Envisioning the future of manufacturing using AI

The manufacturing industry is not immune to artificial intelligence. Process manufacturing sectors such as chemicals, pulp and paper, oil and gas, and food and beverage have been embedding AI routines into their systems for decades to automate workflows and product processes. But this is just one aspect of AI implementation in manufacturing. Significant opportunities remain across the broad industry, particularly in three key areas:

Enables the adoption of cloud platforms and applications,
Make sense of large amounts of data from connected assets and products
and strengthen the workforce

Collectively, these opportunities define the next stage of AI maturity in manufacturing. Generative, agentic, and predictive AI are enabling hybrid cloud connectivity, unified data intelligence, and a more capable and empowered workforce.

Our 2026 Manufacturing Outlook explores how these opportunities will reshape the sector, while considering existing challenges and investments. Manufacturers are deploying cloud platforms and applications consistently across the production floor, extending digital twins of products and assets across the value chain, and addressing the need to upskill employees facing both resource and digital skills constraints. These dynamics are redefining competitiveness across the industry and forming key trends that will drive forecasts over the next five years.

Adoption of AI remains cautious, but it is accelerating.

The slow adoption of GenAI and Agentic AI across manufacturing is likely due to the following reasons, as one manufacturer put it: [perceived as] It takes away the fun part of being an engineer: solving problems. ” But now CIOs, chief AI officers, and VPs of manufacturing are sitting at the top, driven by the realization that these capabilities will only improve engineering, R&D, production, and operations. Our research data shows that process manufacturing organizations are more mature than the discrete manufacturing industry, and both are much more advanced than the energy sector. Initial GenAI and Agentic AI use cases are focused on design enhancement, procurement optimization, guided customer service, and enterprise quality assurance.

Data is both a challenge and a catalyst.

Manufacturers are exposed to massive amounts of data, and as data fabrics and foundations mature, AI adoption will accelerate, driving automation, process optimization, and workforce efficiency. It also helps that manufacturers are finally starting to embrace cloud-based infrastructure and applications, which makes multimodal, ecosystem-driven innovation more possible. Despite this momentum, industrial cloud will likely remain a hybrid approach in the long term, depending on the industry, due to regulatory and intellectual property management concerns.

Sustainability and the energy transition will play a central role.

Manufacturing and energy organizations are undergoing an energy transition with a focus on (a) greenfield and brownfield facility design and sustainability, and (b) supply chain efficiency and optimization. Organizations are exploring ways to integrate BIM (Building Information Modeling) and MOM data with point cloud scans and models of new and legacy facilities to improve energy efficiency in buildings, which are notorious for accounting for 30-40% of global CO₂ emissions. Scope 3 emissions regulations, which hold manufacturers accountable for environmental impacts beyond their direct operations, combined with increased customer demand, are driving investment in supply chain visibility, analytics, and optimization.

Software-defined automation becomes mission-critical.

Software-defined automation of products, assets, and equipment has become mission-critical, and low-code development tools are playing an increasingly important role in improving data and process flows, analytics, and collaboration. Organizations need a fast and streamlined way to update and create new software that enhances data movement, analytics, ecosystem collaboration, and overall operations. This approach should also be integrated with traditional full-code software development tools used for products and assets to ensure quality and consistency across the enterprise.

Industry ecosystems drive innovation and resilience.

The industry ecosystem continues to drive innovation and performance, particularly in meeting regulatory compliance and promoting sustainability and circularity. Manufacturing and energy organizations recognize that they can operate more effectively through an expanded ecosystem that includes diverse external skills, resources, and expertise across marketing and sales, research and development and engineering, production, supply chain, and customer or field service. IDC’s four years of global research data on industry ecosystems shows that organizations that take this approach can share data internally and externally, accelerate innovation, and meet the needs of their customers, consumers, and citizens.

Based on these trends, IDC’s 2026 Manufacturing FutureScape identifies 10 key predictions that highlight how AI will reshape operations, supply chains, and workforce strategies across the manufacturing ecosystem over the next five years.

Software defined factory: The potential for autonomous operations means that by 2029, 30% of factories will leverage open, virtualized, software-defined automation platforms to centrally configure and manage control systems.
Autonomous production scheduling: By 2026, more than 40% of manufacturers with production scheduling systems in place will upgrade their systems with AI-driven capabilities and begin enabling autonomous processes.
Agent-like IT/OT connectivity: By 2027, 40% of all operational data will be autonomously integrated across applications and platforms, driven by increased standardization and the use of data-specific AI agents.
Cross-departmental circular field service: To close the loop between service and design, by the end of 2026, 45% of G2000 OEMs and aftermarket companies will use AI to connect field and engineering data to improve product and service quality.
Predictable industrial data security: To combat the risk of data model poisoning, 75% of leading manufacturers will use AI-enabled OT cyber defenses by 2029 to autonomously flag low-level threats and reduce detection time by 60%.
Skill transfer between humans and robots: By 2028, companies that fail to design a closed human-robot skill loop will face 20% more downtime and retraining costs and reduced efficiency compared to peers that have implemented two-way training.
Agent product/process simulation: By 2028, 65% of G1000 manufacturers will use AI agents in conjunction with design and simulation tools to continuously validate design changes and configurations/variants against product requirements.
Connected employee reskilling platform: By 2027, more than 50% of manufacturers will leverage AI-enabled knowledge management tools to reskill and upskill employees and foster collaboration across the industry ecosystem.
Focus on hybrid AI industry: By 2030, 60% of manufacturers will leverage AI agents to build data models, manage hybrid cloud workloads, and ensure knowledge sharing and collaboration to reduce quality costs by 2%.
industrial model management: By 2027, 60% of manufacturers will leverage hyperscaler ecosystems to build, deploy, and scale new AI solutions to unlock the value of data and accelerate transformation.

When you look at everything holistically, you start to see that AI is becoming a cornerstone of manufacturing strategy. But realizing this potential will require intentional action, cultural change, and sustained investment.

Manufacturers today face the challenge of managing two overlapping transformations: moving to the cloud and deploying AI. Cultural and structural barriers remain, with reluctance to share data across teams and ecosystems, uncertainty about how AI will impact work, and uneven governance models all slow progress. But as with past technological changes, those who evolve will be in control.

Success in this next stage requires a pragmatic, use case-driven approach. Organizations need to start experimenting with AI while establishing centers of excellence, building strong data governance frameworks, and investing in training and enablement. For both IT and business leaders, industry-specific foundational models are key to enabling the effective and reliable use of generative, agential, and predictive AI to address increasingly complex industry challenges.

The potential benefits are enormous. AI provides the ability to accelerate automation, enhance data flows, and augment workforces facing ongoing skills shortages. Leading manufacturers are already treating AI as a core element of their digital transformation, integrating it with emerging technologies such as cloud platforms, big data analytics, AR/VR, and blockchain.

IDC’s 2025 AI MaturityScape Benchmark The most advanced organizations view AI not as an isolated tool, but as an enabler for enterprise-wide transformation. The lesson is clear. AI maturity grows hand in hand with digital maturity.