How AI is shaping the future of manufacturing by Mark Humphlett
AI technology is already transforming the manufacturing industry by automating routine tasks, accelerating data-driven decision-making, and improving quality control. All while enhancing efficiency by detecting defects and issues earlier providing insights into processes and workflows to help employees work more efficiently and systems perform better and increasing overall supply-chain visibility.
But the more important question for manufacturers is not just whether AI can be trusted with decisions, but how to introduce it in a way that keeps humans involved. Adoption will be gradual, with people supervising and approving AI actions until they gain confidence in the results. This means thinking about which decisions AI can handle best and how to choose use cases that deliver value quickly, build trust, and show a clear return on investment. Beyond the immediate promise of automation, AI also enables manufacturers to anticipate problems before they occur (e.g. predictive maintenance and analytics), optimize processes in real time (e.g. process intelligence), and respond to supply chain disruptions (e.g. tariffs, supply shortages) with greater agility. Manufacturers who understand where to leverage AI and deploy it strategically today will define the standards of operational excellence for the decade ahead.
Recent advances in artificial intelligence, such as Gen AI and agentic AI, have led to the emergence of new ways of utilizing the technology. Enterprise automation and AI agents can act autonomously to achieve specific goals. Agentic AI will allow manufacturers to gather data, analyze options, and support decision-making and actions in a continuous cycle. In manufacturing, for example, introducing AI agents will automate the process of identifying project delays, examining root causes, suggesting alternate solutions, and keeping timelines on track, allowing human teams to review recommendations, approve changes, and focus their time on higher-value work.
And while Agentic AI adoption is still in its early stages, McKinsey reports that only 39 percent of companies see meaningful earnings impact at the enterprise level of existing AI innovation initiatives. At the same time, 76 percent of organizations (in the manufacturing industry) expect AI to deliver productivity gains of more than 20 percent over the next three years. This contrast highlights that current ‘generic’ AI implementations are not yet creating measurable value. To start to see value from AI investments, manufacturers must not treat AI as a technology experiment, but as a practical tool for solving real operational problems. This means being intentional in deploying AI around results-driven use-cases, tailoring AI to be built around the way they work and embedding AI into day-to-day operations that are aligned to KPIs executives already track.
Reducing mundane tasks
What sets Enterprise and Agentic AI apart from more generic, rules-based AI implementations is its ability to fundamentally change how manufacturers handle repetitive, high-frequency tasks that have long consumed valuable human capacity. While AI promises to deliver numerous benefits, one of its pivotal strengths is enabling organizations to move away from manual, time-intensive activities by introducing end-to-end autonomy across business processes and decisions. With the right technology in place, routine quality checks, minor machine adjustments, and production line monitoring can be automated, allowing employees to redirect their focus toward the business activities that truly drive value.
By alleviating human teams of repetitive work, AI also helps reduce errors and mistakes that may occur through fatigue or inconsistency. Systems that continuously monitor production in real time can help diagnose process issues, automate tasks where appropriate, and optimize overall business performance The result is not only greater operational efficiency but also higher product quality and consistency, which is essential in highly competitive manufacturing markets.
Enhancing decision-making
Agentic AI means taking on a fully autonomous role in manufacturing. Rather than simply supporting human decision-making, it can monitor operations, act proactively to keep production on track, and make decisions based on full assessment of the scenarios, impact, risk, etc. This means issues can be identified earlier and more consistently, reducing delays, costs, and improving overall quality. For example, complex, strategic decisions that AI is not designed to handle. Leaders can instead devote more time to improving processes, driving unneeded cost out of the business, sparking innovation, and shaping long-term plans. Clear rules and guardrails must be in place to ensure AI acts autonomously when appropriate and defers to humans when necessary. This approach will be evident for the foreseeable future as the technology matures, manufacturers build trust in the outcomes, and organizations define the role they want AI to play. In fact, 82 percent of organizations in the manufacturing industry agree that their future success will depend on the effective use and adoption of new AI technologies. Defining its role now and in the future is essential.
A view to the future
Looking ahead, achieving success in the competitive field of manufacturing will not go to those who take a broad approach to AI system deployment, but to those who implement and deploy enterprise and Agentic AI practically. Closing the gap between AI’s promise and reality ultimately requires manufacturers to move beyond experimentation and the idea of a one-size-fits-all solution. Instead, the key to success lies in their ability to focus on approaches that allow customers to benefit from AI that is easy to activate, trained on real industry-specific business data and processes, and identify areas where AI is an enabler to productivity and efficiency gains.
Many manufacturers are already demonstrating the value of this approach. Organizations using process mining and workflow automation, for example, are gaining clearer visibility into their own operational bottlenecks and identifying the steps that slow down sales order processing, materials handling, invoice processing, and returns management. By using process intelligence to first diagnose process anomalies, AI can be implemented to automate and optimize these routine workflows, which improves lead times and strengthen customer satisfaction.
In addition, technologies such as digital twins are helping manufacturers understand and optimize their operations in entirely new ways. A digital twin is a virtual replica of a physical asset, process, or supply chain that updates in real time as conditions change. By running simulations inside this virtual environment, companies can test how their operations would respond to supplier disruptions, shifts in demand, or production bottlenecks without risking real-world downtime. This combination of automation, real-time visibility, and predictive modelling gives manufacturers far greater agility and resilience, enabling them to make more confident and informed decisions across their operations. Most importantly, manufacturers must recognize that AI is evolving from a supportive tool to autonomous agents capable of proactive decision-making and real-time process optimization.
The gap between AI’s promise and reality will not close on its own. Achieving meaningful results requires deliberate, practical action, thoughtful investment in the right areas, and the willingness to rethink traditional ways of working. Manufacturers who act decisively today will set the benchmark for operational excellence tomorrow. The question is not whether AI will transform manufacturing; it is whether your organization will lead that transformation or be left struggling to catch up.
Mark Humphlett
Mark Humphlett is Senior Director Industry and Product Strategy at Infor, a global leader in business cloud software products for companies in industry specific markets. Infor builds complete industry suites in the cloud and efficiently deploys technology that puts the user experience first, leverages data science, and integrates easily into existing systems. Over 65,000 organizations worldwide rely on Infor to help overcome market disruptions and achieve business-wide digital transformation.