The manufacturing industry has entered 2026 with both confidence and caution. Optimism is running high as years of investment in digitalization, automation and advanced analytics reach critical mass, unlocking capabilities that were once aspirational. Data from the National Association of Manufacturers reveals that ~70 percent of industry leaders have a positive outlook for their companies in 2026, encouraged by the promise of intelligent, adaptive operations.1
Yet uncertainty still looms large.
Geopolitical volatility, trade disruptions and regulatory complexity continue to challenge global manufacturers, with a majority (52 percent) of risk experts anticipating an unsettled outlook through to 2027.2
Skills gaps — a top concern for more than a third of manufacturing executives — and aging workforces are adding pressure, while cyber threats and carbon mandates further raise the stakes.3
With the forces of change unrelenting and disruption coming from unexpected quarters, success in 2026 will require an approach that unites next-generation capabilities in all-new ways. Here, we explore five trends set to define the industry in the year ahead that firms can harness to solve 2026’s unique challenges. In doing so, manufacturers can move the dial from enterprise experimentation toward operational excellence instead and turn uncertainty into a competitive advantage.
1. Dynamic, ‘Living’ Supply Chains for Intelligent Manufacturing in 2026
Supply chains in 2026 are no longer rigid, linear systems. Instead, they’re evolving into ‘living’ networks that sense, adapt and grow, delivering resilience and agility at scale — a defining shift within today’s smart manufacturing supply chains. It’s a shift made possible by a critical mass of investments in IoT, predictive analytics and Agentic AI and their successful integration, with planning, sourcing and logistics connected and sharing a single source of truth.
This connectivity enables real-time visibility and proactive intervention, enabling manufacturers to move from static forecasts to dynamic decision-making, reducing dependency on manual intervention and outdated processes. The result is a supply chain that behaves less like a clunky workflow and more like a living organism — reactive and self-improving. This strengthens overall supply chain resilience in manufacturing.
Toyota’s centralized intelligence hub is a case in point. It merges supplier, commodity and logistics data, with AI models scanning for early warning signs, shipment delays in secondary ports or production anomalies that previously went unnoticed, responding preemptively.4
The urgency of this shift is underscored by the challenges many manufacturers still face. Fragmented ERP landscapes, limited visibility across dispersed stocking locations and slow, manual planning cycles continue to constrain service levels while driving excess inventory and carrying costs. One global manufacturer confronting these issues across more than 50 stocking locations addressed them by deploying a scalable, analytics-led inventory planning model that restored end-to-end visibility and enabled real-time decision-making. The result was a significant improvement in on-time-in-full delivery, alongside lower carrying costs and faster, more confident inventory decisions.
The benefits are profound. Living supply chains enable faster recovery from shocks, optimize inventory without sacrificing service levels and unlock cost efficiencies through predictive re-shopping and automated compliance.
However, according to the Capgemini Research Institute (CRI), five in six organizations feel ill-equipped to accommodate the new supply chain paradigm, despite recognizing its significant business impact.5
In 2026 and beyond, leading manufacturers will treat highly dynamic supply chains not as an aspiration, but as a strategic imperative, closing the preparedness gap and building supply networks designed to thrive amid volatility rather than react to it.
2. Agentic Excellence in Manufacturing Operations
If 2025 was about experimenting with AI in manufacturing, 2026 is about achieving results, with Agentic AI rapidly becoming pervasive across manufacturing systems.6 This next AI wave — defined by autonomous systems composed of multiple collaborating AI agents that can plan, act, adapt and learn with minimal human intervention — is approaching quickly.
Adoption of Agentic AI in manufacturing will increase fourfold within the next two years, by which time one in four manufacturers will be fully harnessing its power.7
Persistent skills gaps and aging workforces are two challenges that Agentic AI will address, automating decision-making and executing tasks under governance frameworks with human-in-the-loop oversight. Agentic systems can re-configure production lines, issue maintenance commands and generate shift handover reports and work instructions autonomously. They can even capture institutional knowledge from retiring employees while making roles more attractive to younger generations.
The wider impact is transformative. Manufacturers are embedding AI into planning, quality control and predictive maintenance, creating environments where machines learn, adapt and act autonomously. Siemens’ advanced AI agents represent a prime example of this shift in action, capable of proactively executing entire processes without human intervention, enabling humans to focus on innovation, creativity and complex problem-solving.8
3. Resilience-as-a-Service
Persistent macroeconomic volatility, geopolitical tension and supply chain fragility are also pushing manufacturers to re-think how they prepare for disruption in 2026. Traditional continuity plans are proving inadequate in a world where shocks, from trade disputes to raw material shortages, can emerge in an instant, driving the industry to begin embracing an era of ‘Resilience as a Service.’
Resilience-as-a-service models unite scenario analytics, supply chain control towers and planning-as-a-service to deliver real-time dashboards that track exposure to tariffs, logistics bottlenecks and supplier risk in real-time, enabling manufacturers to pivot quickly.
The need to build such capabilities is urgent, with 73 percent of US manufacturers currently citing trade uncertainty as their top business challenge.9
This need is particularly acute where commercial decisions must be made under volatile market conditions. For example, one leading beverage manufacturer faced growing uncertainty around how sensitive sales volumes were to price changes and how to set optimal pricing ranges without eroding market share. By adopting a scenario-based planning approach that modeled price elasticity alongside competitive dynamics, the company was able to evaluate the impact of pricing decisions before execution, improving sell-through volumes while reducing trade spend. This strengthened its ability to hedge against volatility rather than react to it.
