The transformative potential of Agentic AI is being steadily realized across industries. Agentic AI refers to autonomous AI systems composed of multiple collaborating agents that can plan, act, adapt and learn with minimal human intervention, moving beyond prediction to full-cycle execution. As with any emerging technology, there is no shortage of hype. However, unlike earlier waves, Agentic AI is already demonstrating operational value in live industrial environments.
For manufacturing, the shift is particularly profound.
According to research from the Manufacturing Leadership Council, the adoption of Agentic AI within the manufacturing industry is set to increase fourfold over the next two years.1
This surge will help propel the manufacturing-focused AI market beyond USD 155 Billion in value by 2030, up from just USD 34 Billion in 2025.2
This evolution builds on the foundation laid by our 2024 Generative AI-in-Manufacturing analysis, which explored how Generative AI re-shaped knowledge work, decision support and digital engineering. Agentic AI represents the next frontier, where AI systems don’t just support decisions; they execute them intelligently across the physical-digital value chain.
As Agentic AI rapidly becomes the backbone of intelligent manufacturing systems, this paper explores where it is already generating impact, framed through the same five-stage manufacturing lifecycle used in the previous 2024 article.
Agentic AI Across the Manufacturing Value Chain
As the data shows, forward-looking manufacturing companies have already begun their Agentic AI journeys. These early adopters are using the technology to re-imagine decision-making processes, elevate production lines, enhance product quality and minimize waste.
Below, we explore five stages of the manufacturing lifecycle and how Agentic AI is transforming each one.
1.
R&D
Agentic AI is set to supercharge the creative process at the start of the manufacturing lifecycle, with R&D enhancements a transformative use case for the technology. Capable of exploring material combinations, testing product variants and analyzing performance outcomes at unprecedented speed, Agentic AI in manufacturing R&D represents a creative accelerant.
This new paradigm is borne out by the latest research, which reveals that AI can accelerate R&D processes by 20 to 80 percent for industries producing complex manufactured products, when harnessed optimally.3 The scope of Agentic AI empowerment within R&D can range from material innovation and creative prototyping through to testing and refining late-stage product designs.
Use cases are myriad and transformative, including identifying new customer needs, synthesizing existing research and data, automating documentation tasks and collaborating with humans for ideation. Siemens Gamesa is one organization showcasing this potential in action, harnessing AI to generate new wind turbine designs that are materially more efficient and cost-effective than existing specifications.4
As these capabilities combine, agentic systems can quickly become the always-on digital researchers of an enterprise, learning from experiments, refining hypotheses and uncovering breakthroughs far faster than traditional methods.
2.
Sourcing and Procurement
Sourcing raw materials, energy, components and intellectual property is traditionally labor- and data-intensive. Agentic AI in sourcing and procurement introduces a step change by not only analyzing information but also acting on it within pre-defined frameworks, reducing manual effort and accelerating decision cycles.
Agentic AI can autonomously scan global supplier databases, assess compliance against tender specifications and initiate preliminary engagement. Unlike traditional AI, which stops at insights, Agentic AI can execute tasks, such as scheduling follow-up calls or requesting documentation, streamlining supplier onboarding. Similarly, agents can continuously monitor financial health, geopolitical developments and operational performance across suppliers. When risk indicators emerge, such as bankruptcy signals or logistics bottlenecks, contingency actions can be triggered.
Impacts are just as revolutionary when it comes to dynamic procurement and demand alignment. By integrating historical sales, market trends and real-time signals, Agentic AI can forecast demand and autonomously place purchase orders within an approved threshold, reducing overstocking and optimizing working capital. Amazon’s Project Eluna showcases this agentic foresight in action, pulling in historical and real-time data to anticipate bottlenecks, keep operations running smoothly and reduce cognitive load.5
Crucially, Agentic AI doesn’t just flag and correct potential issues; it learns from them. Through this continuous learning and adaptive decision-making, the technology transforms procurement from a transactional function into a strategic driver of innovation and long-term business value.
3.
Production
Imagine a factory floor where machines don’t just follow instructions; they interpret, adjust and optimize in real-time. It’s a future being enabled by Agentic AI as manufacturing shifts from reactive to proactive. In traditional settings, process adjustments rely on manual oversight and scheduled reviews. Agentic systems, however, act autonomously, monitoring real-time data, identifying inefficiencies and fine-tuning operations without human intervention.
These AI systems can continuously optimize production parameters such as temperature, speed and material flow, adapting to changing conditions to ensure consistency. The result is a factory that behaves more like a living system — self-aware, self-correcting and always improving.
The deployment of digital twins in manufacturing — virtual, real-time replicas of physical assets and production environments — is fast becoming another cornerstone of smart enterprises, enabling to model, test and optimize without disrupting live operations. Nevertheless, their true potential is unlocked when paired with Agentic AI, turning digital twins into dynamic, self-improving ecosystems that accelerate optimization.
