Despite rapid advances in robotics, AI, and analytics, a majority of factories are only semi-automated at best. Adoption barriers often stem not from scepticism about technology itself, but from grounded business realities. Manufacturers remain cautious about automation largely because the business case is more complex than the technology pitch.

For many, automation is a strategic decision that must align with existing capital cycles, workforce readiness, and operational risk tolerances. The following five factors explain why even efficiency-driven manufacturers continue to move cautiously.

1. High capital intensity and total cost of ownership

Automation still shows up as a big line item rather than an operating tweak. Beyond the sticker price of robots, AMRs, conveyors, and vision systems, manufacturers must fund safety enclosures, upgrades to PLCs (programmable logic controllers), network infrastructure, and integration engineering that can easily double project costs.

For SMEs, this clashes with thin margins and conservative lending, especially in cyclical sectors like automotive or construction materials. Even where financing is available, boards now expect robust payback within two to three years, not open-ended “strategic” investments.

Warehouses face similar dynamics: integrating ASRS (automated storage and retrieval systems) or AMRs (autonomous mobile robots) into brownfield facilities means retrofitting racking, conveyors, and WMS (warehouse management system) interfaces, which pushes total cost of ownership far beyond brochure estimates. As a result, many operators default to incremental investments — low-cost sensors, semi-automated work cells, or leasing models — rather than committing to fully robotic lines.

2. Integration headaches with legacy infrastructure

Most factories are not greenfield “lights-out” showcases but a patchwork of older CNCs, bespoke fixtures, and partially digitalized lines. Connecting new robots, AGVs, and vision systems into this environment often requires custom mechanical design, nonstandard I/O mapping, and middleware to bridge incompatible protocols.

Integration firms report that for complex lines, engineering and commissioning can exceed the hardware cost, especially when you add parts presentation systems, safety redesign, and line balancing. Downtime is another deterrent: taking a profitable line offline for weeks to reconfigure conveyors, change layouts, and debug PLC logic is a hard sell to operations leaders.

In warehouses, AMR and shuttle deployments must sync reliably with existing WMS and ERP stacks, which often lack clean APIs or real-time inventory accuracy. These integration challenges are amplified in multi-site rollouts, where each plant has unique legacy equipment and tribal process knowledge.

3. Workforce resistance and skills constraints

Automation is as much an organizational change program as a technology investment. Yet many manufacturers underestimate the cultural and skills transformation required to move from manual or semi-automated workflows to robot-centered operations.

Operators and supervisors worry, often legitimately, that new systems will reduce headcount, compress overtime, or shift power toward central engineering. Surveys show sizeable pockets of employees who fear job displacement or lack confidence in their ability to master programming, troubleshooting, or data-driven decision-making.

Unions can slow approvals until retraining commitments, redeployment paths, and safety assurances are codified. At the same time, there is a chronic shortage of technicians and engineers who can design, maintain, and optimize robotic cells, particularly in smaller regional markets.

This creates a double bind: plants need skilled people to deploy automation, but they are pursuing automation partly because they cannot hire enough skilled people. Without deliberate communication, inclusive planning, and funded upskilling programs, resistance at the shop-floor level frequently converts strategic enthusiasm into stalled pilots or half-implemented systems.

4. ROI uncertainty in volatile, high-mix environments

On PowerPoint, automation RoI looks straightforward: higher throughput, fewer defects, lower labor cost. On the plant floor, the math gets messy. Demand volatility, SKU proliferation, and frequent engineering changes mean robots are rarely running the same process, at the same volume, for long stretches of time.

In high-mix, low-volume environments — common in job shops, contract manufacturing, and specialty warehouses — reprogramming robots, redesigning grippers, or reconfiguring ASRS logic can erode gains and extend payback timelines well beyond initial projections. Executives also factor in hidden costs: performance ramp-up, debugging, training, spare parts, software subscriptions, and cybersecurity.

Many report past experiences where early automation projects under-delivered, creating institutional skepticism about vendor ROI claims. Warehouse operators, likewise, worry about committing to systems that may not flex easily with omnichannel peaks, seasonal promotions, or changes in packaging and order profiles. In this context, maintaining human-driven flexibility can feel pragmatic.

5. Reliability, serviceability, and operational risk

For plant managers accountable to uptime KPIs, automation introduces a new class of single points of failure. A stalled palletizing robot, misaligned vision sensor, or frozen WCS (warehouse control system) can halt an entire line or warehouse zone, with cascading impacts on delivery SLAs.

In many regions, especially outside major industrial hubs, access to certified service technicians and rapid-response support is limited, extending mean time to repair when issues arise. Even with remote diagnostics, plants often need on-site intervention for mechanical failures, safety interlock problems, or PLC-level bugs.

Predictive maintenance, digital twins, and real-time monitoring promise better resilience, but these tools themselves require data quality, integration, and specialized skills that many mid-market manufacturers are still building. Past experiences with brittle automation — systems that function well only under ideal conditions and fail unpredictably under dust, temperature swings, or product variation — reinforce caution.

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China’s robots may yet prove less a novelty than a policy tool, according to Morgan Stanley. Analysts at the investment bank predict that robots, and especially humanoids, could help extend the China’s grip on manufacturing, just as electric vehicles did starting about a decade ago.

The bank’s latest note, led by its Chief Economist in Asia, Chetan Ahya, argues that humanoids and other robots will become a key driver of China’s export machine over the next five to 10 years. It repeated its forecast that China’s share of global exports will rise from 15 percent now to 16.5 percent by 2030.

The analysts also said Chinese makers already account for roughly 90 percent of the 13,000 to 16,000 humanoid robots shipped globally last year, with the US and Japan still mostly at prototype stage. China’s annual humanoid sales, they said, could more than double to about 28,000 units this year, more than any other economy.