Resilience-as-a-service also fuels regionalization strategies such as near-shoring, friend-shoring and multi-sourcing, as companies seek to build resilience into their supply chains and improve control.10 Vianode’s large-scale production facility for advanced battery materials in Ontario is one case in point, with the location chosen to help support a secure North American supply chain for critical minerals that power strategic industries.11
4. Next-gen Cybersecurity
Cyber threats are emerging as one of the most urgent risks for manufacturers in 2026. The industry continues to face the highest volume of email-based cyber-attacks, accounting for 26 percent of all recorded e-mail threats in 2025 — more than any other sector.12 The stakes couldn’t be higher: The 2025 attacks on Jaguar Land Rover halted production for over a month, costing an estimated GBP 50 Million per week.13
These events underscore a harsh reality — manufacturing’s digital transformation has left it exposed. Legacy operational technology, still critical to many plants, is particularly vulnerable. As manufacturers integrate IoT sensors, robotics and AI into their operations, these older systems often lack modern security protocols, creating entry points for attackers.
In response, leading organizations are investing heavily in next-gen cybersecurity, deploying zero-trust architectures, AI-driven threat detection and secure remote access across all machinery and IT systems. In one example, a global manufacturer operating across more than 150 locations faced significant exposure due to fragmented security infrastructure, limited in-house security capacity and a lack of real-time threat visibility. By establishing a unified security framework — including centralized malware protection, standardized patch management and 24×7 threat intelligence — the organization improved visibility and control across its global estate and began embedding cybersecurity as a shared operational responsibility rather than a purely technical function.
Crucially, they also establish a culture of cybersecurity across the organization, with technology alone not enough. Creating such a culture is increasingly a strategic priority, and we can expect to see regular training programs rolled out at every level of the enterprise, along with simulations to strengthen workforce awareness. In doing so, organizations can protect production continuity, safeguard intellectual property and maintain trust in an era where the cost of compromise is measured in millions and minutes.
5. Data-driven Disclosures
We can also expect sustainability reporting to take on a new guise in 2026, becoming a strategic capability as mandatory Environmental, Social and Governance (ESG) disclosures and carbon pricing push manufacturers toward finance-grade data, assurance and reporting operations. In this sense, robust, transparent ESG reporting in manufacturing will become the new license to operate.
This shift, however, represents a complex challenge.
According to Capgemini, the industrial sector accounts for over 35 percent of global greenhouse gas emissions, with nearly 70 percent stemming from Scope 3 emissions across suppliers and customers.14
Capturing and validating this data demands advanced analytics and integrated platforms.
Manufacturers should be ready for changing customer requirements, new regulations and ESG mandates. The cost of scrambling to meet sustainability rules far outweighs the investment in proactive strategies. A better approach is to view sustainability as an opportunity for differentiation, innovation and new business models. Success will require collaboration up and down the value chain to maximize benefits and minimize burden from compliance efforts.
Leading organizations are already proving what’s possible. One heavy industry firm, for example, has used digital twins and AI to cut kiln energy use by 15 percent and shorten development cycles by 70 percent, demonstrating how data-driven sustainability can deliver both environmental and operational gains.15
As ESG requirements continue to evolve, manufacturers face increasing pressure to deliver accurate, timely and defensible disclosures. Reactive, spreadsheet-led approaches are proving inadequate, especially when reporting cycles shorten and regulatory expectations rise. The challenge is not only collecting ESG data but also doing so at scale, with consistency, auditability and forward-looking insight.
One case in point illustrates how this challenge is beginning to be addressed. A global enterprise implemented an AI-powered ESG analytics platform — automating data collection and standardization across business units, applying AI-enabled taxonomy mapping and enrichment, and embedding greenhouse gas calculation and forecasting into its reporting processes. As a result, the organization reduced manual effort in ESG data collection and reporting and accelerated disclosure cycles while improving audit readiness and regulatory compliance.
Beyond compliance, such capabilities create the foundation for more informed sustainability decision-making, enabling organizations to track progress against ESG targets, model emission trajectories and anticipate regulatory risk. In 2026 and beyond, the ability to operationalize ESG data in this way will increasingly differentiate manufacturers that can treat sustainability as a disciplined, data-driven function rather than an episodic reporting exercise.
Unlocking the Future of Manufacturing in 2026
The manufacturing industry in 2026 stands at a pivotal moment. Years of incremental improvements are converging to create an opportunity for industry-wide transformation, driven by technology, regulations and the need to be hyper-agile in an increasingly unpredictable world.
Promisingly, industry leaders are embracing the challenge.
For example, 77 percent of manufacturers are planning to increase investments in AI over the next 12 months, and 71 percent expect increases of over 10 percent.16
This kind of investment ensures that living supply chains, agentic systems, next-generation cybersecurity and re-imagined resilience no longer represent future concepts but new capabilities.
Transformation at this scale, however, cannot be achieved in isolation. Many manufacturers are seeking partners who bring deep domain expertise, advanced analytics and flexible operating models to help them on their transformation journey. Doing so can accelerate adoption and minimize risk.
From embedding AI into frontline workflows to building new ESG reporting systems, success will hinge on collaboration across ecosystems — suppliers, technology providers and strategic service partners. Those who act quickly can unlock new efficiencies, business models and sources of competitive advantage in 2026 and beyond, thriving in complexity and proactively shaping the next era of intelligent manufacturing.
Dive deeper into the capabilities shaping the future of intelligent manufacturing.