Global humanoid robot production exceeded 20,000 units in 2025, a tenfold increase from fewer than 2,000 units in 2024. However, the vast majority were used for research, data collection, and entertainment. Only around 10% of units produced were deployed in real-world applications. While this represents a substantial increase from dozens of units in 2024, growth was driven more by an expanding customer base and increasingly diversified pilots than by scaled commercial deployments.

By the end of 2025, most real-world application projects remained small-scale proof-of-concept (POC) deployments, predominantly driven by government subsidies, strategic investments, and supply chain partnerships. The market still lacks large-scale, long-term deployments based purely on commercial rationale. Although cost reduction and efficiency improvements through automation may have been the original intent of many pilot projects, most had not progressed beyond short-term demonstrations and small-scale controlled operations.

Autonomous operation, return on investment realisation (requiring sufficient efficiency and task success rates), and multi-task-level generalisation capabilities still appear to form the “impossible triangle” that humanoid robots struggle to break through.

Chinese vendors lead humanoid robots production

China was the engine of both supply and the early adoption of humanoid robots during 2025. In terms of overall unit production, Chinese vendors’ share exceeds 90%, with the remainder largely from US manufacturers. In terms of adoptions, about 75% of humanoid robots were delivered in China. The disparity between domestic production and demand in China during 2025 is primarily due to significant overseas sales achieved by several Chinese humanoid robot manufacturers, with demand driven mainly by academic research and entertainment use.

The vendor landscape is also highly concentrated in China. The top 5 producers in 2025 were all Chinese manufacturers, collectively accounting for approximately 70% of global humanoid robot production. Unitree and Agibot each produced and shipped over 5,000 units, together surpassing 11,000 units and representing more than 50% of the global market.

However, today’s market concentration and leadership are driven by early research demand (including data collection for physical AI training), attempts to gain media attention, and curiosity-driven trials, rather than by proven commercial deployments. The robust material support provided by the Chinese government for humanoid robotics is the primary reason behind the aggressive expansion of Chinese vendors and the domestic market in 2025. We believe the competitive landscape is far from settled, given that both the market and the underlying technology remain in a very nascent, immature phase. It could still experience substantial dynamical change, with more established cross-industry players joining the field, such as leading automotive and consumer electronics vendors.

Commercial inflection point

Looking ahead, we believe the market is likely to continue growing, with annual volumes reaching thousands of units. The share of units deployed for real-world applications will increase gradually over time. However, in the short run, growth won’t be entirely driven by rational commercial considerations. It is primarily driven by numerous small-scale pilots at present, rather than large-scale commercial projects. Customers will be concentrated among well-capitalised companies and enterprises with capital and supply chain ties to humanoid robot companies, and government involvement will play a key role.

We believe that near-term deployments will remain predominantly semi-autonomous, with certain specific tasks still requiring rule-based control or human teleoperation. The latter is expected to achieve the first actual commercial deployment of humanoid robots in hazardous work scenarios and regions with significant regional labour cost disparities. In contrast, highly autonomous, AI-driven humanoid robots will initially be adopted in scenarios with greater tolerance for task speed and error rates.

Our field observations indicate deploying humanoid robots at scale for tangible workforce value remains constrained by critical usability gaps (including task reliability and efficiency insufficient to achieve ROI, and limited multi-tasking capabilities). Technical bottlenecks span immature embodied AI, severe physical data scarcity, and inadequate hardware endurance. Meanwhile, the absence of established safety standards and regulatory frameworks constitutes a key barrier to expanding humanoid robots into human-machine interactive settings.

Consequently, we expect the market will struggle to achieve a large-scale commercial inflection point across multiple domains within the next five years. A commercial inflection point is forecast post-2032, contingent on breakthroughs in autonomous and reliable task execution, acceptable ROI, and clearer regulatory environments. By 2035, global shipments for real-world applications are projected to exceed 700,000 units, with market revenue reaching approximately $15 billion.

Marco Wang is market analyst at Interact Analysis

interactanalysis.com
 

RECENT RESEARCH from Make UK found that approximately 36 %of manufacturing vacancies are difficult to fill due to candidates lacking the necessary skills, qualifications or experience. Collaborative robots are becoming an increasingly important tool for manufacturers in addressing this gap. By taking on repetitive, physically intensive and potentially hazardous tasks, cobots enable businesses to sustain production levels despite ongoing workforce challenges.

In addition to helping bridge labour gaps, cobots deliver consistent performance that supports improved productivity and product quality. What’s more, they allow human employees to concentrate on more complex value-adding activities that require problem-solving, judgement and expertise. Reduced physical strain can also contribute to improved workplace wellbeing, while intuitive programming lowers barriers to adoption by reducing training requirements and implementation times. For many manufacturers, these advantages help accelerate return on investment.