The bigger story is not about one country’s robot tally, but about industrial policy in an age of tightening supply chains and slower growth. Across the world, governments are treating automation as a strategic asset: a way to offset labour shortages, preserve export competitiveness and defend manufacturing share. China has gone further than most, folding embodied AI and advanced automation into its broader push for “future industries.”

The analysts from Morgan Stanley said, “looking ahead, humanoids and robots will be the next key driver of China’s export machinery over the coming 5 to 10 years.” It added that the industry’s development echoes the rise of electric vehicles a decade ago.

Morgan Stanley’s case rests on scale, state support and a supply chain that already dominates many core components. In its broader humanoid research, published last year, the bank estimated the market could reach $5 trillion by 2050 and said China could have 302.3 million humanoids in use by then, the most of any country. It also warned that the technology remains expensive, imperfect and years away from mass household adoption.

That caution matters. The report is bullish on China’s industrial advantage, but it does not claim humanoids will transform factories overnight. For now, the more immediate lesson is that Beijing appears determined to use robots to deepen an old strength rather than invent a new one. In that sense, the bet is less on science fiction than on familiar industrial discipline, aided by cheap production, abundant suppliers and a state willing to back winners.

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techUK has published its first robotics report, arguing that Britain has the ingredients to lead the next phase of robotics, but needs to move faster on adoption, Britain’s leading trade association said in a post. The report says the prize could be worth up to £150 billion (about $202 billion) in gross value added over the next decade if robots and smart machines are taken up more widely.

The document is, in effect, a plea to turn research strength into commercial scale. techUK says advances in artificial intelligence, sensing, computing power and advanced materials are making robots more capable, connected and autonomous, with applications already spreading across healthcare, manufacturing, agriculture and infrastructure.

Across rich economies, robotics is becoming less a question of novelty than of industrial policy. Countries that combine research, manufacturing capacity and permissive regulation are best placed to convert automation into higher productivity, better services and new business models; those that do not risk watching their inventions commercialised elsewhere.

“Something significant is happening in the world of robotics, and it is happening fast,” Rory Daniels, head of emerging technology and innovation at techUK, said in the post on the association’s website. “The UK must double down on its areas of leadership and leverage these to support innovators, accelerate deployment, and empower our most successful companies to scale and export”.

techUK’s central argument is that the chief obstacle is not invention but uptake. Many UK firms, especially small and medium-sized enterprises, have yet to deploy even off-the-shelf robotics tools, a gap the report says must close if productivity is to rise and the economic value of innovation is to be captured at home.

To make that case tangible, the report cites examples from across its membership. Siemens says AI-powered robotic picking software can identify and grasp unknown objects in milliseconds, while the Manufacturing Technology Centre says robotic systems for tunnel installation have delivered up to 40 percent productivity gains and 30 percent cost reductions, alongside safety improvements.

The report also points to Airbus Defence and Space’s work on satellite servicing, debris removal and autonomous assembly, and to Oxa, which has raised £103 million to scale autonomous vehicle technology across heavy industrial logistics, light logistics and asset monitoring.

techUK sets out nine recommendations, including treating robotics as a frontier technology, strengthening the Department for Science, Innovation and Technology’s robotics capacity, using public procurement to drive adoption, and making robotics an explicit priority in the government’s Scale-Up Support Service.

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Accenture, Vodafone Procure & Connect and SAP are testing whether humanoid robots can do more than theatrically mimic human labour on a trade-show floor. In a warehouse in Duisburg, Germany, the partners say they have shown that physical AI can help spot safety risks, inspection failures and idle space with real-time reporting into SAP’s warehouse system.

The pilot matters because warehouse automation is moving from fixed machinery to systems that can observe, decide and act in less structured environments. That is a broader shift in industrial technology: firms want robots that can work beside people, use enterprise data and justify themselves not just as labour substitutes, but as tools for safety, compliance and operational control.

Against that backdrop, the partners are presenting the work at Hannover Messe 2026, where industrial software, robotics and supply-chain automation increasingly overlap. The appeal is not just cost-cutting, but the prospect of a new operating model in which physical AI feeds live data back into business systems and, eventually, into new services.

“Trained in digital twins and powered by physical AI, humanoid robots can reduce worker injuries and other warehouse safety incidents and lower overtime costs and the dependency on temporary labor,” Christian Souche, Accenture’s advanced robotics lead, said in a press release.

The pilot used humanoid robots at Vodafone Procure & Connect’s warehouse to carry out visual inspections through SAP Extended Warehouse Management. According to the release, the robots detected misplaced or damaged products, checked pallet stacking and weight distribution, identified unused storage space and flagged hazards such as obstacles in aisles or misaligned pallets.

SAP handled the integration with the warehouse management system, while Accenture built the robot intelligence and operating framework. The companies did not present this as a finished commercial product, but as a proving ground for a future “humanoid workforce” model that could be scaled if the economics and reliability hold up.

The humanoid robots used in the pilot are powered by Accenture’s Robot Brain solution. They are trained in digital twins of warehouse environments, built on Accenture’s Physical AI Orchestrator, which uses NVIDIA Omniverse libraries, the Mega NVIDIA Omniverse Blueprint and the NVIDIA Metropolis libraries and Blueprint for video search and summarization for the deployment of visual AI agents.

Vodafone’s logistics chief, Reinhard Stefan Plaza Bartsch, said the pilot was meant to explore “how humanoid robotics can improve efficiency, safety and operational visibility” and to clarify how such capabilities might scale across the supply chain. That is a sober ambition, and a sensible one: warehouses are where the promises of embodied AI will be judged by productivity, not by spectacle.

