2026: The year agentic AI transforms industrial manufacturing

Digital transformation: Why manufacturers must act now

Reflecting on 2025

Across global manufacturing, one theme in conversations has been consistent: Volatility is no longer something you plan around—it’s something you operate within. We’ve seen organizations face unprecedented geopolitical uncertainty, ongoing supply chain disruption, intense regulatory demands, and rising customer expectations, all striking in ways that are testing every step in the production cycle.

What has become clear is that organizations with a strong digital foundation—unified data, greater process automation, and real‑time operational visibility—are far better equipped to absorb change and adjust quickly. Those still constrained by fragmented data, siloed systems, and manual workarounds, however, have found it challenging to respond with the speed, resilience, and consistency needed to stay competitive.

Read more about our latest research that uncovers how the most productive organizations are closing the gap between technology’s promise and their digital transformation journeys.

What’s to come in 2026

If we learned anything from 2025, it’s that the conversation moving forward is shifting from digital and artificial intelligence (AI) experimentation to deployment at scale. Manufacturers are recognizing that success increasingly depends on how well intelligence and resilience are embedded into the core of day-to-day operations, rather than layered on at the edges.

At the center of this shift is the evolving role of AI in manufacturing. What has been largely exploratory is now becoming industrialized, where demonstrating a real return on investment is required. In particular, we are seeing growing momentum beyond predictive and generative AI to agentic AI—where the technology is not just used to analyze data, generate insights, or make recommendations, but to pursue defined outcomes by coordinating decisions, taking actions, and orchestrating processes across planning, production, and execution.

The rise of agentic AI

We’ve seen growing curiosity—and in many cases, early adoption—of AI agents that handle very specific tasks or responsibilities. Leaders tell us they’re looking for systems that don’t simply notify them of issues but help resolve them. Among early adopters, we’re already seeing agent‑driven workflows identify deviations, adjust schedules, update work orders, or automatically trigger supplier follow‑ups.

Manufacturers describe the change as subtle but powerful. Instead of being asked to monitor, interpret, and respond to every shift in the process, much of the routine decision‑making now happens automatically, in the background. As Shen Lu, CIO of Gellert Global Group, explains, “Infor’s Industry AI Agents have the potential to significantly enhance ERP (enterprise resource planning) functionality—delivering faster access to information, quicker issue resolution, and improved customer satisfaction. By automating repetitive tasks, these agents enable employees to focus on higher‑value work that drives organizational growth and competitive advantage.”

The result is a shift in how work gets done. Teams step in where human judgment is needed, but no longer carry the burden of endless mundane tasks. Instead, they can focus more time on what humans do best—creative problem solving, innovation, and continuous improvement. The workforce is no longer overwhelmed by process but is empowered by it, spending less time reacting and more time proactively shaping better outcomes. In this new era, technology doesn’t take the lead; it empowers people to lead with greater clarity and confidence than ever before.

In our experience, the biggest barrier is often not technology, but mindset. Delegating decision‑making to autonomous systems can feel unfamiliar in traditionally conservative industries, even as market pressures continue to intensify. By embedding intelligence directly into operational workflows, AI helps bridge that gap—positioning itself not as a replacement for people, but as a trusted collaborator alongside them.

Building the foundation: From integration to intelligence

If there’s one consistent takeaway from our discussions, it’s this: AI succeeds only when the digital foundations are strong.

Manufacturers who have invested in integrated platforms—connecting the machines on factory floors with supply networks, logistics systems, and service operations—are the ones now able to activate AI capabilities at scale. These organizations tell us that once data silos are removed, visibility across departments, functions, and production environments naturally turns into action. Predictive inventory shortfalls are automatically replenished, inventory becomes self‑balancing, and production plans adapt in real time.

Leaders increasingly link this integration to resilience. They know supply chain volatility is not going away, and they’re looking for ways to run lean while still protecting flexibility. Time and again, we hear about the value of shifting toward “just‑enough” operations—meeting customer commitments without unnecessary buffers, enabled by real‑time intelligence. This only becomes possible when manufacturers can adapt to changing conditions in real time and pivot quickly to mitigate impact.

Empowering the connected workforce

As all forms of AI (predictive, generative, agentic) become more embedded in operations, one principle remains front and center in our discussions with manufacturers: This is not about replacing people—it’s about enabling a more connected, informed workforce. Modern enterprise applications are increasingly delivering real‑time insights and guidance through familiar, intuitive interfaces—whether that’s tablets on the shop floor, kiosks in the warehouse, or emerging technologies such as augmented‑reality glasses.

AI is starting to play a meaningful role by providing contextual guidance and triggering actions in real time, helping reduce cognitive load while keeping people accountable. The result is a workforce that is better supported in the moment—with in‑context safety checks, clear work instructions, and built‑in quality measures that adapt to changing conditions rather than relying on static processes.

Upskilling continues to be a clear priority. We’re seeing growing demand for data fluency and AI literacy across roles, but the journey doesn’t have to be disruptive or immediate. For many machine operators, the first step may simply be digitally guided workflows, ensuring quality is consistently met regardless of skill level. Over time, that same individual can begin to use analytics to identify efficiency opportunities or quality trends. This phased approach helps organizations address urgent operational needs today while building the skills and confidence required for the future—ensuring the workforce evolves alongside the technology.

Driving sustainable, profitable growth

Another trend emerging consistently in our conversations with manufacturing organizations is the merging of two strategic priorities—sustainability and profitability. Manufacturers increasingly recognize that intelligent, automated optimization is key to both.

Many are navigating a dual imperative: To drive growth while operating more responsibly. Tightening environmental, social, and governance (ESG) requirements, combined with more informed and conscious customers, are placing greater emphasis on transparency across operations and supply chains.

Organizations using AI are already being asked to translate sustainability ambitions into everyday operational decisions. By optimizing production processes in real time, reducing waste and improving resource efficiency, AI and machine learning are helping manufacturers lower energy consumption and material loss—delivering measurable environmental benefits while directly improving margins.

We are also seeing growing momentum around deep traceability capabilities. Platforms that enable businesses to track material origins, monitor ethical sourcing, and demonstrate compliance are becoming essential—not just for regulatory reporting, but for maintaining brand trust. This same level of visibility is supporting the move toward more circular operating models, where materials can be tracked for reuse and recycling, strengthening long‑term resilience as manufacturers look ahead to 2026 and beyond.

2026: A year of realizing AI value

Across all these conversations—a shared sentiment is emerging: 2026 is not about distant transformation; it’s about scaling what already works while delivering value and return on investment (ROI).

Manufacturers that combine AI, human capability, and connected digital platforms are building operations that learn, adapt, and improve continuously. They’re reducing complexity, accelerating decision cycles, and creating space for innovation.

The advantage lies not in technology alone, but in how it is applied. Manufacturers that successfully integrate intelligence into everyday workflows are turning data into action and complexity into clarity, while keeping people firmly at the center of decision‑making.

In our experience, the manufacturers best positioned for the years ahead are those willing to rethink how humans and intelligent systems work together—strengthening resilience, accelerating learning, and unlocking new sources of profitable growth. For them, 2026 is less a milestone and more a moment to extend the lead they’ve already begun to establish.

Ready to lead the change? Explore how industry-specific solutions from Infor™ can help you integrate AI into your operations, empower your workforce, and build resilience for 2026 and beyond.

Learn more:

AI in Different Industries | Industry AI Examples | Infor
Agentic AI vs. Generative AI | What’s the Difference? | Infor
Enterprise Artificial Intelligence (AI) Solutions | Infor Industry AI
Embrace AI as an SMB manufacturer | Executive brief | Infor