Why Your Manufacturing Strategy Needs Agentic AI
It’s time to talk about what’s truly happening in the world of manufacturing technology. Forget flashy claims and dry discussions of minor efficiency gains. We’re in the middle of a fundamental, tectonic shift. A near-future, sooner-than-you-think extinction-level event is looming. While the failure to embrace Agentic AI is an existential threat, creating competition from the 24/7 autonomous factory, the true value and key to success lies not in the machines, but in the dramatic increase in human capability it enables.
The Benefits of Agentic AI in Manufacturing
As I noted in one of my earlier articles, AI in Operational Technology: Unlocking Value Through Industrial Data, AI is a force multiplier. It brings non-linear improvements in capability. Used strategically, wisely, and intelligently, AI is a differentiator. Agentic AI brings high optimization and low friction for everyone from the supply chain through manufacturing, and all the way into the hands of consumers.
McKinsey notes in Empowering Advanced Industries with Agentic AI:
The bottom line: Agentic AI is not just a productivity tool. It is a new revenue engine—a strategic lever that could reshape cost structures, organizational models, and leadership KPIs.
The biggest idea to grasp is this: we are moving into an era of Agentic AI enablement on the way to Ambient AI. People – not just the technology – are the vital element in making all of this work.
While the threat of autonomous competition is real, the true value of Agentic AI is not just in faster, better, more capable machines, but in the dramatic increase in human capability it enables. The intelligent factory's future hinges on the workforce's ability to adapt and become technocrats, overseeing and mastering these new systems through AI literacy, Human-in-the-Loop protocols, and a foundation of empathy to ensure trust and communication.
Agentic AI is the Means to Ambient AI
Ambient AI is the final phase of pervasive AI—AI all around—where AI is seamlessly integrated and embedded in an environment, enabling AI agents to sense, intuit, and respond appropriately, thereby enhancing the system's capabilities.
The foundation of Agentic AI is the agentic mesh, an orchestration layer that connects multiple autonomous agents and enables them to work in harmony. This mesh is built upon four core principles, which together ensure the system is flexible, scalable, and robust:
Composability: New tools, models, or agents can be added without changing the core system architecture.
Distributed Intelligence: This enables agents to coordinate their efforts and divide complex tasks across multiple networks.
Layered Decoupling: Separating logic, memory, orchestration, and the interface into distinct layers significantly enhances the system's modularity and maintainability.
Vendor Neutrality: This can be seen as grounded in a single source of truth that is governed, contextualized, and powers distributed intelligence
Agentic AI is an AI system that doesn’t just answer questions, but takes goals, breaks them into steps, and acts autonomously—using tools, data, and feedback loops—to drive toward an outcome while staying within defined constraints and policies.
Agentic AI is:
Goal‑oriented: Starts from a clear objective and plans a path to get there.
Action‑taking: Calls APIs, triggers workflows, or updates systems instead of only returning text.
Self‑monitoring: Observes results, adjusts the plan, and retries or escalates when things don’t go as expected.
Top Challenges to Implementing AI in Manufacturing
Several "blockers" or challenges that can impede the adoption of Agentic and Ambient AI are:
Legacy Infrastructure: The "physical plant capacity" must match digital goals; aging or disconnected factories cannot support these advanced systems.
Data Silos: Traditional silos between the shop floor and design studios can prevent real-time problem-solving.
High Costs: Implementing these technologies is expensive and requires strong financials and significant capital for both technology and facility upgrades.
Organizational Silos: Like data silos, the systems of human organization limit adoption and moves to Agentic AI
Fear and Lack of Trust: People often fear what they do not understand; without a deep "field understanding" and focus on empathy, systems will not be trusted by the workforce or customers.
Inadequate Workforce Skills: A lack of AI literacy and mastery of new workflows prevents wide-scale deployment.
How Manufacturers Can Start Building Agentic AI Systems
Agentic AI strategy demands a human perspective. The following are the principal components of a multi-stage approach for enterprises to prepare for and deploy these technologies.