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Accenture has invested, through its venture arm, in General Robotics, an AI-native company that says its software can let organisations deploy and adapt robots of any form for any task. The companies will work together to help manufacturers, logistics groups and other asset-heavy industries move towards autonomous operations powered by physical AI.

The bet is small in balance-sheet terms, but it sits squarely in a larger industrial shift. Across factories and warehouses, firms are trying to push robots beyond isolated demonstrations and into systems that can learn, coordinate and adapt as production changes, a harder problem than simply adding more machines to the floor. That is why the race is increasingly about software layers, simulation and orchestration, not only motors, sensors and arms.

General Robotics’ pitch is that it offers a general-purpose intelligence layer that can sit across different robots and help them work with less custom programming. In Accenture’s telling, the investment is meant to speed deployment and make robotics more practical at scale for clients that want more output without proportionately larger workforces.

“Robotics powered by physical AI tackle challenges our clients encounter, including labor shortages, diminished productivity in factories and warehouses, and the ongoing increase in both capital and operational expenditures,” Prasad Satyavolu, Accenture’s global lead for manufacturing and operations, said in a press release. He added that the partnership would focus on an “enterprise-grade robotics intelligence and orchestration layer” to help companies deploy systems “safely, efficiently, faster, and at scale”.

General Robotics, founded in 2023 and based in Redmond, Washington, says its GRID platform connects robots, agents and AI models through a single system. The company says the platform relies on modular AI capabilities, cloud orchestration and simulation rather than fixed programming. Accenture did not disclose financial terms.

Ashish Kapoor, General Robotics’ chief executive and co-founder, added: “We’re providing the intelligence grid that connects robots, agents and AI models through a single platform designed to speed deployment and adapt as AI advances and robotic tasks become more sophisticated”.

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Google DeepMind has unveiled Gemini Robotics-ER 1.6, a reasoning model meant to help robots understand the physical world with greater precision, from spatial judgment to task planning and success detection. The upgrade also improves multi-view understanding and adds instrument reading, a capability developed with Boston Dynamics, according to a blog post shared by DeepMind’s Laura Graesser and Peng Xu.

The model is meant to serve as a high-level brain for robots, able to call tools such as Google Search, vision-language-action models and third-party functions as part of its planning. DeepMind says it performs better than both Gemini Robotics-ER 1.5 and Gemini 3.0 Flash on tasks such as pointing, counting and telling whether a job has been completed.

The announcement matters beyond one lab’s technical progress. Robotics has long been constrained less by hardware than by the difficulty of making machines reason reliably in messy, changing environments; advances in embodied reasoning could therefore accelerate use cases in factories, warehouses, inspection and service robotics worldwide.

“We are also unlocking a new capability: instrument reading, enabling robots to read complex gauges and sight glasses — a use case we discovered through close collaboration with our partner, Boston Dynamics,” Graesser and Xu said in the post.

DeepMind frames success detection as central to autonomy, arguing that robots must know not just how to act, but whether an action has worked. It also says Gemini Robotics-ER 1.6 improves multi-view reasoning, allowing the system to relate multiple camera feeds even when scenes are dynamic or partly obscured.

The company is also pitching the model as its “safest robotics model to date,” claiming superior compliance with safety policies on adversarial spatial reasoning tasks. That line will matter to industrial users, who want robots that are not merely clever but predictable. DeepMind says it is inviting developers to submit labelled images showing failure modes, suggesting the work remains a collaboration rather than a finished product.

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Industrial automation is accelerating as economies worldwide prioritise the integration of factory robots to bolster productivity. According to the World Robotics 2025 report by the International Federation of Robotics (IFR), Western European countries reached a record density of 267 robots per 10,000 manufacturing employees in 2024. This figure places the region ahead of North America, which recorded 204 units, and Asia, which averaged 131 units.

The European Union maintains a robot density of 231 units, significantly above the global average of 132. Within the region, Germany remains the primary driver, ranking third globally with 449 units per 10,000 employees, following an average annual growth of 5 percent since 2019. Other European nations in the global top ten include Sweden, Denmark, and Slovenia.

The global surge in automation reflects a fundamental realignment of industrial strategy as nations grapple with tightening labor markets and the imperative for supply-chain resilience. In an era where demographic decline and rising costs threaten traditional manufacturing models, robot density has emerged as a critical indicator of an economy’s ability to maintain its industrial edge without relying on a growing pool of human labor.

“The robot density metric provides a uniform basis for comparison by relating the total number of robots used in a country to its economic size, as measured by its workforce,” Takayuki Ito, President of the International Federation of Robotics, said in a post.

The Republic of Korea maintains the world’s highest robot density with 1,220 robots per 10,000 employees, benefiting from its dominant electronics and automotive sectors. Singapore follows in second place with 818 units, while Japan ranks fourth with 446. The United States ranks eighth worldwide with 307 units, representing a 4 percent year-on-year increase.

However, the most significant shifts are occurring in China. Although its density ranks 22nd globally at 166 units, its operational stock of 2 million units is the largest in the world — approximately 4.5 times that of Japan. In 2024 alone, China accounted for 54 percent of all global robot installations, deploying 295,000 units as it seeks to automate its vast manufacturing base.

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Agile Robots, a German maker of versatile robotic hardware, has struck a research partnership with Google DeepMind to infuse its machines with advanced artificial intelligence. The collaboration marries DeepMind’s Gemini Robotics foundation models — capable of multimodal reasoning — with Agile’s actuators, sensors and full-stack platforms.

The aim is to create adaptable systems for manufacturing, from electronics assembly to heavy logistics, where robots must handle the unpredictable.