Navigating new regulations

Across manufacturing operations, cobots are increasingly utilised to streamline assembly processes and improve operational workflows. A 2024 study published in Frontiers in Robotics & AI showed that collaborative robots can boost productivity by automating repetitive activities while leaving more complex, cognitively demanding work to human operators. 

Adoption continues to grow, with the International Federation of Robotics estimating that 10.5% of all industrial robots installed in 2023 were cobots. Regulatory requirements are now evolving in parallel with technological progress. The EU Machinery Regulation 2023/1230, adopted in 2023 and scheduled to apply fully from January 2027, replaces the previous Machinery Directive. It introduces more robust expectations around safety, responsibility and cybersecurity for advanced machinery and collaborative systems. These changes encourage greater transparency and more rigorous conformity procedures, signalling that compliance and safe integration will become increasingly important considerations for automation projects.

For engineers, the implications extend directly to how collaborative robotic systems are designed, deployed and validated. Within logistics environments, cobots are increasingly used for palletising, order picking and intralogistics material handling. Technologies such as machine vision, force sensing and safety-rated scanners allow close human–robot interaction without the need for conventional safety cages. 

In automotive production, collaborative robots support tasks including precision assembly, screwdriving, machine tending and quality inspection, helping improve cycle-time consistency while reducing operator fatigue and ergonomic strain.

Successful integration depends on following established best practices. This includes carrying out task-specific risk assessments in accordance with ISO 12100 and ISO/TS 15066, implementing safety-rated monitored stop functions and speed-and-separation monitoring, and maintaining strong cybersecurity controls for connected robotic systems. Engineers should also consider modular architectures, standardised industrial communication protocols such as PROFINET and EtherCAT, and digital simulation platforms that enable layouts and workflows to be validated before installation.

Challenges in legacy manufacturing environments

Many manufacturing facilities continue to rely on ageing infrastructure and legacy equipment that was never designed with robotics in mind. Restricted floor plans, outdated systems and inconsistent connectivity can complicate introducing automation. If new technologies are not compatible with older assets, implementation can quickly become complex, time-consuming and expensive.

The success of collaborative automation depends on more than the robot itself. Programming, setup and configuration, software integration and ongoing maintenance may exceed the expertise of a team. Initial investment costs, from the initial equipment itself to any necessary facility upgrades and workforce training can also appear significant, particularly in today’s often turbulent economic landscape.

Traditional industrial robots, typically engineered for fixed and repetitive applications, often require extensive reprogramming in response to any changes in production requirements. The resulting downtime can rapidly undermine their benefit and limit long-term value. To overcome these limitations, manufacturers are increasingly turning to flexible automation strategies that emphasise rapid deployment, simple reconfigurability and scalable investment models, particularly within small and medium-sized operations.

For example, CubeBOX EcoLEAN-V1 and V2 are designed to be repositioned and reconfigured as production needs evolve. This adaptability enables manufacturers to expand automation gradually across their operation without committing to fixed production layouts. The approach reflects a wider industry move towards agile automation — solutions capable of evolving alongside changing operational demands. EcoLEAN is available in a range of configurations to accommodate different payloads, component sizes and space limitations, making it suitable for a variety of manufacturing settings.

The right investment

According to a 2025 Deloitte survey of 600 manufacturing executives, 80 per cent expect to allocate at least 20 per cent of their improvement budgets to smart manufacturing initiatives this year, with investment focused on foundational technologies and digital capabilities.

This level of commitment signals a clear industry shift: manufacturers are no longer viewing digitalisation and automation as optional upgrades, but as essential capabilities that will define competitiveness in the years ahead. This level of commitment is indicative of a broader shift in thinking across the sector. Manufacturers increasingly regard automation and digital transformation not as optional enhancements, but as critical capabilities that will shape competitiveness in years to come.

While collaborative technologies remain a major driver of this transition, factors such as flexibility, mobility and affordability will ultimately determine how manufacturing operates, how widely automation is adopted and who can benefit from its advantages.

Hakan Aydoğdu is CEO of Tezmaksan Robot Technologies

tezmaksanrobotics.com

NVIDIA AND LG Group are building an AI factory to accelerate LG Group’s next wave of AI-driven businesses, spanning robotics, autonomous driving, data center technologies and GPU cloud services. The AI factory will provide LG Group with accelerated computing infrastructure to train, simulate, validate and deploy AI-based applications across its key businesses. 