Embed Human-in-the-Loop: Ensure that human oversight, ethical controls, and safety protocols remain central to the design of autonomous systems.
Build AI Literacy: Establish foundational AI knowledge as a universal requirement across the workforce.
Master Tools and Workflows: Develop advanced skills in using AI tools and designing AI-driven workflows.
Systematize Innovation: Implement a "Observe, Test, Prove, Scale, and Learn" cycle where innovation is data-driven and measured by standardized KPIs across all platforms.
Invest in Physical Infrastructure: Upgrade physical plants and capacity to match digital goals, as you cannot "digitize an aging, disconnected factory in decay".
The Innovation Cycle
The innovation cycle looks the same for Agentic AI as it does for humans. They are synergistic. Humans build and operate Agentic AI systems. In Agentic AI systems, this very same process is outlined in the diagram below. They just do this on time scales of 10, 100, 1000, and even millions of times faster than people.
Here again, the sponsors, designers, operators, and beneficiaries (end users) of the Agentic AI-powered manufacturing are integral to both the human and machine loops. Data collection, enrichment, contextualization, and all related data operations are the sine qua non of this process. They are not the goal. From cycle to cycle and over the lifetime of manufacturing systems, people must be able to extract meaning and value from the systems they build, operate, and use.
Perspectives on Agentic AI in Operational Technology (OT)
Shift from Historical Use to Transformative Use
For years, Machine Learning (ML) and AI in the operational technology (OT) space have been sophisticated tools. AI could recommend a fix: "If you see X, then you should do Y." It was an advisor, feedback, and a message on an HMI screen. But in the emerging Agentic AI space, AI shifts from being a tool that recommends to an agent that does real work.
We have reached the limits of previous attempts at traditional optimization. Efficiency gains are not enough to offer a lasting competitive edge. Instead, they are prerequisites. As Praveen Rao, Global Head of Manufacturing, Google Cloud, states:
For two decades, manufacturing has been defined by a relentless pursuit of optimization. We automated assembly lines, digitized records and built predictive maintenance models, all in the service of marginal gains in efficiency.
While this approach yielded significant returns, we have reached the ceiling of what traditional, rule-based automation can achieve. In 2026, the industry is undergoing a fundamental shift: moving beyond the "if-then" logic of the past toward a model of agentic enablement.
Think of the difference between a self-driving car that tells you what lane to be in, as compared to a fully autonomous self-driving car. A driver, passenger, or other person sharing the road with an autonomous driver wants and needs absolute trust that the AI agents will make the right decisions.
This shift to autonomous work is having far-reaching effects, changing everything from the factory floor to the global supply chain. Supply chain optimization is increasingly critical for ever-tightening margins. Agentic AI plays a vital role in the supply chain. Autonomous Supply Chain optimization can reduce waste, increase worker productivity, and improve financial performance. Its role must be planned, watched, and guarded.
Everyone from the plant floor to the finance and executive levels wants just-in-time ordering, optimal inventory levels, zero production delays, and high assurance across the entire value chain. Ordering too many raw materials is just as bad as ordering too little. In the end, such errors ultimately harm not just the company but the customer. Human oversight, through conscientious design with safety protocols and ethical controls, is critical. Guardrails are a must. Human-in-the-loop will always be absolutely essential.
Agentic AI - What Does It Look Like?
What does Agentic AI actually look like in practice? It’s not about replacing people; it’s about creating technocrats. Technocrats are technical experts who make decisions based on objective data and expertise. The operators of tomorrow won't be managing simple machines; they’ll be agentically enhanced users managing advanced, autonomous capabilities like never before.
Industrial Workforce Upskilling
Companies are already preparing their workforce to oversee and optimize AI-driven systems rather than just maintain them. This journey includes three stages: building AI literacy, mastering tools and workflows, and deploying Agentic AI widely.
Foundational: Make AI literacy a universal requirement.
