The Factory of 2035: AI, Energy, People and the Future of Manufacturing

Predictions about the future of manufacturing have a habit of choosing a single protagonist. In one decade it was lean production. Then offshoring. Then Industry 4.0, 3D printing, the "lights-out" factory, and now artificial intelligence.

Each of these mattered. None, on its own, decided which manufacturers prospered. The companies that did well tended to combine new technology with the right workforce, supply chain, capital discipline and operating model, often while competitors bet heavily on one element and neglected the others.

The decade to 2035 is likely to follow the same pattern, with one important difference: more variables are changing at once. AI is moving from analysis into operational decisions. Robotics is spreading beyond automotive and electronics. Electricity demand is rising sharply in some regions, partly due to AI itself. Skilled labour is scarce in many industrial economies. Supply chains are being redesigned for resilience as well as cost. And cybersecurity has become an operational risk, not just an IT one.

This article considers how those forces may interact. It is not a forecast. The three scenarios for 2035 below are reasoned possibilities intended to support strategic thinking, not predictions. Where we cite evidence, it describes the world as it stands in late 2026. Where we look forward, we say so.

The forces shaping the next decade

AI-assisted production and decision-making

Industrial AI is already established in narrow applications: visual inspection, predictive maintenance, demand forecasting, process optimisation and scheduling. The more significant change in the coming decade may be AI's movement into decision support across functions: helping planners balance capacity and orders, guiding technicians through diagnosis, drafting quotations from drawings, summarising quality trends and coordinating responses to supply disruptions.

The constraint is less the capability of AI models than the condition of industrial data and processes. AI cannot optimise a schedule built from inaccurate routings, or predict failures from sensor data nobody stores. Many manufacturers will discover that their AI strategy is, in practice, a data-quality and process- discipline strategy.

Industrial robotics

Robot adoption continues to grow. According to the International Federation of Robotics' World Robotics 2025 report, 542,000 industrial robots were installed worldwide in 2024, with the operational stock reaching about 4.66 million units. Asia accounted for 74 percent of new installations, and China alone for 54 percent. The IFR projected continued growth, with annual installations expected to exceed 700,000 by 2028.

The more interesting shift may be qualitative. Collaborative robots, easier programming, better vision systems and AI-assisted path planning are lowering the cost of automating smaller batches and more varied tasks. That matters particularly for high-mix, low-volume manufacturers, which have historically found robotics difficult to justify.

Autonomous and semi-autonomous systems

Autonomous mobile robots for material handling, automated storage and retrieval, and semi- autonomous inspection are already in commercial use. By 2035, more factories may operate with substantial autonomy in logistics and some routine processes. Full "lights-out" manufacturing is likely to remain limited to specific high-volume, highly standardised operations. Most factories will remain human-supervised systems, with autonomy concentrated where tasks are well defined and errors are recoverable.

Additive and hybrid manufacturing

Additive manufacturing is becoming an established production tool in specific applications: complex parts, tooling, spare parts and consolidated assemblies. As discussed in our analysis of AM in 2026, the industry is maturing toward measurable production value rather than broad disruption. Hybrid approaches, which combine additive processes with machining, casting or forging, may prove as important as pure AM, particularly for repair, customisation and low-volume production.

Digital twins and industrial data

A digital twin — a continuously updated virtual model of a product, process or factory — can support design, commissioning, scheduling, maintenance and training. The value is real, but so is the effort required to build and maintain one. The likely pattern by 2035 is not one comprehensive twin of every

factory, but many focused models where the economics justify them: critical equipment, bottleneck processes, energy systems and new line commissioning.

Underneath digital twins lies a more fundamental issue: whether a manufacturer's operational data is connected, trustworthy and usable. Companies that integrate their machine, quality, maintenance, planning and commercial data will be able to adopt new tools faster than those that do not.

