Four Changes That Will Help Manufacturers Navigate Disruption

Manufacturers had a rough ride in 2025. Shifting tariff policies and trade dynamics forced production sites to relocate, ongoing labor shortages tightened margins while highlighting persistent skills gaps, and supply chains were reconfigured almost as quickly as they were disrupted. That’s not forgetting the rising sustainability requirements that added new layers of complexity into the mix.

Artificial intelligence became the must-have tool across the industry to address these top challenges but progress was inconsistent. While some manufacturers have established connected factories and data-driven supply networks, many are still building the technical infrastructure - and the organizational and cultural readiness - they will need to apply AI at scale across the enterprise.

2026 holds a lot of promise for AI but success hinges on commitment and execution. Already 93% of chief operating officers at companies with revenues exceeding $1 billion intend to boost investment in AI and digital capabilities over the next five years. But the challenge now is in determining where to start, how to expand, and how to deploy AI in ways that produce tangible results. So what can we expect to change in the months ahead?

1. Turn uncertainty into readiness with AI-enabled supply chain modeling

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.

Another change 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.

But don’t rely on outsourcing the intelligence. 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 steadily 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 respond better to change. Over time, this will embed optimization, resilience, and value creation directly into supply chains management, not as a one-off exercise but as part of day-to-day operations.

2. Greener outcomes will start when AI is built-in

As global regulations fluctuate and investor expectations rise, manufacturers must measure environmental performance with the same rigor they apply to cost and quality control. 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. Fixed hierarchies will give way to AI-driven agility

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 organizational 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.

But only once the constraint shackles are released. In the months ahead manufacturers will begin reassessing their organizational designs - not to reduce roles, but to remove the structural barriers that limit what people can achieve with AI. This is not a strategy to remove human workers; 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 for possibilities 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.

4. Overhauling the shop floor to address productivity lags

Productivity challenges have been familiar to manufacturers for years, and they’re only accelerating. Recent OECD data shows annual productivity gains have fallen from 2 to 3% in the early 2000s to less than 1% today. After years of digital transformation investment, many manufacturers are asking: Why has output not kept up the 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.

And robots are primed to join the ranks. The next leap in industrial productivity will result from a fundamentally new workforce model, with robots and AI-enabled systems operating 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 manufacturers that will not mean overnight automation. It will mean rethinking how people and robots collaborate from 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.

Manufacturers that hesitate will risk being constrained by a workforce model that can no longer scale with demand.

Rewarding boldness and decisiveness

The manufacturers who thrive over the next year won’t wait for perfect conditions, flawless data, or full organizational readiness. Instead, they will build readiness as they go - focusing on the highest-impact use cases, modernizing selectively, strengthening critical foundations, and streamlining legacy systems so that every step forward accelerates the next. Success will favor those who act with discipline, learn from past mistakes, and build momentum with every step.