The manufacturing industry is no stranger to artificial intelligence. Process manufacturing sectors such as chemical, pulp & paper, oil & gas, food & beverage have embedded AI routines into their systems for decades to automate workflow and product processes. But this represents only one dimension of AI adoption in manufacturing, Across the broader industry, significant opportunities remain, especially in three key areas:
- enabling the adoption of cloud platforms and applications,
- making sense of the massive amounts of data from connected assets and products,
- and augmenting the workforce
These opportunities collectively define the next phase of AI maturity in manufacturing—one where generative, agentic, and predictive AI will enable hybrid cloud connectivity, unified data intelligence, and more capable, empowered workforces.
Key trends shaping the future of manufacturing
Our 2026 Manufacturing Industry FutureScape explores how these opportunities are reshaping the sector while accounting for existing challenges and investments. Manufacturers are navigating the need to deploy cloud platforms and applications consistently across production sites, extend digital twins of products and assets across the value chain, and upskill a workforce that faces both resource and digital skills constraints. These dynamics are redefining competitiveness across the industry and shaping the key trends driving our predictions for the next five years.
AI adoption remains cautious—but accelerating.
There is slow adoption of GenAI and Agentic AI across manufacturing overall, perhaps because, as one manufacturer said, “it is [perceived as] taking away the fun part of being an engineer—problem solving.” Yet it now sits at the top of the CIO, Chief AI Officer, and VP of Manufacturing’s agenda, driven by the realization that these capabilities will only make engineering, R&D, production, and operations better. Our survey data shows that process manufacturing organizations are more mature than discrete manufacturing industries, both of which are far ahead of the energy sector. Early GenAI and Agentic AI use cases focus on design augmentation, procurement optimization, guided customer service, and enterprise quality assurance.
Data is both a challenge and a catalyst.
Manufacturers are deluged by data, and as data fabrics and foundations mature, AI adoption will accelerate—driving automation, process optimization, and workforce efficiency. IDC’s 2024 DataSphere research predicts that by 2030, all manufacturing industries will accumulate 92 exabytes of data from multiple internal and external sources—underscoring the importance of unified data platforms. It also helps that manufacturers are finally embracing cloud-based infrastructure and applications, which will make multimodal and ecosystem-driven innovation more attainable. Despite this momentum, cloud in the industrial space will likely remain a hybrid approach over the long term, varying by industry, due to regulatory and IP management concerns.
Sustainability and the energy transition take center stage.
The energy transition is underway for manufacturing and energy organizations, with a focus on (a) greenfield and brownfield facility design and sustainability, and (b) supply chain efficiency and optimization. Organizations are exploring how to integrate BIM (building information modeling) and MOM data with point-cloud scans and models of both new and legacy facilities to improve energy efficiency in buildings—which notoriously account for 30–40% of global CO₂ emissions. Scope 3 emissions regulations (which hold manufacturers accountable for environmental impacts beyond their direct operations), combined with growing customer demand, are driving investment in supply chain visibility, analytics, and optimization.
Software-defined automation becomes mission critical.
Software-defined automation for products, assets, and facilities is becoming mission critical, and low-code development tools play an increasingly important role in improving data and process flow, analytics, and collaboration. Organizations need a rapid, streamlined way to update and create new software that enhances data movement, analytics, ecosystem collaboration, and overall operations. This approach must also integrate with traditional full-code software development tools used for products and assets to ensure enterprise-wide quality and consistency.
Industry ecosystems drive innovation and resilience.
Industry ecosystems continue to drive innovation and business performance, particularly in meeting regulatory compliance and advancing sustainability and circularity. Manufacturing and energy organizations have recognized that they operate more effectively through extended ecosystems that include diverse external skills, resources, and expertise across marketing and sales, R&D and engineering, production, supply chain, and customer or field service. IDC’s four years of global survey data on industry ecosystems show that organizations taking this approach are better able to share data internally and externally, accelerate innovation, and meet customer, consumer, and citizen needs.
2026 manufacturing industry predictions
Building on these trends, IDC’s 2026 Manufacturing Industry 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: Driven by the potential of autonomous operations, by 2029 30% of factories will configure and manage control systems centrally utilizing open, virtualized, software-defined automation platforms.
- Autonomous production scheduling: By 2026, over 40% of manufacturers with a production scheduling system in place will upgrade it with AI-driven capabilities to start enabling autonomous processes.
- Agentic IT/OT connectivity: By 2027, 40% of all operational data will be integrated across applications and platforms autonomously due to increased standardization and the use of AI agents purpose-built for specific data.
- Cross functional circular field service: To close the loop between service and design, by the end of 2026 45% of G2000 OEM and aftermarket firms will use AI to connect field and engineering data, improving product and service quality.
- Predictive industrial data security: To counter data model poisoning risks, 75% of large manufacturers will use AI-enabled OT cyber defense by 2029, autonomously flagging low-level threats and cutting detection times by 60%.
- Human/robotic skills transfer: By 2028, firms that fail to design closed human-robot skill loops will face 20% higher downtime and retraining costs, and reduced efficiency compared with peers that implement bidirectional training.
- Agentic 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 worker reskilling platforms: By 2027, over 50% of manufacturers will utilize AI-enabled knowledge management tools to re-/upskill their workforce and foster collaboration across industry ecosystems.
- Hybrid AI industrial focus: By 2030, 60% of manufacturers will leverage AI Agents to build data models and manage hybrid-cloud workloads, ensuring knowledge sharing and collaboration that lowers cost of quality by 2%.
- Industrial model management: By 2027, 60% of manufacturers will leverage hyperscaler ecosystems to build, deploy, & scale new AI solutions, unlocking the value of their data and accelerating transformation.
The road ahead for manufacturers
Looking at everything as a whole, we begin to see how AI is becoming foundational to manufacturing strategy. But realizing this potential requires deliberate action, cultural change, and sustained investment.
Manufacturers today face the challenge of managing two overlapping transformations: the migration to cloud and the adoption of AI. Cultural and structural barriers remain—reluctance to share data across teams and ecosystems, uncertainty about AI’s impact on jobs, and uneven governance models all slow progress. Yet, as with past technology shifts, those who evolve will lead.
Success in this next phase requires a pragmatic, use case–driven approach. Organizations should begin experimenting with AI while establishing centers of excellence, building strong data governance frameworks, and investing in training and enablement. For IT and business leaders alike, industry-specific foundation models will be key to enabling effective and trusted use of generative, agentic, and predictive AI that can address increasingly complex industrial challenges.
The potential upside is immense. AI offers the ability to accelerate automation, strengthen data flow, and augment workforces that face ongoing skills shortages. Leading manufacturers are already treating AI as a core element of digital transformation—integrating it with cloud platforms, big data analytics, AR/VR, and emerging technologies such as blockchain.
IDC’s 2025 AI MaturityScape Benchmark confirms this trajectory: the most advanced organizations view AI not as an isolated tool, but as an enabler of enterprise-wide transformation. The lesson is clear—AI maturity grows hand in hand with digital maturity.
It is only a matter of time before AI becomes deeply embedded across the manufacturing sector. The question is no longer if—but how fast manufacturers can scale adoption to unlock new value, improve resilience, and redefine what’s possible in the next industrial era.