Energy availability and electricity demand

Energy may become one of the defining constraints on industrial competitiveness. Electrification of heat and transport, new industrial capacity and digital infrastructure are adding to electricity demand in many regions. The International Energy Agency's Energy and AI report (April 2025) projected that global electricity demand from data centres would more than double to around 945 TWh by 2030, slightly more than Japan's entire current electricity consumption. The IEA also highlighted the need to accelerate investment in generation and grids, and the uncertainty about whether energy-sector bottlenecks can be resolved in time.

For manufacturers, the implications are practical. Grid connection capacity, electricity prices, price volatility, reliability and access to low-carbon power will increasingly affect where new plants are located and how existing ones operate. Energy efficiency, on-site generation, storage and flexible operation may become sources of competitive advantage, not just cost-saving projects.

Workforce transformation and technical skills

The World Economic Forum's Future of Jobs Report 2025 estimated that, globally, around 170 million jobs would be created and 92 million displaced between 2025 and 2030, a net increase of roughly 78 million, and that about 39 percent of workers' core skills would change over that period.

In manufacturing, the shift is less about the disappearance of work than its changing composition. Demand grows for technicians who can maintain automated systems, engineers who can work with data and software, and supervisors who can manage mixed human-machine operations. Many industrial economies already face shortages of these skills, and demographic ageing in several manufacturing nations will intensify the pressure.

Automation will not remove the need for people. It will raise the importance of the people who remain.

Cybersecurity

As factories connect machines, sensors, control systems and enterprise software, the attack surface grows. Operational technology was historically designed for reliability rather than security, and many plants still run legacy control systems. A cyber incident can now stop production, damage equipment or compromise product quality and safety.

Frameworks such as the IEC 62443 series for industrial automation and control systems provide a foundation, but implementation in mid-sized manufacturers remains uneven. By 2035, cybersecurity will likely be a basic requirement of customers, insurers and regulators, not an optional differentiator.

Manufacturing geography and nearshoring

Supply chains are being redesigned around resilience, trade policy, proximity to customers and energy availability, as well as cost. Production is diversifying across Southeast Asia, India, Mexico, Eastern Europe and other locations, while China remains central to many supply networks. As we argue in our article on China+1, relocation does not automatically reduce risk. The more durable trend is toward networks designed for flexibility: multiple qualified sources, regional production for regional demand and better visibility of upstream dependencies.

Sustainability and materials efficiency

Customer requirements, regulation and investor expectations are pushing manufacturers to measure and reduce emissions, energy use and material waste. Materials efficiency — designing parts with less material, reducing scrap, remanufacturing and recycling — often aligns environmental and economic goals. Carbon reporting requirements, where they apply, are making emissions data part of the commercial conversation with customers.

Equipment investment and capital allocation

Faster technology cycles complicate capital decisions. A machine expected to run for 15 years may be overtaken by new capabilities within five. Companies are responding with more modular equipment, software-upgradable systems, leasing, outsourcing and staged investment. The ability to evaluate technology rigorously, and to avoid investing too early or too late, becomes a strategic capability in its own right.

How the forces interact

The central argument of this article is that these forces should not be assessed separately. Their interaction determines outcomes.

• AI depends on data, which depends on connected equipment, which creates cybersecurity exposure.

• Robotics reduces labour dependency, but increases dependence on skilled technicians and reliable electricity.

• Nearshoring brings production closer to customers, but may locate it where energy and skills are scarcer or more expensive.

• Electrification reduces emissions, but increases exposure to grid constraints.

• Additive manufacturing can shorten supply chains, but only with qualified processes and people.

A strategy that optimises one force while ignoring the others is likely to disappoint.

Three scenarios for 2035

The scenarios below are deliberately simplified and illustrative. They are not forecasts, and reality is likely to contain elements of all three, varying by region and industry.

Scenario 1 — High productivity: "The connected factory delivers"

An optimistic scenario.