Mastery: Develop advanced tool use and workflow design skills.
Deployment: Embed Agentic AI widely across functions and teams.
Ambient AI: The final phase of pervasive AI that responds, learns, and adapts to human situations, ultimately making manufacturing lines safer and lives better.
The workforce transforms, but reducing headcount is not the primary goal; the dramatic increase in human capability is. Certainly, the machines and the processes attain all new levels of productivity, efficiency, and usefulness. All of this is dwarfed by the benefit Agentic AI brings to the human side of the equation.
If you are looking for a full roadmap, read our whitepaper The Blueprint for Agentic AI in Industrial Operations, for a detailed framework on how to design, deploy, and scale agent-based systems in real-world industrial environments.
Supply Chain Optimization
But the most dramatic change in store for Agentic AI in manufacturing is in resilience and optimization. By 2026, the industry is looking toward self-healing supply chains. Imagine a system that autonomously detects disruptions, such as a delay from a critical raw material supplier. Instead of sending an alert and waiting for a human purchasing manager to investigate the next morning, the AI agent cross-references alternative, vetted sources, initiates a purchase order, and adjusts the production schedule autonomously. This signaling and adjustment happen all overnight, without human intervention. Connected worker systems automatically update workers’ tasks in response to supply chain changes. That’s supply chain resilience on a level previously only dreamed about. The supply chain is responsive and protects against the bullwhip effect
Strategic Opportunities with Agentic AI
The opportunity here is massive and impactful: moving from merely collecting data, predicting failures, and feeding reports and processes to actively planning and executing solutions in service of human goals. Here again, the human factor is critical—oversight, safety, and approval for critical or high-risk decisions. Advisory information is data-driven and pervasive. Agents make use of this prevalence of information. They turn the information they collect into knowledge.
Market Potential - Finance and Operations
According to McKinsey, the potential of Agentic AI is “no longer theoretical”. McKinsey reports:
Revenue Growth: Projected annual increase of $450 billion to $650 billion, representing a 5% to 10% uplift in industries like automotive.
Cost Efficiency: Potential reduction in operational costs by 30% to 50% through the automation of complex, repetitive tasks.
Speed and Innovation: Transactional cycle times have already been seen dropping from days to minutes
Manufacturing AI-Agents - The Real-Time Loop
Manufacturing agents can bridge the traditional silo between the shop floor and design studios, allowing manufacturability issues to be resolved in real time and instantly optimizing processes that once took weeks of meetings and testing. The Innovation Imperative is Scout, Test, Scale, and Learn.
True, enduring success in any industry is built on a steady stream of high-quality products, but sustaining that promise requires constant recalibration. In the Agentic AI era, innovation itself must become systematized. Agentic AI and the processes it fosters live on relevant, high-quality data and information, both at the machine-to-machine and human levels.
Agentic AI-Powered Innovation
The innovation is driven by survey, test/prototype, scale, learn, repeat. Innovation loops need to be short and informed. AI-driven systems are used to both discover and implement new technologies, test them through pilots, and critically, measure their impact on both operations and consumer sentiment. They do not live on their own merits as judged by the builders or designers. To be effective, this process and supporting systems standardize Key Performance Indicators (KPIs) across all internal and external platforms—from manufacturing OT dashboards to marketing media platforms, and from product-level to deep measurement of end-customer usage.
Consistent measurement, across the board, is what makes innovation digestible. It allows every team—from the creative department to the retail floor—to iterate from the same set of data. This is how you build feedback loops, not just one-off flash-in-the-pan.
Empathy
Another element that’s often overlooked is that the firsthand insights gathered there feed directly into product, packaging, marketing, and advertising decisions. The consumers of products and services, whether direct end consumers or other businesses, have goals and values. Product value and customer loyalty tend to increase when consumers know a manufacturer understands them and shares similar ideals. They care about how a product is positioned, the goals designers have, and the values producers and manufacturers hold. Incorporating field insights ensures the AI data, which is all about optimization and efficiency, is grounded in real-world human empathy. There simply is no substitute for field understanding.