The two firms will exchange real-world data to refine their technologies: Agile’s deployments, numbering over 20,000 units across factories in China, Europe and America, will feed DeepMind’s models, while the AI sharpens Agile’s hardware for tasks demanding vision, touch and planning. Initial applications target precision sectors such as 3C electronics, new energy vehicles, data-centre maintenance and heavy industry. Agile’s existing wares, blending German engineering with mass scalability, now gain a shot at true autonomy.

This pact underscores a pivotal shift in robotics, where disembodied AI meets physical form amid a $200 billion industrial-robotics market strained by labour shortages and geopolitical supply risks. As America’s Figure AI and China’s state-backed humanoids vie for dominance, Europe’s entry via DeepMind, Alphabet’s moonshot arm, hints at a transatlantic counterweight, potentially accelerating the dawn of factories run by thinking machines rather than mere repeaters.

“The huge opportunity ahead lies in autonomous, intelligent production systems that can transform entire industries. Integrating Google DeepMind’s Gemini Robotics models into our robotic solutions positions us at the cutting edge of this rapidly growing market,” Zhaopeng Chen, founder and chief executive of Agile Robots, said in a press release.

Yet factories pose sterner tests than labs: dust, variability and safety rules have dashed prior AI dreams. Agile’s foothold and DeepMind’s prowess in complex reasoning offer grounds for cautious optimism. Carolina Parada, head of robotics at DeepMind, hails the tie-up as “a vital step towards realising embodied AI that learns and adapts like never before”. Success could redefine toil from Shenzhen to Stuttgart.

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Jeff Bezos, the founder of Amazon, is in early talks to raise about $100 billion for a new investment vehicle that would buy traditional manufacturing companies and overhaul them with artificial intelligence, according to a report in the Wall Street Journal, echoed by Reuters and other outlets.

The effort is described as a “manufacturing transformation vehicle” focused on industrial mainstays such as chipmaking, aerospace and defence, according to the news reports. Bezos has been courting some of the world’s biggest asset managers, as well as sovereign-wealth funds in the Middle East and Asia, during recent trips to the region.

The fund, which would rival SoftBank’s $100 billion Vision Fund in sheer scale, marks one of the most ambitious attempts yet to wire AI directly into the foundations of heavy industry rather than the consumer internet. If successful, it would accelerate a broader shift in which capital and algorithmic know-how migrate from software and advertising to factories, supply chains and the “real economy.”

It also underscores how a small set of tech billionaires now treat large buyout pools as strategic weapons in an emerging contest over who controls the data and industrial capacity on which future AI systems will feed.

At $100 billion, the proposed pool would give Bezos a war chest big enough to roll up mid-sized industrial firms that are struggling with labor costs, capital-intensive retooling and the investment required to digitise their plants. For governments anxious about productivity, reshoring and defence supply chains, such a vehicle could look like a timely private complement to industrial policy — or, to critics, the beginnings of an unaccountable industrial conglomerate built in AI’s image.

The various news reports call the venture a bid to “drive and speed up automation” inside acquired companies, using AI models being developed at Project Prometheus, Bezos’s industrial AI startup. Bezos serves as co-founder and co-CEO of Project Prometheus, which is separately seeking up to $6 billion in fresh capital.

“The goal is to transform legacy manufacturers into AI-first companies that can design, simulate and operate complex systems far more efficiently than today,” one investor presentation says, according to people familiar with the pitch, as reported by the WSJ.

Labor advocates warn that aggressive automation could hollow out middle-skill jobs, even as investors bet that AI-optimised plants will pay for themselves through higher throughput and fatter margins.

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BMW Group is bringing humanoid robots into series production in Germany for the first time, extending an ambitious push to fuse artificial intelligence with factory automation. The carmaker will run a pilot at its Leipzig plant using AEON, a humanoid robot developed by long-standing partner Hexagon, to support assembly of high‑voltage batteries and component manufacturing from summer 2026.

The move follows a 2025 trial in Spartanburg, South Carolina, where Figure AI’s Figure 02 robot handled precise, repetitive body‑shop tasks in BMW’s X3 production line. Over ten months, Figure 02 worked ten‑hour shifts, five days a week, moving more than 90,000 sheet‑metal components and supporting the production of over 30,000 vehicles, while integrating with BMW’s existing smart transport robots and robotics ecosystem.

Automakers and technology groups are racing to deploy humanoid robots as labour markets tighten, vehicle architectures grow more complex and traditional industrial automation reaches its limits. Proponents argue that mobile, human‑shaped machines, powered by increasingly capable AI systems, can slip into brownfield plants, take on ergonomically punishing or safety‑critical tasks and be reprogrammed as models and processes change, potentially reshaping factory work in the same way industrial robots did in the late 20th century.

“Digitalisation improves the competitiveness of our production – here in Europe and worldwide. The symbiosis of engineering expertise and artificial intelligence opens up entirely new possibilities in production,” said Milan Nedeljković, BMW’s production chief.

BMW describes the initiative as part of a broader “Physical AI” strategy that combines digital AI agents, a unified data platform and robots able to learn and operate autonomously on the shop floor. It has created a Center of Competence for Physical AI in Production in Munich to vet technology partners, run lab‑based evaluations with real use cases and then move promising systems into plant‑level pilots.

At Leipzig, AEON will be tested as a multifunctional platform whose human‑like form factor can carry different grippers and scanning tools and move dynamically on wheels. The emphasis, BMW says, is on complementing existing automation, taking over monotonous and physically demanding tasks, and improving working conditions rather than replacing staff.