In this scenario, AI models become reliable enough for broad operational use, and manufacturers invest steadily in the data infrastructure needed to use them. Robotics becomes cheaper and easier to deploy in small batches. Energy investment keeps pace with demand in major manufacturing regions, and low- carbon electricity becomes more widely available at stable prices. Training systems adapt, producing a growing pool of technicians and engineers comfortable with automation and data.

What factories look like: highly instrumented plants with AI-assisted planning, quality and maintenance; flexible robotic cells handling high-mix work; widespread use of digital twins for critical processes; AM and hybrid production embedded in tooling, spares and complex parts; mature cybersecurity practices.

Who wins: manufacturers that invested early in data, skills and flexible automation, including smaller firms able to use accessible, cloud-based tools to compete on responsiveness and specialisation.

Main risk within the scenario: productivity gains concentrate among leaders, widening the gap with companies that delayed investment.

Scenario 2 — Uneven adoption: "Islands of excellence"

A moderate scenario.

In this scenario, technology continues to improve, but adoption is uneven. Large firms and leading suppliers build sophisticated operations, while many mid-sized and smaller manufacturers adopt technology piecemeal: a vision system here, a cobot there, a few AI tools in the office, without integrating them. Energy constraints appear in some regions but not others. Skills shortages persist and limit how quickly automation can be deployed and maintained.

What factories look like: a wide spread, from highly automated plants to operations little changed from 2025. Many factories have pockets of advanced technology surrounded by manual processes and disconnected data. Interoperability remains a frustration.

Who wins: companies that make disciplined, high-return investments, integrate them well and build internal capability steadily. Regions with reliable energy and skilled workforces attract new investment.

Main risk within the scenario: many firms accumulate technology without accumulating productivity.

Scenario 3 — Constrained: "The limits of infrastructure"

A constrained scenario.

In this scenario, infrastructure and geopolitics become binding constraints. Grid investment lags demand in several major regions, producing high and volatile electricity prices and delays in connecting new capacity. Trade fragmentation raises costs and complicates access to key equipment, materials or components. Skills shortages worsen as older workers retire faster than new ones are trained. Cyber incidents affecting industrial systems become more frequent and costly.

What factories look like: investment shifts toward resilience over efficiency, including on-site energy, redundant suppliers, more inventory and simpler systems that are easier to secure and maintain. Automation continues, but is focused on addressing labour shortages rather than broad productivity.

Who wins: manufacturers with secure energy, diversified and well-understood supply networks, strong workforce retention and robust cybersecurity. Location decisions are driven heavily by infrastructure and political stability.

Main risk within the scenario: cost inflation and capacity constraints reduce competitiveness for energy-intensive and import-dependent manufacturers.

Comparing the scenarios

Dimension High productivity Uneven adoption Constrained

AI in operations Broad, integrated Pockets; varied value Selective; focus on labour gaps

Robotics Flexible, affordable, Growing but uneven Driven by labour widespread scarcity

Energy Adequate, increasingly Regionally uneven Binding constraint in low-carbon key regions

Workforce Skills systems adapt Persistent shortages Severe shortages

Supply chains Flexible, visible Partial diversification Fragmented; resilience networks over cost

Cybersecurity Mature practice Inconsistent Major operational risk

Small manufacturers       Compete through            Divergence between        Pressure from costs
                          accessible tools and       adopters and laggards     and constraints
                          specialisation

The view from Asia-Pacific

These forces will not play out uniformly. For manufacturers operating across Asia-Pacific, a few regional features deserve particular attention.

The region is where most robots are installed. With Asia accounting for roughly three-quarters of new industrial robot installations in the IFR's most recent data, automation norms, supplier ecosystems and skills are being set largely in this region. Manufacturers elsewhere increasingly benchmark against Asian plants, not the other way round.

Energy is becoming a locational factor within the region. The IEA identified Malaysia as one of the markets where data centres could account for a substantial share of electricity demand growth to 2030. Where industrial and digital loads compete for the same grid capacity, power availability and pricing may influence manufacturing investment decisions as much as labour costs once did.