The collaboration brings together NVIDIA’s full-stack, end-to-end AI factory platform with LG Group’s global leadership in consumer electronics, robotics, mobility components, smart spaces and data center technologies. Together, the companies are connecting AI model development, physical AI data generation, robot simulation and training, edge deployment and factory-scale digital twins into a unified workflow for building physical AI systems. 

The combination of LG’s production technology data and know-how from global manufacturing sites with NVIDIA’s AI infrastructure and digital twin technologies will help enhance AI-driven manufacturing AI competitiveness. The two companies will collaborate to build an autonomous manufacturing ecosystem in which the entire process – from raw material procurement to production, logistics and customer delivery – is connected in real time through data and AI, and establish it as a new global smart factory standard.

LG Electronics is developing home-based robots like CLoiD to help with a wide range of indoor household tasks, enhancing everyday convenience and improving quality of life. By integrating the NVIDIA Isaac Sim and NVIDIA Isaac Lab open robotics frameworks into their development workflows, LG can simulate, train and validate these home cobots in physically accurate virtual environments before deployment. 

The company is exploring using the NVIDIA Isaac GR00T open, reasoning vision action language model for both its home robots and modular robotics platforms. The GR00T model will provide LG robots humanlike reasoning and the ability to execute complex tasks. NVIDIA and LG Electronics also plan to jointly develop reference robots, positioning LG’s robots as part of the NVIDIA Isaac GR00T ecosystem.

To help overcome the training data challenge for robotics, LG Electronics is developing a physical AI data factory poised to help Korean and global companies accelerate physical AI projects. By turning compute into data, LG will be providing high-quality training data for robotics and industrial AI projects, using NVIDIA Cosmos world foundation models for synthetic data generation and augmentation.

LG Innotek, harnessing its world-class optical expertise, plans to provide state-of-the-art robotics components, including sensing solutions, specifically optimized for NVIDIA’s development environments and GPU architecture.

LG CNS is building an ecosystem that enables anyone to easily adopt AI robots in manufacturing and logistics sites. By integrating NVIDIA’s robotics technologies including Isaac open robotics frameworks, NVIDIA Cosmos open world models and Isaac GR00T robotic foundation models into its PhysicalWorks industrial robot platform, the company is accelerating the AI transformation of logistics and manufacturing floors.

Building the AI Factory Infrastructure

The two companies will also expand cooperation in the field of next-generation AI factories, which will support the AI era.

Beyond its certification cooperation with NVIDIA on cooling solutions for AI factory thermal management — including cooling distribution units (CDUs) and cold plates — LG Electronics is further elevating its AI factory capabilities through technical collaboration on prefabricated modular design technologies. This initiative aligns with the NVIDIA DSX AI factory platform, enabling the rapid deployment of scalable, high-performance supercomputing infrastructure.

These technologies include CDUs, cold plates and prefab modular design capabilities to help address the power, thermal and deployment requirements of next-generation liquid-cooled AI factories.

In collaboration with LG Electronics and LG Energy Solution, LG Uplus — a telecommunications provider under LG Corp. — plans to build scalable, power-efficient AI factories based on NVIDIA DSX. The effort is expected to combine NVIDIA accelerated computing and AI factory reference architectures with LG’s infrastructure, energy and telecommunications capabilities to support future AI cloud and GPU service opportunities. 

LG CNS plans to build scalable, power-efficient, high-performance AI factories powered by NVIDIA GPUs based on NVIDIA DSX.

LG Uplus plans to build a large-scale AI data center capable of accommodating the latest NVIDIA GPUs.

LG Energy Solution plans to collaborate with NVIDIA on emerging 800 volt-direct-current data center energy solutions, in alignment with NVIDIA’s BESS Self-Qualification guidelines, to keep pace with next-generation GPUs. 

Accelerating mobility AI

In mobility, LG Electronics works with NVIDIA to align its advanced driver-assistance systems (ADAS) and in-vehicle AI systems with the NVIDIA DRIVE platform. 

The collaboration will focus on aligning sensor, compute and software architectures with the NVIDIA DRIVE Hyperion architecture, supporting LG Electronics’ roadmap for autonomous driving, ADAS and software-defined vehicles. 

LG Electronics also plans to use NVIDIA DRIVE AGX accelerated compute for its future mobility applications, including AI-powered cockpits and edge AI processing. Through this work, LG Electronics aims to strengthen its automotive electronics portfolio and accelerate the development of AI-driven mobility solutions for global manufacturers.

