturers that will pull ahead in 2026 won’t wait for perfect data, complete readiness or ideal conditions. Instead, they’ll move with purpose — focusing on the highest-impact use cases, modernizing strategically, strengthening essential foundations and eliminating the friction of legacy systems — so each action builds on the last. In the year ahead, the advantage will go to those who act quickly, adapt constantly and develop readiness as they advance.