“Our aim is to be a technology leader and to integrate new technologies into production at an early stage,” said Michael Nikolaides, who oversees BMW’s global production network and supply chain.

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Humanoids have begun to step into factories and warehouses in scenarios closer to real-world applications. Earlier humanoid robot deployments were largely confined to demonstrations, but the gap between lab and factory floor is closing. Recent high-profile deals such as the one between Toyota and Agility Robotics reflect the trend. Toyota Motor Manufacturing Canada has signed a Robots-as-a-Service agreement to deploy seven of Agility’s humanoid robots in manufacturing at its Woodstock, Ontario facility. Agility has also expanded its customer base in e-commerce, including a warehouse automation push by Mercado Libre in Latin America.

At BMW Group’s Spartanburg plant in the US, over ten months in 2025, Figure AI’s Figure 02 robot worked alongside human employees on the BMW X3 production line, completing approximately 1,250 operating hours and moving more than 90,000 components. The robot handled precise positioning of sheet metal parts for welding — a task requiring millimeter-level accuracy while being physically demanding for human workers. Within that period, it contributed to the production of 30,000 vehicles.

BMW has just announced that it is bringing robots and physical AI to some of its German plants.

These deployments also reveal how humanoids are being used currently. All involve structured, repetitive tasks in controlled environments. Material handling tasks dominate, including moving bins, positioning components, transporting totes between stations and so on. No manufacturer is deploying humanoids for assembly operations, welding cells, or machine tending — tasks that would require greater dexterity and adaptability.

Humanoid robot costs and ROI

The case for humanoid robots is gradually but increasingly supported by the math. A manufacturing worker in the US costs approximately $160,000 annually when wages, benefits, overhead, and payroll taxes are included. Current humanoid robot prices range from $13,500 for basic models like the Unitree G1 to $250,000 for more capable systems like Agility’s Digit.

At these prices, the payback period for replacing a single-shift worker ranges from two months for the least expensive models to nineteen months for premium units.

Multi-shift operations improve the economics further. A robot operating sixteen hours daily can effectively replace two workers at a combined cost of $320,000 per year. Maintenance, software updates, and integration costs add 50-100 percent to the purchase price, but the basic calculation remains attractive for high-wage environments.

In regions where labor costs are lower, or where existing automation solutions remain cost-effective, the business case weakens. The appeal of humanoid robots derives partly from their ability to work in facilities designed for human workers, avoiding the expense of redesigning production lines. But this advantage matters most in brownfield sites with space constraints or frequent reconfiguration needs.

The bill of materials for a humanoid could drop from the current $40,000 to $50,000 to $10,000 to $20,000 by 2035, the consultancy Bain & Co. projects.

Technical limitations of current humanoid robots

Current humanoids excel at mobility and basic manipulation in mapped environments but struggle with fine motor tasks requiring tactile feedback. Battery life remains limited — most industrial humanoids operate for 4-8 hours before requiring recharge. Reliability under continuous operation is improving but not yet proven over multi-year timeframes.

Safety certification for fenceless operation alongside human workers represents another hurdle. Most current deployments operate in semi-segregated zones or during off-hours when human presence is minimal. Agility Robotics has stated its next-generation Digit will be the first cooperatively safe humanoid capable of working directly alongside people at scale.

BMW notes that while motion sequences trained in the laboratory transferred to production faster than expected, early involvement of production IT infrastructure, occupational safety, process management, and shop floor logistics teams proved essential. Integration with existing manufacturing execution systems and warehouse management platforms requires substantial engineering effort.

Bain and Company’s research indicates that humanoid adoption will occur in three waves: industrial applications in automotive, mining, and construction first; then commercial uses in cleaning, healthcare, and hospitality; finally, consumer applications in domestic settings. The trajectory depends on technology maturation but also on measurable return on investment and building user risk tolerance.

From pilots to scale

The current landscape features many pilot programs, partnerships and testing agreements. It remains to be seen how many of those will transition to commercial deployments of hundreds and then thousands of humanoid robots.

Manufacturing executives require proof of reliability over extended periods — preferably years, not months. They need evidence that humanoids can match the uptime of existing automation solutions, which often exceed 95 percent. Questions about service and support networks, spare parts availability, and long-term vendor viability weigh heavily in capital equipment decisions of this magnitude. For context, a transition from pilot to meaningful scale would likely mean fleets of 50 or more units operating continuously across multiple shifts, a threshold no manufacturer has publicly reached with humanoid robots as of 2025.

The industry also faces a coordination challenge. Humanoid robots, unlike most other manufacturing technologies, lack established supply chains with tiered manufacturers building standardized modules. Each company develops its own actuators, control systems, and AI models. Some suppliers are beginning to address this by developing standardized humanoid components for use across multiple platforms. This vertical integration may accelerate innovation but creates vendor lock-in and complicates maintenance for end users operating multi-vendor fleets.

Humanoid robots are entering commercial use, but years away from scale deployment in most manufacturing environments. Even as they are being used for genuine industrial applications, deployments at scale are some years away.

Widespread adoption will require significant cost reductions, demonstrated multi-year reliability in production settings, expanded dexterity for complex manipulation tasks, and standardized safety certifications for human-robot collaboration. Progress on all fronts is happening, but unevenly.

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Humanoid Robots Are Entering the Workforce

Long a staple of science fiction, humanoid robots are edging into factory floors and warehouses, but Forrester expects the transition to be cautious rather than explosive. A new report from the research firm, ‘The State of Humanoid Robots, 2026,’ argues that the machines are shifting from spectacle to tool, with early adopters chasing productivity gains while wrestling with cost and regulatory headaches. Other analysts have proposed a staged adoption model, with each phase contingent on clearing the economic hurdles of the last.