LG Innotek is rapidly cementing its leadership in the autonomous driving market, using its core portfolio of world-class sensing, connectivity and lighting solutions. LG Innotek plans to collaborate with NVIDIA on next-generation components engineered specifically for NVIDIA architecture. 

Advancing sovereign AI with EXAONE

NVIDIA and LG AI Research are collaborating to advance EXAONE, one of Korea’s leading sovereign AI models and an open model family available to developers, enterprises and researchers. 

LG AI Research used NVIDIA Blackwell GPUs, NVIDIA NeMo framework and NVIDIA Nemotron open datasets to support EXAONE model development, as well as NVIDIA TensorRT-LLM software to build high-performance inference engines for optimized deployment.

LG Group is exploring broader adoption of EXAONE and agentic AI technologies across its businesses through platforms such as ChatEXAONE — LG Group’s EXAONE-based enterprise chatbot service. NVIDIA will help power LG AI Research’s sovereign AI models, so LG Group can accelerate enterprise AI transformation, software-defined operations and productivity across its business portfolio. 

www.nvidia.com/en-gb/

Cold spray is an advanced manufacturing process that builds or repairs metal components by accelerating fine metal powders at very high speeds onto a surface. Unlike traditional methods, the material does not melt during deposition. This avoids heat damage and preserves the original properties of the material.

Experts from the Faculty of Engineering with support from an international Japanese technical team at PlasmaGiken have designed and installed the High-Pressure Cold Spray system that allows higher particle impact speeds, stronger bonding between deposited material and substrate and the processing of harder and more demanding materials.

This technology enables the repair and remanufacture of high-value components in aerospace, fusion energy, nuclear and defence sectors, lowering costs and improving sustainability through reduced material waste compared to conventional manufacturing.

Housed in the Centre of Excellence in Coating and Surface Engineering (CE-CSE) the facility will be a national hub for collaboration with industry and research partners, supporting the UK’s ambitions in advanced manufacturing and clean energy technologies.

Tanvir Hussain, Professor of Coatings and Surface Engineering at the University of Nottingham has led the project, he said: “After three years of proposal development and industrial collaboration, we are proud to commission the UK’s highest-pressure cold-spray additive manufacturing facility. This is not simply a new piece of equipment added to the UK cold spray landscape, but it represents a national capability that will support advanced manufacturing research and industrial innovation for years to come for the UK for all engineers and technologists.

“The Cold Spray facility combined with the expertise of academics in Nottingham, alongside industry partners will allow this National Facility to drive innovation at pace and allow exploration and testing of new techniques and applications.”

The project has been funded through Research and Innovation and the Faculty of Engineering at the University of Nottingham, PlasmaGiken Co., Rolls-Royce, BAE Systems and Engineering and Physical Sciences Research Council (EPSRC).

Engineers at the University of Nottingham are already leading innovation in spray technology and  recently developed a new high-performance tungsten-copper metallic coating in one step using plasma spray, for future high heat flux (HHF) plasma facing components (PFC), specifically in the divertor target plate.

www.nottingham.ac.uk

AI is already changing how economies work, public services operate and countries protect their interests. As more of the economy and public services comes to rely on AI, it matters more than ever that Britain has the ability to develop key technologies securely here at home. Ready access to compute is critical to these ambitions: giving AI innovators the digital horsepower they need, to get to work in Britain.

The new AI Hardware Plan sets out how the government will back British companies developing the chips and semiconductor technologies behind AI, while also investing in the scientists, engineers and technicians needed to turn new ideas into products and good jobs in the UK.

It comes just over a month after she announced in her speech at RUSI that the government would bring forward plans to boost Britain’s sovereign AI capabilities.

The global AI chips market is expected to reach one trillion dollars in the early 2030s. If Britain could secure just 5% of this market it would bring fifty billion dollars in revenue to the UK with tens of thousands of highly paid jobs in tech. 

British companies – from Arm, whose chip designs are used in everything from smartphones to AI data centres, to startups like Fractile and Olix, which have raised more than £320 ($440) million between them – are already leading the next generation of AI hardware. This plan backs them, and the startups coming up behind them, to become the "British AI titans of the future".  

As the market shifts from general-purpose chips to bespoke hardware, that plays directly to the UK’s strengths, creating an opportunity for British firms to lead in the AI infrastructure of the future.  

Technology Secretary Liz Kendall said:  "AI is the defining currency of economic and hard power in today’s world and the countries that control the hardware behind it will hold the keys to the future.  

"The UK is already a global leader in chip design, and I believe this is a race Britain can win. To do that, we must back more British AI – and that means investing in the chips, computing power and skilled people behind it.   