In a manufacturing setting, for instance, Agentic AI could contextualize and feed real-time data into a digital twin, ensuring the twin has large volumes of diverse data on which it thrives. Digital twins, meanwhile, can refine and validate Agentic AI output, ensuring only the best strategies are implemented, all without waiting for human approval.
This symbiotic relationship has the potential to unlock trillions in economic value, with McKinsey research revealing that 75 percent of large enterprises are already actively investing in digital twins to scale AI solutions.6 The adoption of digital twins is expected to grow by 36 percent over the next five years, powering smarter factories and better decision-making.7
BMW’s Virtual Factory is a prime example, replicating over 30 production sites in a real-time, 3D virtual world. It integrates building, equipment, logistics and vehicle data with 3D simulations of manual processes, enabling planners to virtually design, test and optimize production workflows before physical implementation. Recent expansion has seen the addition of Agentic AI assistants to make planning even more adaptive.8
4.
Distribution and Logistics
While manufacturers have been quick to embrace automation on the factory floor, many still rely on manual processes and delayed decision-making when it comes to getting products to market. Whether to retailers or directly to consumers, future-ready manufacturing requires strong, adaptive logistics. By harnessing Agentic AI, manufacturers can drive autonomous supply chain management, with improved responsiveness and the ability to absorb global disruptions in hours or minutes rather than weeks.
Take supply chain planning, for example. Currently, supply chain automation relies on static rules, scheduled replenishments and reactive adjustments. Agentic AI takes this one step further. Instead of simply executing pre-defined tasks, AI agents can act as a network of intelligent collaborators, orchestrating inventory flows and logistics dynamically.
These agents can re-route shipments during disruptions, re-balance stock across regions and even negotiate supplier terms autonomously, delivering resilience at scale.9
This potential is fueling a supply chain digital twin market projected to reach USD 8.7 Billion in value by 2033, with an anticipated compound annual growth rate of 12 percent during 2024-2033.10
This visibility can also enable manufacturers to optimize for any aspect across the end-to-end value chain, with energy and resource efficiency a prime example. Based on similar principles, a network of agentic assistants can autonomously analyze traffic patterns, fleet performance and delivery deadlines to generate and execute optimized routing plans. Agents can autonomously re-schedule deliveries, consolidate loads and adjust routes in real-time, reducing fuel consumption, labor costs and environmental impact.
5.
Customer Service and Maintenance
As well as optimizing conditions for production, Agentic AI offers a proactive solution to another nemesis of manufacturing: Disruptions and downtime. Predictive maintenance systems, powered by agentic capabilities, can enable organizations to monitor equipment health, forecast failures and autonomously schedule interventions before breakdowns occur, relegating downtime to a thing of the past.
Unlike traditional maintenance models based on fixed schedules or reactive repairs, Agentic AI leverages sensor data, historical performance and environmental inputs to anticipate issues in real-time. These systems can prioritize tasks, order parts and re-route workflows, minimizing disruption and maximizing uptime, with this transparency and foresight able to be passed onto end customers, enhancing experiences.
The benefits are significant.
According to research from the National Institute of Standards and Technology (NIST), predictive maintenance techniques can reduce maintenance cost by between 15 and 98 percent, cut equipment downtime by 35 to 45 percent and reduce defects by 65 to 95 percent, revealing why it represents a foundational pillar of smart manufacturing.11
Looking ahead, agentic systems of the future will use computer vision, sensor fusion and adaptive algorithms to detect anomalies mid-process and adjust parameters instantly. According to McKinsey, manufacturers deploying Agentic AI in such ways have experienced rapid improvements to defect detection, while transaction cycle times have been reduced from days to hours or even minutes. 12
Partnering to Unlock Agentic AI’s Transformative Potential
There is plenty of buzz around Agentic AI. However, as organizations begin to wrestle with its potential, it is clear the hype is real. Looking ahead, Agentic AI is set to rapidly transform the industrial landscape, whether through optimizing energy use, predicting machine failures or discovering new materials, among other game-changing impacts. It’s why more than 77 percent of manufacturers plan to increase investments in AI over the next 12 months, with 71 percent expecting increases of over 10 percent. 13
For manufacturers, however, significant hurdles exist in moving from agentic pilots to scaled deployment enterprise-wide:
As enterprises embrace autonomous manufacturing systems, Agentic AI offers a once-in-a-generation opportunity to re-think how factories operate, how supply chains respond and how innovation accelerates. The next wave of industrial transformation will belong to enterprises that combine domain expertise, robust digital foundations and the right ecosystem partnerships to scale agentic capabilities safely and confidently.
To explore how Agentic AI can unlock the next frontier of industrial transformation, connect with our experts.