Early Deployments Are Delivering Measurable Results

Forrester’s latest automation survey finds that 69 percent of automation decision-makers are adopting or planning to adopt humanoid robots, with deployments already underway in manufacturing, logistics, healthcare, and customer service. Early adopters report 40 percent reductions in processing errors and 20 percent declines in labor costs where humanoids standardize repetitive, high-friction work, from BMW’s use of robots for ergonomically awkward assembly to KEENON Robotics cutting restaurant labor costs through automated food preparation and cleaning.

The push comes amid a broader race to automate labor-intensive tasks as companies confront aging workforces, skills shortages, and persistent margin pressure, though the debate on humanoid viability remains unsettled. Investors and boardrooms are betting that advances in generative and physical AI, coupled with cheaper cloud-based infrastructure, will allow humanoid machines to operate safely and reliably in spaces built for people, even as regulators and risk officers fret about safety, cybersecurity, and liability.

“Humanoid robots are no longer a futuristic fantasy, but a pragmatic tool for operational transformation,” says Charlie Dai, a vice president and principal analyst at Forrester. “Leaders must approach this technology with disciplined experimentation, viewing these robots as workforce multipliers that augment human capabilities rather than wholesale replacements.”

AI Advances Are Accelerating Humanoid Capabilities

The report points to advances in AI to explain the acceleration. NVIDIA’s Isaac GR00T-Dreams, for instance, is said to cut model-development time from three months to 36 hours by using synthetic motion generation and multimodal foundation models, while physical-AI techniques narrow the sim-to-real gap to improve balance and reliability in real-world settings.

Humanoids Are Augmenting Workers, Not Replacing Them

For now, Forrester stresses that humanoids are amplifying human workers rather than displacing them. In warehouses, AgiBot’s A2-W handles 30 percent of material transport with zero errors, and in Singapore’s Sengkang Community Hospital, a robot called Dexie supports multilingual dementia care so staff can focus on more complex, higher-empathy tasks.

What Is Holding Back Large-Scale Deployment

Yet the firm is skeptical that a flood of chrome-plated colleagues is imminent. High R&D costs, deployment complexity, and underdeveloped rules on safety, cybersecurity, and liability will keep most projects at pilot scale for the next two years, it says, with prudent companies advised to take a measured, human-centric approach rather than succumb to humanoid hype. We examine the pilot to production gap in detail, including the operational and economic conditions that determine when manufacturers move from testing to full deployment.

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Agility Robotics, maker of the bipedal robot Digit, has signed a commercial robots-as-a-service agreement with Toyota Motor Manufacturing Canada (TMMC), turning a recent pilot into a full deployment at the carmaker’s plants in Ontario.

Digit units will be introduced to lines involving manufacturing, supply-chain and logistics tasks, with the aim of easing physical strain on workers and streamlining shop-floor operations, the Oregon-based robotics company said in a press release.

The deal follows a trial at TMMC’s Cambridge facility and will see humanoid robots integrated into existing workflows rather than driving costly retooling of production lines. Agility says its Digit platform and Agility Arc cloud software can be slotted into current layouts to handle monotonous, process-automated jobs that are hard to staff and retain, from repetitive material handling to backroom logistics. The humanoid robot economics behind deployments like this one, including payback periods and cost projections, are explored in our manufacturing analysis.

Humanoid robots have become the latest frontier in factory automation as manufacturers confront ageing workforces, labour shortages and pressure to raise productivity without sacrificing safety. Agility already counts Amazon, GXO and Schaeffler among its customers, and is pitching Digit as a general-purpose, “human-centric” machine that can walk where people walk and adapt to changing workflows using artificial-intelligence tools.

For global automakers such as Toyota, whose Canadian arm runs its largest production operation outside Japan, humanoids offer a way to automate ergonomically risky or low-value tasks while preserving the company’s long-cultivated “Respect for People” ethos on the line.

“Toyota Motor Manufacturing Canada has long been a leader in automotive manufacturing innovation,” said Tim Hollander, TMMC’s president. “After evaluating a number of robots, we are excited to deploy Digit to improve the team member experience and further increase operational efficiency in our manufacturing facilities.”

Beyond the initial rollout, Agility and TMMC plan to assess further use cases where robots and AI could augment automotive production, particularly in extremely repetitive and physically taxing jobs. Agility’s chief executive, Peggy Johnson, said the company’s next generation of Digit is intended to be the first “cooperatively safe” humanoid designed to work alongside people, enabling customers to scale deployments beyond fenced-off automation cells.

Agility argues that, as its robots learn new tasks and adapt on the fly, they will become a flexible layer of automation that can be reallocated as product mixes and production schedules shift.

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In this video, Venkatesh Prasad, Head of Mechanical Engineering at Ati Motors, explains why their Sherpa Autonomous Mobile Robots (AMRs) are “friends in the warehouse.” These robots are built to handle all the gruelling hard work without ever getting tired, providing a reliable solution for demanding industrial environments.

Prasad details how Sherpas are uniquely engineered to operate across both indoor and outdoor settings, successfully navigating challenges like uneven floors, debris, and water. To manage these hazards, the mechanical design must be exceptionally adaptable.

This involves the development of specialised drive systems, advanced subsystems, and robust protective enclosures to ensure the robot’s longevity and performance in the field. Discover how Ati Motors is redefining the capabilities of AMRs by prioritising mechanical adaptability and resilience for any terrain.

Catch the full interview here

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Bedrock Robotics and Machina Labs said in separate announcements they have raised fresh investments to automate some of the most labour- and capital-intensive corners of the industrial economy, underscoring investors’ appetite for software-defined hardware in old-line sectors.

