Manufacturing's next move: Preparing the enterprise for AI-led transformation

The future of manufacturing depends on foundations designed for what's ahead

In my previous blog, “The new manufacturing mandate: Resilient, intelligent, ready for uncertainty,” I discussed how resilience has become the defining capability for modern manufacturers. Trade fragmentation, climate pressures, talent shortages and accelerating digital change have fundamentally changed the environment in which manufacturers operate.

So, if resilience depends on greater adaptability, is today's manufacturing enterprise actually built for it?

For many organizations, the answer lies somewhere between “maybe” and “no.”

For decades, manufacturers invested in technology to improve efficiency, standardize operations and support predictable growth. Enterprise resource planning (ERP), manufacturing execution systems (MES) and other core platforms were designed for a world with linear supply chains, siloed functions, stable markets, and incremental technology changes.

Those systems continue to operate the business, but they also make it more challenging to adapt as the business evolves.

Manufacturing executives recognize the pressure this creates. In the 2026 Voice of Our Clients research, more than half (51%) identify legacy systems as a barrier to progress.

Manufacturers are not short on ambition or ideas. However, they are trying to scale those ideas across enterprises that were never designed for today's pace of change. The shift now must be from siloed operations to connected enterprises that can collaborate across ecosystems of suppliers, partners and customers while responding more quickly to changing market conditions.

Execution, not strategy, is becoming the real challenge

Most manufacturers already have a transformation strategy. The harder task is turning that strategy into sustained business outcomes.

Fragmented applications, disconnected data and years of incremental change and workarounds have created technology estates that are increasingly difficult to adapt. Every new initiative demands more integration, more governance and more effort to connect information across the business, slowing decisions and actual progress.

Only 25% of manufacturers describe their business model as highly agile, while just 19% say the same about their operating model.

These figures highlight a broader issue. Agility is no longer determined by how quickly one function can move. It depends on how effectively the enterprise works as a connected system.

When business and technology are only partially aligned, transformation slows. Data becomes harder to trust, governance becomes more complex and scaling innovation becomes more expensive.

That is why many manufacturers are beginning to rethink the enterprise itself. Rather than simply layering AI onto existing environments, they are reengineering core platforms, building resilience into the architecture and adopting modular capabilities that can evolve alongside the business.

AI is becoming part of the enterprise

Manufacturers have moved beyond asking whether AI has value.

The focus is now on identifying where it can improve engineering, production, supply chains and enterprise operations and on determining how those capabilities can be scaled.

Today, 66% of manufacturers report applying AI across core business and operational processes.

As AI becomes embedded across the enterprise, weaknesses in the underlying architecture become more apparent. Fragmented ERP, product life cycle management (PLM) and MES environments create inconsistent data and limited visibility. Disconnected processes make cross-functional orchestration more difficult, while governance models designed for traditional enterprise applications are now expected to support intelligent systems that continuously learn and adapt.

Manufacturers are still building those foundations. Only 37% report having a holistic AI strategy, while nearly half are not yet measuring business outcomes from their AI initiatives.

Agentic AI raises the stakes

AI is continuing to evolve. Generative AI (GenAI) demonstrated how quickly intelligent technologies could improve individual productivity. Agentic AI expands that opportunity across the enterprise.

Rather than supporting individual tasks, intelligent agents have the potential to monitor operations, trigger workflows, coordinate decisions across systems and enable more autonomous execution throughout the manufacturing value chain.

Manufacturers are already preparing for that shift. Nearly three-quarters (73%) are exploring agentic AI, ahead of the cross-industry average of 69%.

The opportunity extends well beyond improving reporting or introducing more capable assistants. Manufacturers are beginning to consider production environments that adjust to changing conditions, engineering processes that coordinate across functions, maintenance activities that anticipate failures and service operations that respond more intelligently.

Those capabilities depend on more than AI. They require trusted data, modular architectures, clearly defined system interfaces, secure environments and governance that extends across both IT and OT. As intelligent systems take on more responsibility, the quality of the enterprise foundation becomes increasingly important.

Modularity is becoming the foundation for change

Manufacturers are rethinking how the enterprise itself evolves. Large-scale replacement programs are giving way to building more modular, interoperable and software-defined environments.

A modular enterprise allows manufacturers to modernize core systems without rebuilding the entire technology estate. New AI capabilities can be introduced more quickly, data can move more freely across the business, and individual components can evolve as business priorities change.

The benefits extend beyond technology.

As manufacturers strengthen connections across suppliers, partners and service providers, modular platforms make it easier to participate in broader digital ecosystems while reducing dependence on monolithic environments that are increasingly difficult to adapt.

The direction is already clear. Manufacturers are investing in digitally connected operations, software-defined environments and enterprise-scale AI.

At the same time, important gaps remain. While 54% of manufacturers report having a holistic data strategy, fewer than half say they can readily locate and access their data assets.

Without trusted data, connected platforms and modular architectures, transformation becomes progressively harder to scale.

Building resilience into the enterprise

The question is no longer, “Where can we pilot AI?” It is, “What must we reengineer, so AI becomes part of the enterprise core?”

Preparing the enterprise for AI requires modernizing the digital core, reducing legacy complexity, connecting data across operations, strengthening IT/OT convergence and establishing governance designed for intelligent systems. It also means creating an enterprise architecture that can evolve alongside technology, business priorities and an increasingly connected ecosystem of partners.

The manufacturers that lead over the next decade will not necessarily be those deploying the most AI. They will be those that build enterprises capable of absorbing change, integrating new technologies and adapting continuously as the industry evolves.

Every manufacturer’s AI journey is different, but the questions are becoming remarkably similar. If you are exploring how to build the enterprise capabilities, data foundations and operating model needed to scale AI with confidence, I would welcome the opportunity to discuss the opportunities and challenges shaping your path forward.