How is manufacturing moving from predictive to generative and agentic AI?

Manufacturing is evolving from relying mainly on predictive AI to adopting generative and agentic AI that support more autonomous decision-making and execution. Progress is uneven because gaps in talent, skills, data, technology, and governance still prevent many firms from scaling AI beyond isolated use cases into full enterprise-wide transformation.

What are the main AI maturity highlights in manufacturing?

Manufacturing shows its highest AI maturity in principles and strategy, with strong progress in technology, data foundations, and governance. The main weakness is in talent, skills, and enablement, which still lag and limit the scaling of AI across organizations.

What AI benefits do manufacturers gain now and in the next few years?

Today, manufacturers mainly gain faster, more consistent decisions and early efficiency improvements from AI. Over the next 1–2 years, AI value is expected to shift toward sustained workforce productivity gains, lower operating costs, scalable operations, and greater organizational agility and resilience as AI reshapes how companies operate.

How does FPT help manufacturers transform with AI and engineering?

FPT helps manufacturers transform by combining large teams of data and AI engineers, a global delivery model, and AI-first platforms tailored to factory and supply chain operations. Its solutions, including Flezi OPS and Flezi UDP, boost productivity, visibility, and machine efficiency while helping firms move from isolated AI pilots to connected, scalable intelligent operations.

Where can I learn more about FPT’s AI services for manufacturing?

You can learn more about FPT’s AI-focused manufacturing services by visiting its dedicated manufacturing industry page. This page helps you explore offerings designed to keep manufacturers competitive in the evolving AI landscape.

The State of AI in Manufacturing: How Far Has the Industry Come, and Where Is It Headed?

Q: Why is manufacturing advanced in AI while many firms are only mid-maturity?

Manufacturing is seen as advanced in AI because predictive AI is widely adopted and many firms are already experimenting with agentic AI, yet most organizations still classify themselves at an intermediate stage. Being intermediate means AI has moved beyond pilots into real use, but it is not yet fully integrated and scaled across the entire enterprise.

Manufacturing is emerging as an advanced AI adopter, moving beyond experimentation into intermediate and advanced maturity, underpinned by strong AI strategies, technology foundations, and governance but still constrained by talent and skills gaps that limit scaling. As AI’s role shifts from faster decisions and early efficiency wins to augmenting workforce productivity and reshaping operating models around resilience, agility, and autonomy, manufacturers are preparing for more complex, integrated deployments. In this context, FPT uses deep engineering expertise, global delivery, and AI-first platforms like FleziPT, Flezi OPS, and Flezi UDP to help manufacturers move beyond isolated use cases toward smarter, more connected, and scalable AI-enabled operations.
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Written by

Minh Tran

Published on

September 04, 2026

the state of AI in manufacturing blog thumbnail image fpt software

From predictive AI transforming day-to-day operations to the rapid emergence of generative and agentic AI, manufacturers are moving into a new phase of AI adoption. Organizations are building the capabilities needed to improve productivity, automate processes, strengthen resilience, and create new sources of business value.

Yet, progress is uneven. While many manufacturers have moved beyond experimentation, gaps in talent, skills, data, technology, and governance can still make it difficult to scale AI from individual use cases into enterprise-wide transformation. So where does manufacturing stand today, and what value can organizations expect AI to deliver next?

Manufacturing is emerging as an advanced AI adopter

The manufacturing industry is emerging as one of the more advanced sectors in its AI maturity journey, although organizations remain at different stages of adoption. Decision-makers generally perceive their organizations as being at an intermediate stage of AI maturity: AI adoption is moving beyond experimentation and isolated use cases, but many organizations are still working toward more comprehensive, enterprise-wide, and strategically integrated deployment.

The manufacturing sector has made meaningful progress in establishing AI capabilities, while also highlighting significant opportunities to scale AI from individual initiatives into broader business transformation. According to a study conducted by Forrester Consulting on behalf of FPT, 56% of decision-makers classify their organizations as being at an Intermediate stage of AI maturity, while 26% categorize their organizations as Advanced.

Predictive AI is already seeing substantial operational adoption in manufacturing, reaching approximately 55%. At the same time, agentic AI is gaining momentum, with approximately half of organizations either experimenting with or already deploying agentic AI solutions. This combination of established predictive AI capabilities and emerging agentic AI adoption positions manufacturing for the next phase of AI-driven transformation, in which AI can increasingly support not only prediction and automation but also more autonomous decision-making and execution.

AI maturity in manufacturing: What are the highlights?

Figure 1: AI adoption maturity across transformation functions categorized by 4 stages

AI principles & strategies defined by high maturity

AI principles and strategy represent the most mature dimension for manufacturing compared with other industries. According to research, approximately 42% of manufacturing organizations have reached the highest level of maturity in this area. At this level, manufacturers have established clear AI principles and strategic frameworks that guide AI initiatives across the enterprise. These efforts are supported by active C-suite sponsorship and clearly defined value objectives, with organizations considering both tangible outcomes - such as productivity, cost efficiency, quality, and revenue growth - and intangible benefits, including employee experience, customer value, and organizational agility.