San Francisco-based autonomous construction company Bedrock Robotics has raised $270 million in a Series B round to advance software-defined machines for infrastructure projects. The investment, co-led by CapitalG and Valor Atreides AI Fund, brings the company’s total capital to more than $350 million and pushes its valuation past $1.75 billion. Backers include NVentures, Tishman Speyer, MIT and C4 Ventures.

Bedrock, which emerged from stealth in July 2025 with $80 million in seed and Series A capital, retrofits excavators, bulldozers and loaders so contractors can deploy supervised and, eventually, fully autonomous fleets on complex infrastructure projects.

The new capital will help shift customers from piloting individual autonomous machines to orchestrating connected fleets across large sites. A recent deployment on a 130-acre manufacturing site validated its mass excavation capabilities.

Across rich economies, ageing workforces, infrastructure pushes and subsidies for factories building chips, batteries and data centres are colliding with chronic shortages of skilled tradespeople and rigid 20th‑century manufacturing assets. Investors are betting that autonomy and AI-native production lines can unlock capacity without a commensurate rise in headcount, recasting earthmoving and metal-forming as software problems rather than purely mechanical ones.

“The construction sector is facing an overwhelming demand that it cannot fulfill,” said Boris Sofman, Bedrock’s boss, arguing that system-level autonomy will let contractors redeploy scarce operators to supervision and strategy while fleets of machines run longer with less idle time.

Machina Labs

In Los Angeles, Machina Labs has secured $124 million in Series C financing to deploy its first “Intelligent Factory” for metal manufacturing in defence and aerospace. Led by an unnamed lead investor, the round will fund a 200,000-square-foot facility housing up to 50 RoboCraftsman cells. The company plans to produce thousands of metal assemblies annually for customers in defence, advanced mobility and automotive sectors.

The Intelligent Factory uses AI-driven robotics to replace traditional tooling, enabling rapid reconfiguration for diverse parts. Machina aims to cut lead times and support high-mix, low-volume production amid supply-chain strains.

For defence ministries, aerospace primes and carmakers fretting over supply-chain fragility, such technologies promise shorter lead times, more configurable plants and a measure of industrial resilience.

“The world’s most advanced designs are being held back by 20th-century factories,” said Edward Mehr, the company’s chief executive. “This round allows us to scale manufacturing infrastructure that moves at the speed of software. We’re not just making parts, we’re reprogramming the factory itself to serve defense, aerospace, and automotive customers who can’t afford to wait.”

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AI is rapidly boosting robots’ capabilities, efficiency and adaptability on factory floors and in service settings, the International Federation of Robotics (IFR) argues in a new position paper.

IFR highlights how deep-learning computer vision now allows robots to see and interpret visual data for tasks such as object recognition, barcode reading, sorting and real-time production-line monitoring, while supervised learning powers defect detection, predictive maintenance, quality inspection and process optimisation. Natural-language processing lets collaborative and service robots understand and respond to spoken or written commands, and mobile robots blend data from cameras and LiDAR to perform simultaneous localisation and mapping for warehouse and shop-floor navigation.

AI for robotics promises to bring the productivity improvements seen from generative AI to the physical world, even as governments and boardrooms alike try to balance the gains from AI-enabled automation with unease over jobs, safety and the energy appetite of large models.

Robot installations are already taking over physically demanding and repetitive tasks, freeing workers from harsh conditions, even as AI adds demand for data scientists, machine-learning specialists and ethicists, and forces companies and employees to keep re-skilling to remain competitive. At the same time, the IFR notes, AI-enhanced efficiency and output may spur economic growth while intensifying pressure on businesses and workers to adapt to faster cycles of technological change.

“In future, AI in robotics will further influence how teams work, how decisions are made, and how performance is monitored,” the IFR notes, adding that improved workflows “may also raise concerns about employee surveillance or reduced autonomy.”

The federation notes that reinforcement learning, though still emerging in industrial settings, is gaining traction in motion and path planning, grasping and adaptive control, where robots learn by trial and error in dynamic environments. It singles out generative AI as the next step, predicting that models will generate code for entire robotic functions from natural-language instructions. The quality of AI-generated code should be rigorously tested IFR adds.

Developers and users, it cautions, must contend with data poisoning, biased or compromised training sets and the unpredictability of autonomous systems, since malfunctions in the physical world can have more severe consequences and human-robot collaboration must remain physically safe at all times. A growing focus on sustainability will push robotics towards efficiency and longer robot lifespans, the IFR says, even as the sector confronts the ecological cost and carbon footprint of training large AI models.

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Humanoid robots, long a staple of science fiction, are edging closer to commercial reality as companies begin structured trials in factories, warehouses and commercial buildings, according to new analysis from Bain & Company. The consultancy argues that the timing and scale of adoption will depend less on gee-whizz demonstrations and more on prosaic factors such as power systems, sensor density and measurable returns on investment.

Bain says the technology stack for humanoids is progressing unevenly: intelligence and perception are advancing quickly but are still short of enabling fully autonomous operation, while power systems and dense, reliable sensing remain stubborn bottlenecks. Large-scale penetration, it contends, will only come once those constraints ease, safety and privacy concerns are addressed, and early adopters can prove positive returns that justify broader rollout.

Behind the cautious timeline lies a bigger wager about the future of work and automation. Over the next decade, Bain expects humanoid robots to move from experimental deployments to large-scale use across three successive waves —industrial, commercial and then consumer — potentially reshaping labour-intensive sectors from automotive and mining to healthcare, hospitality and domestic services.

Their spread will test how quickly firms, regulators and workers are willing to trust machines that look and move like people, yet are designed to slot into tightly optimised workflows rather than to replace humans wholesale.