People have an innate tendency to fear what they don’t understand. They won’t trust what they fear. Systems that truly and deeply embody a deep field understanding of customers is essential in building products that are safe, humane, and trusted.
Agentic AI and Market Forces
While the long-term benefit of AI is efficiency and operational excellence, the immediate financial opportunity for incumbents lies in a responsive blend of market economics and pricing strategy. Agentic AI is not cheap.
Inflation, world events, pandemics, government policies, and other economic forces have altered global economies lately. Consider this representative scenario, one that has played out many times recently with major manufacturers.
When commodity prices spiked, these companies aggressively raised consumer-facing prices by large percentages to offset costs. Now that raw material prices are or will be retreating from record highs, such a company is not planning to lower its retail prices. This puts the company in a strong financial position. The company's higher prices offset lower raw-material costs, resulting in a substantial financial benefit.
To sustain these digital ambitions, however, requires a parallel commitment to the physical world. One leading firm is executing a significant investment strategy that includes building new, cutting-edge facilities and upgrading long-existing sites with new, optimized production lines.
This is the financial prerequisite for Agentic AI: the physical plant capacity must match the company’s digital goals. Strong financials and sufficient capital are needed to pursue both corporate expansions and major technology investments is non-negotiable. You can’t digitize an aging, disconnected factory in decay.
Realizing the Next Agentic AI-Powered Assembly Line
“You can have any color Model T as long as it is black.” - Quote attributed to Henry Ford
Well, not quite. There is far more to Agentic AI than mere assembly lines. Critical as the assembly line was in the early days of automation and as it remains so, the entire periphery surrounding the assembly line is just as important. Deep customer understanding feeds directly into product design and development. The manner in which a product is manufactured directly impacts the value chain. The whole constellation of raw materials supply chain, through manufacturing to distribution, marketing, and sales, runs on data and information. Pervasive, Agentic AI uses this information at every point. The resulting system of systems is one where the whole is far more valuable than the sum of its parts.
Henry Ford pioneered the assembly line, which ushered in the age of mass production. Mass customization is a promise that has yet to be realized. Agentic AI is just the technology shift needed to fully capitalize on economies of scale and mass customization. Manufacturers now have a line of sight and a path to provide mass customization without taking a hit on supply chain inefficiencies. Products can be built in more controlled ways with great insights into customer use, cost, value, and lifecycles. Consumers and purchasers of intermediate goods in the supply chain now stand a better chance than ever of getting what they really want. This is good for everyone.
Adaptability and Market Resilience
Finally, there is market resilience. In a world of shifting consumer trends, there were fears that demand for certain categories would collapse. These fears were amplified by slow time-to-market and deflated or nonexistent product development incubation. Resilient companies collect data, turn it into information, and turn that into knowledge. And, ultimately, these resilient companies turn that knowledge into wisdom.
Having this acquired knowledge and wisdom, coupled with nimble, agile, and intelligent product-to-market capabilities, the most advanced companies can turn a risky, shifting consumer market into a competitive advantage rather than a product-killing risk. Agentic AI lets companies move fast and respond to consumer and market demands. This resilience further secures the investment in the underlying manufacturing technology.
Solving Real-World Challenges for Agentic AI Adoption
The solutions to the challenges that impede the adoption of Agentic and Ambient AI are best addressed through a multi-stage approach, which includes the following.
Solve Legacy Infrastructure & High Costs
To unlock the full value of AI in manufacturing, organizations first have to address legacy infrastructure and cost pressures head-on. That starts with investing in the physical footprint—plants, equipment, and capacity—that can actually support their digital ambitions. This is the financial prerequisite for everything that follows; you cannot “digitize an aging, disconnected factory in decay” and expect modern, resilient outcomes. The capital program and the digital program have to move together; AI simply exposes the limits of the underlying infrastructure faster and more painfully.