AI technology and data supported by cloud-native architecture

Manufacturing is also making strong progress in the AI technology and data foundation, with close to half of the organizations reporting that their data is trusted, discoverable, contextualized, timely, and secure enough to support predictive, generative, and agentic AI workloads. Building on this foundation, 27% have adopted cloud-native, scalable, and edge-ready architectures designed to support diverse AI workloads across increasingly distributed industrial operations to enable greater flexibility, scalability, security, and performance as AI models and applications evolve.

Additionally, manufacturers are increasingly adopting cloud-native and scalable technology architectures. The shift toward cloud-native and edge-ready architectures reflects manufacturers' need for flexible technology foundations that can support increasingly diverse AI workloads while accommodating the distributed nature of modern industrial operations.

Strong AI governance

AI governance is another area in which manufacturing demonstrates comparatively strong maturity. Manufacturing organizations show the strongest advanced governance capabilities across industries, reflecting an increasing recognition that responsible and scalable AI requires governance to be embedded into the way AI is designed, deployed, and managed. According to the research, 28% of surveyed manufacturing organizations have reached the highest level of maturity characterized by a strong balance between governance, democratization, adaptability, and innovation.

However, more talents, skills, and enablement are needed

While manufacturers are strengthening their strategy, technology, and governance foundations, talent and skills remain a significant barrier to scaling AI. The Forrester research indicates that 15% of manufacturing organizations do not have a cohesive AI talent strategy, while AI literacy remains low among employees and decision-makers and technical expertise remains scarce.

The benefits of AI in manufacturing: Today and tomorrow

Figure 2: The benefits AI can help manufacturing companies achieve across different time horizons: now, in the next 6–12 months, and in the next 2 years.

Faster decision-making and improved efficiency are the clearest benefits today - but drops sharply as a future priority

AI is embedded into everyday workflows, with the value shifting from making decisions faster to making better decisions. Specifically, faster, more consistent decision-making is the strongest benefit manufacturers have realized today (40%), but its expected importance falls sharply to 9% over 1–2 years. In contrast, workforce productivity uplift remains consistently important (25% in 1–2 years). This indicates that rather than simply replacing human work, AI is expected to continuously augment workers’ expertise and capacity, particularly where physical operations, judgment and human oversight remain essential.

Manufacturing is moving from quick wins to more complex, enterprise-wide AI implementation

Currently, manufacturers are seeing efficiency as an early-stage AI win. Indeed, efficiency is one of the most realized benefits (34%) yet falls to only 6% as a 1–2 year expectation. Instead, AI is moving from “helping people work better” to “changing how the organization operates.” Lower operating costs, scalable operations, organizational agility, and resilience all become the most prominent over 1–2 years.

Powering the future of manufacturing with AI

Going beyond software and enterprise applications, FPT extends digital intelligence into the physical world of factories and supply chains through FleziPT, its AI-first platform for enterprise transformation. Within the FleziPT ecosystem, FPT developed Flezi OPS - an AI-powered Manufacturing Operations Management (MOM) solution that helps plan, manage, and optimize factory operations amid frequent change. The solution sits above unified manufacturing data to orchestrate people, machines, and production workflows, delivered as an application with an embedded intelligence layer. Flezi OPS can improve up to 40% manufacturers’ productivity, over 90% workstation visibility, and 100% data-driven planning, with full-scale deployment achievable in approximately 4 months.

To further support manufacturing companies, FPT also introduced Flezi UDP - a built-in AI solution for anomaly detection and prediction, real-time alerts before downtime, rapid and repeatable plant rollouts, and full traceability from machine events to orders. Flezi UDP provides manufacturers with a single source of truth, and delivers measurable operational value across planning, management, operations, and maintenance. The solution enables up to 35% machine-ops savings, standardized cross-plant performance, and smarter data-driven decisions. Together, these solutions aim to help manufacturers move beyond isolated AI use cases to build smarter, more connected, scalable, and intelligent operations.

To help manufacturers address talent shortages while accelerating their AI and digital transformation journeys, FPT combines deep engineering expertise with scalable global delivery capabilities, supported by a team of more than 1,100 data engineers and 1,500+ AI engineers across 30 countries and territories. Through our agile Global Delivery Model, we enable manufacturers to flexibly access global talent and resources to deploy high-quality, cost-effective solutions across factory networks worldwide, leveraging emerging technologies including Cloud, Low-code, custom applications, AI, analytics and automation, IoT, and digital twins.

Discover our manufacturing services and stay ahead of the AI race by exploring our dedicated offerings here: https://fptsoftware.com/en/industries/manufacturing

Frequently Asked Questions

How is AI transforming the manufacturing industry?

AI is transforming manufacturing by improving decision-making, operational efficiency, workforce productivity, and business resilience. Predictive AI is already widely adopted for applications such as anomaly detection, predictive maintenance, forecasting, and process optimization, with approximately 55% of manufacturers adopting predictive AI operationally. As generative and agentic AI mature, manufacturers are moving beyond prediction and automation toward AI-assisted decision-making, workflow orchestration, and more autonomous execution. Over the next 1–2 years, the greatest value is expected to come from higher workforce productivity, lower operating costs, scalable operations, greater agility, and stronger resilience.