“Humanoid robots are on the way, but their success depends as much on economics and trust as on technology,” the report concludes.

In Bain’s schema, early industrial use cases span automotive production, mining and construction, where humanoids can step into dangerous, variable or ergonomically awkward tasks that traditional fixed or wheeled robots struggle to handle. Our analysis of industrial humanoid deployments at BMW and Toyota examines how this first wave is playing out on factory floors today.Commercial settings such as professional cleaning, healthcare and hospitality — tour-guide roles among them — are expected to follow as software improves and operators gain confidence.

Consumer adoption, in areas such as domestic cleaning and education, is likely to lag until costs fall and reliability is proven in harsher industrial environments. Bain foresees a hybrid future in which non-humanoid machines handle highly repetitive workflows, while humanoid robots act as “flexible generalists” alongside humans, who focus on strategic oversight such as planning, workflow orchestration and risk management.

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The world’s biggest technology companies have kicked off 2026 with announcements of ambitious plans to further develop AI for the physical world, and especially in the field of brains for robots and humanoids. Two more announcements that are noteworthy come from Microsoft, and AsiaInfo and ABB Robotics.

Microsoft has introduced Rho-alpha, a new robotics model that aims to bring the generative-AI revolution from text and images into the physical world. Built on the company’s Phi series of vision-language models, it is designed to translate natural-language instructions into control signals for dual-arm robots performing complex manipulation tasks.

Rho-alpha is described as a “vision-language-action-plus” system, adding tactile sensing to the usual camera and language inputs and with plans to incorporate force and other modalities. Microsoft is trialling the model on dual-arm and humanoid platforms and is inviting organisations to apply for an early-access programme, ahead of broader availability through its Foundry offering.

The launch underscores how big technology firms are racing to turn large AI models into “physical AI” platforms that can perceive, reason and act among humans, not just generate text or images. As labour markets tighten and factories, warehouses and hospitals hunt for productivity gains, adaptable robots that can learn from feedback and cope with messy real-world environments have become a strategic prize.

“Through these advancements, we aim to make physical systems more easily adaptable, viewing adaptability as a hallmark of intelligence,” Microsoft’s research team wrote. “We believe robots that can adapt more easily to dynamic situations and to human preferences will be more useful… and more trusted by the people who deploy and operate them.”

AsiaInfo, ABB Robotics team up

Meanwhile, AsiaInfo and ABB Robotics have launched a new “Embodied Intelligence Laboratory” in China, betting that “physical AI” will be the next frontier in industrial automation. The lab was formally inaugurated on Jan. 25 and is positioned as the practical outgrowth of the two companies’ existing strategic partnership.

Backed by Alibaba Cloud and Nvidia, the laboratory will blend AsiaInfo’s strengths in AI applications, 5G-A communications and cyber security with ABB’s know‑how in robot control. The partners plan to focus on industrial simulation and physical AI built on Alibaba Cloud’s vision‑language‑action large model and Nvidia’s simulation platform, targeting deployment in complex factory environments.

The tie‑up underlines how Chinese software, Western industrial automation and US chip design companies are converging around embodied intelligence as manufacturing digitises at speed. As factories push to cut labour costs and cope with ageing workforces, vendors from ABB to Alibaba and Nvidia are racing to turn generative and embodied AI into robots that can perceive, decide and act reliably on the shop floor.

“The Laboratory may serve as a key platform for their continuous enhancement in technological synergies and co-creation of application scenarios, with a view to advancing the implementation of the application scenarios of physical AI in manufacturing sector and setting an industry intelligence benchmark with global influence,” AsiaInfo’s board said.

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Humanoid, a London-based robotics startup, has run its first live factory trial with Siemens, deploying a wheeled humanoid robot to handle box-moving chores inside one of the German group’s electronics plants. Over a two-week proof of concept in Erlangen, the HMND 01 Alpha robot shuttled plastic totes from storage stacks to a conveyor, working alongside humans in regular production.

The exercise was designed to see whether a general-purpose humanoid can take on the unglamorous but necessary work of industrial logistics, rather than the carefully fenced-off duties of traditional industrial arms. In Siemens’ tote-to-conveyor “de-stacking” task, the robot repeatedly picked containers from a pile, trundled them across the shopfloor and deposited them at a handover point for human operators until the stack was empty.

Humanoid robots have become the latest obsession in factory automation, as manufacturers hunt for flexible machines that can slip into existing layouts and workflows rather than demand greenfield redesigns. From carmakers to e‑commerce giants, companies are testing legged and wheeled bipeds in warehouses and plants, hoping they can eventually tackle varied, dull and physically taxing jobs amid labour shortages and ageing workforces. Siemens’ trial suggests that, at least for tightly defined tasks, the technology is moving from lab demo to production line experiment.

“Our focus is on creating robots that deliver measurable value in real-world settings,” Artem Sokolov, the UK company’s founder and chief executive, said in a press release. “This joint PoC with Siemens showed clear potential for practical deployment of humanoid robots. We see them move steadily toward the real world, and partnerships like this one help accelerate that transition.”

The PoC unfolded in two phases, starting with in‑house development using a physical twin of the workcell to refine performance before shifting to the live factory. In Erlangen, HMND 01 ran autonomously for stretches of more than 30 minutes, handled two tote sizes and met targets including 60 tote moves per hour, uptime above eight hours and pick‑and‑place success rates exceeding 90 percent.

Both companies bill the trial as a beginning rather than a climax. They are weighing additional logistics use cases and a broader rollout across Siemens facilities, turning the Erlangen factory — described by the group as its “customer zero” — into a test bed for what may become a new class of industrial co‑workers.

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