Address Inadequate Workforce Skills & Fear/Lack of Trust
In parallel, the workforce challenge is as much about mindset and trust as it is about technical skills. The shift to AI and automation needs to be framed as a massive opportunity for people, not a countdown to being left behind. The goal is to elevate operators, engineers, and planners into a higher-value “technocrat” role (defined later), where they govern intelligent systems rather than compete with them. That reframing is the starting point for reducing fear and motivating adaptation.
From there, organizations need to establish AI literacy as a universal baseline—everyone should understand core concepts, language, and limitations. On top of that, they must deliberately build deeper capability in AI tools and workflows, so people know how to use agents and models in the context of actual day-to-day work.
Crucially, human-in-the-loop cannot be treated as a footnote at the end of the architecture deck; it must be visible from the first communication as the primary safety and trust mechanism. Transparent, empathetic communication about where humans retain authority, how they intervene, and what ethical and safety controls are in place is the immediate antidote to fear—not just a feature of the final system. When this is backed by a structured change-management program and a clear career path into technocrat roles, upskilling stops feeling like a burden and becomes a promotion path.
Bridge Data Silos and Systematize Innovation
Once the physical and human foundations are in motion, the next step is to break data silos and turn innovation from isolated projects into a disciplined practice. That means running a repeatable “Observe, Test, Prove, Scale, and Learn” cycle in which ideas are evaluated using shared KPIs across plants, lines, and functions. Innovation should flow end-to-end—from raw materials and product design, through manufacturing and supply chain, into customer adoption and product lifecycle—not as point solutions bolted onto a single line.
Manufacturing agents then become the connective tissue between traditionally separated domains: they pull real-time signals from the shop floor and bring them directly into design studios and planning functions, so manufacturability issues are identified and resolved in the moment, rather than months later. As these agents extend into areas like supply chain—enabling self-healing responses to disruptions—the human role again shifts toward governing policies and approving the rules that drive automated decisions.
The financial upside often quoted in the market (hundreds of billions in potential revenue and efficiency gains) will only materialize if the human workforce fully adopts and masters these new workflows. In other words, the people operating as technocrats are the multiplier on the returns from both infrastructure and AI investments.
The Market for Agentic AI
Ultimately, the Total Addressable Market (TAM) for Agentic AI and self-healing systems is not some abstract dollar figure. It’s an absolute necessity like air or water. The industry is rapidly moving toward a "sovereign data" model in which the "how" of production—the proprietary processes, operational logic, and data it generates—becomes a manufacturer's most valuable intellectual property. We are seeing this shift across many markets, not least of which in manufacturing. Major AI Computing manufacturers are prioritizing sovereignty over data and AI, capitalizing on the growing trends and customer demand.
The addressable market for these technologies is, in effect, a company's entire global supply chain and manufacturing footprint. It is the very infrastructure they must modernize to survive. Any firm that fails to adopt these systems will eventually find itself competing against faster, autonomous, and more efficient "intelligent factories." As such, adopting and weaving Agentic AI and subsequent innovations, improvements, and tooling is a vital aspect of all modern manufacturing companies, whether they know it or not.
The shift is fundamental. It's not about making a line 5% or 10% faster; it's about fundamentally changing how things are made, how companies innovate, and how they secure their financial future. The time for Agentic AI is now. And it is non-negotiable.
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Bill Sommers
Bill Sommers is a Technical Account Manager at HiveMQ, where he champions customer success by bridging technical expertise with IoT innovation. With a strong background in capacity planning, Kubernetes, cloud-native integration, and microservices, Bill brings extensive experience across diverse domains, including healthcare, financial services, academia, and the public sector. At HiveMQ, he guides customers in leveraging MQTT, HiveMQ, UNS, and Sparkplug to drive digital transformation and Industry 4.0 initiatives. A skilled advocate for customer needs, he ensures seamless technical support, fosters satisfaction, and contributes to the MQTT community through technical insights and code contributions.