Why is manufacturing advanced in AI while many firms are only mid-maturity?

Manufacturing is relatively advanced in AI because the industry has high-value, data-rich, repeatable processes where AI can deliver measurable operational benefits. Factories continuously generate data from machines, sensors, production lines, quality systems, maintenance records, supply chains, and workforce activities, creating strong foundations for predictive AI, anomaly detection, optimization, and automation. At the same time, manufacturers face constant pressure to improve productivity, reduce downtime and costs, increase quality, respond faster to disruptions, and manage increasingly complex operations. These pressures make AI a practical business priority rather than simply an innovation experiment. Manufacturing is also progressing in areas such as AI strategy, technology and data foundations, cloud-native and edge architectures, and governance, with many organizations already moving predictive AI into day-to-day operations and beginning to experiment with generative and agentic AI.

However, being an advanced AI adopter does not mean that most manufacturers have achieved enterprise-wide AI maturity. The industry remains uneven: according to the Forrester research cited above, 56% of manufacturing decision-makers classify their organizations as Intermediate, compared with 26% at Advanced maturity. Scaling AI beyond individual use cases is difficult because manufacturers still face fragmented data, legacy systems, inconsistent processes across plants, limited AI talent and literacy, technology-integration challenges, and the need for strong governance and change management. Many organizations have proven that AI works in specific areas but have not yet built the capabilities to replicate those successes across factories and functions. This explains the apparent contradiction: manufacturing is ahead of many industries in adopting and operationalizing AI, yet most individual manufacturers are still in the middle of their maturity journey - moving from isolated productivity and efficiency gains toward enterprise-wide intelligence, autonomous decision-making, resilience, and scalable transformation.

What are the current AI maturity state in manufacturing?

Manufacturing industry demonstrates the strongest AI maturity compared to all other industries (healthcare, automotive, insurance, banking, and more). Specifically, the industry is mature in AI principles, strategy, and governance, with organizations increasingly establishing clear AI priorities, executive sponsorship, value-driven roadmaps, and responsible governance frameworks. Significant progress is also being made in technology and data foundations, including trusted and contextualized data, cloud-native and scalable architectures, and infrastructure capable of supporting predictive, generative, and emerging agentic AI across distributed operations. However, talent, skills, and organizational enablement remain the critical maturity gap. Limited AI expertise, low AI literacy, and the shortage of people who can bridge AI with deep manufacturing knowledge make it difficult to move beyond isolated use cases, scale successful solutions across plants and functions, and translate strong strategies and technology foundations into enterprise-wide, sustainable AI transformation.

What benefits do manufacturers gain from AI now and in the next few years?

Today, manufacturers are primarily realizing AI value through faster, more consistent decision-making and immediate efficiency gains, helping teams respond more quickly to operational issues and optimize day-to-day processes. Over the next 1–2 years, the value of AI is expected to shift from short-term productivity and decision acceleration toward broader, sustained business transformation, with greater workforce productivity, lower operating costs, more scalable operations, and stronger organizational agility and resilience. As AI becomes increasingly embedded in workflows and evolves from prediction and assistance toward recommendation, orchestration, and autonomous execution, it is expected to reshape how manufacturers operate, allocate resources, respond to disruption, and create long-term competitive advantage.

What are the biggest challenges to scaling AI in manufacturing?

The biggest barriers to scaling AI in manufacturing are talent and skills shortages, data quality and accessibility, legacy technology, governance, and the complexity of deploying AI across multiple plants and operations. While manufacturers are making strong progress in AI strategy, technology, data foundations, and governance, talent and organizational enablement remain key gaps. According to the research, 56% of manufacturing decision-makers consider their organizations to be at an Intermediate level of AI maturity, while 26% consider them Advanced, showing that many companies have moved beyond experimentation but are still working to scale AI from individual use cases into enterprise-wide transformation.

Which FPT's AI solutions can support manufacturing companies and how do they deliver the results?

FPT brings digital intelligence closer to the physical operations of factories and supply chains through FleziPT, its AI-first platform designed to drive enterprise transformation. As part of the FleziPT ecosystem, Flezi OPS is an AI-powered Manufacturing Operations Management (MOM) solution that enables manufacturers to plan, manage, and optimize production in dynamic operating environments. Built on unified manufacturing data, Flezi OPS combines an application layer with embedded intelligence to coordinate people, machines, and production workflows. It can deliver up to 40% higher productivity, more than 90% workstation visibility, and 100% data-driven planning, with full-scale implementation possible in approximately four months.

Complementing Flezi OPS, Flezi UDP provides an AI-driven approach to manufacturing performance and equipment management, combining anomaly detection, predictive capabilities, real-time downtime alerts, and end-to-end traceability from machine events to production orders. Designed for rapid and repeatable deployment across plants, Flezi UDP creates a single source of truth for operational data and helps manufacturers improve planning, operations, maintenance, and performance management. By enabling up to 35% savings in machine operations, standardized performance across plants, and more informed data-driven decisions, Flezi UDP helps manufacturers build more connected, proactive, and scalable operations.