Diversification is reshaping manufacturing maps. Southeast Asia, India and others are attracting new capacity as companies diversify supply networks, while Greater China retains supplier depth that is difficult to replicate. The practical challenge for many manufacturers is managing production across several locations at different stages of industrial maturity.

Skills systems differ sharply. Some economies in the region have strong technical education pipelines and active government support for industrial upskilling; others face shortages of experienced technicians and engineers. The pace at which advanced manufacturing can be deployed in a given location may depend more on this than on access to equipment.

For companies connecting Asian, American and European operations, these differences are not obstacles to be averaged away. They are inputs to strategy: where to automate first, where to locate energy-intensive processes, where to build skills and where to rely on partners.

Can small manufacturers compete?

Larger organisations have obvious advantages: capital, data scale, specialist teams and purchasing power. Yet smaller manufacturers have consistently found ways to compete, and several trends may help them.

• Accessible technology. Cloud software, affordable sensors, collaborative robots and AI tools reduce the minimum scale needed to adopt advanced capabilities.

• Specialisation. Deep expertise in a niche process, material or customer segment can matter more than scale.

• Speed and flexibility. Smaller firms can make decisions and change processes faster.

• Partnerships. Shared facilities, research institutes, equipment suppliers and service providers can supply capabilities a small firm cannot afford alone.

• Customer intimacy. Close relationships and responsive engineering support remain valuable in high-mix industrial markets.

The risk for smaller manufacturers is not that technology is inaccessible. It is that they adopt it opportunistically, without the data, skills and process discipline needed to turn it into productivity.

Five strategic decisions for the next three years

Uncertainty is not a reason to wait. Most of the decisions below are valuable in all three scenarios, which makes them robust starting points.

• 1. Treat industrial data as infrastructure

Decide which operational data matters most — machine, quality, maintenance, production and commercial — and invest in capturing, connecting and governing it. This underpins AI, digital twins, energy management and supply-chain visibility, and is valuable in every scenario.

• 2. Build an energy strategy, not just an energy budget

Understand current and future electricity needs, grid capacity, price exposure, opportunities for efficiency and on-site generation, and how energy availability affects future site decisions. In a constrained scenario, this may be decisive; in an optimistic one, it is still a cost and sustainability advantage.

• 3. Invest in people before machines

Map the skills required to operate and maintain the factory of the next decade, and start developing them now through training, partnerships with educational institutions, apprenticeships and retention. Automation without capable people rarely delivers its promise.

• 4. Adopt automation and AI through focused, measured deployments

Avoid both paralysis and indiscriminate adoption. Prioritise applications with clear business value, integrate them with existing systems, measure results and scale what works. Build internal capability to evaluate technology critically.

• 5. Design for resilience: supply network, cybersecurity and capital flexibility

Map upstream dependencies, qualify alternatives for critical items, raise operational-technology cybersecurity to an appropriate baseline, and prefer investment structures — modular equipment, staged commitments, outsourcing where sensible — that preserve flexibility as technology and conditions change.

Conclusion: competitive advantage will come from integration

The factory of 2035 will not be defined by a single technology. It will be defined by how well manufacturers combine automation, energy, people, supply networks and data into systems that are productive, resilient and adaptable.

That is both a challenge and an opportunity. It means no company can simply buy its way into the future by purchasing the latest equipment. But it also means that thoughtful companies of every size can build advantage through good decisions about data, energy, skills, technology adoption and resilience.

The useful question for industrial leaders is not "Which technology will win?" It is "Which capabilities will we need in every plausible future, and are we building them now?"

Omnidite Advisory works at the intersection of strategy, technology and execution, helping industrial companies evaluate emerging technologies and translate them into practical operating decisions.

This article provides general information and commercial commentary, not legal, investment, regulatory, medical or certification advice. Illustrative examples and calculations are not actual customer engagements or quotations.