"That is exactly what this plan does, backing the British firms developing the next generation of AI hardware, so we get more jobs, more growth, and more control over the technologies our future depends on."

The AI Hardware Plan includes:  

• New £750 million for a national AI supercomputer: One of the most advanced in the world when deployed in 2030, it will bring together proven and next-generation processors and cutting-edge chips to run complex tasks more efficiently than traditional supercomputers. This is known as a heterogeneous mixed chip system. We want to see British-designed chips form a crucial part of the system, which will join Isambard-AI and Zenith (alongside DAWN) as part of the UK’s AI Research Resource, giving researchers, start-ups and public services the computing power they need to develop and run AI securely in Britain.
• Supporting UK start-ups by creating demand for powerful new chips in Britain: Of the £750 million, £400 million will go towards equipping the UK’s AI supercomputer with next-generation chips - a significant increase on previous plans. £150 million of this will be used to buy next-generation inference chips - which power the day-to-day use of AI tools - this summer, creating an immediate opportunity for British firms, who are well placed to compete. The government is acting as an early customer to help bring new technologies to market. A further £250 million will support the purchase of more specialised chips as the most promising technologies mature - helping the strongest designs reach market and compete at scale.
• Backing British companies to develop new technology: £120 million will fund a new AI Hardware Innovation Programme. Developing new chips can take years and cost millions before companies know if they will work, this programme gives British companies the funding to design, develop and test innovative novel chips, so the best ideas can move forward. It is how the UK makes sure the next generation of world-leading chip companies are grown here in Britain. 
• Helping firms prove their chip technology: At least £20 million of the programme will expand the Scaling Inference Lab, delivered by ARIA and CommonAI, to help companies prove their technology, attract investment and secure partnerships with global tech firms. The Lab is already delivering results. British AI company Oriole Networks, working with AMD through the Lab, will deploy the world’s first large-scale AI system that uses light rather than electrical signals to move data between chips, boosting the performance of UK data centres.  
• Boosting skills needed by the AI hardware sector: £45 million in new support for skills – backing doctoral training and undergraduate bursaries to train more engineers, chip designers and technicians, and open up clear pathways into the sector from university and on-the-job training to build the pipeline of British talent the sector needs. This means that government is now making £80 million available to support the industry’s skills needs.  

Building on the UK’s strength in cutting-edge tech, a new fund led by Silicon Valley investors Playground Global – whose partners include Pat Gelsinger, former CEO of Intel – and backed by up to £150m from the British Business Bank, will invest in UK-based AI hardware companies, giving British innovators the long-term backing they need to grow and stay in the UK, subject to completion of due diligence and legal negotiations. 

It is the single largest fund investment the British Business bank has ever made, a signal of the scale of the government’s commitment to backing British AI hardware. The fund, developed by DSIT, and announced by the Chancellor at London Tech Week, will help them crowd in more private investment, and develop the technologies our future depends on, ensuring Britain can compete with the biggest players on the world stage. Playground Global will also open its first office outside the US in the UK, underlining Britain’s position as a leading place to develop the next generation of AI hardware.   

www.gov.uk

Image credit: Dorset Council

Ragwort is a poisonous weed that threatens livestock health, damages grazing land and creates major challenges for farmers and landowners. Usually it is pulled by hand, which is labour intensive, costly and can pose risks to people and the environment. Ragwort also plays an important role in supporting wildlife, including pollinators such as bees and butterflies. Control is therefore targeted, with plants removed in areas where there is a risk to grazing animals.

Raggy has been developed by South West firm Robotriks in partnership with Dorset Council and long-time collaborators Telint and Neutral Networks using funding from Qualcomm Incorporated, through its Qualcomm for Good Initiative, which aims to enrich lives through programmes that strengthen economic and social development.

Ben Timmons, senior director, business development of Qualcomm Technologies International. said: “Through Qualcomm for Good, we are proud to support Dorset Council and Robotriks in harnessing edge AI capabilities to modernise agriculture and solve real-world challenges for farmers and the environment. Raggy is a powerful demonstration of what’s possible with intelligent connected technologies.”

Jake Shaw-Sutton, director of Robotriks said: “Our Robotic Traction Unit (RTU) is fully electric and built for real-life farm conditions. It is a modular platform, designed as a flexible farm multi-tool which can perform a range of tasks across agriculture and, potentially, other sectors.

“Raggy uses advanced machine vision and connected technology, powered by the Qualcomm Dragonwing platform, to identify and remove ragwort mechanically at the root. This approach reduces the need for harmful chemicals, supports healthier soils and protects animals and habitat.”

Dave Happy, CEO of Telint said: “This is yet another practical example of Dorset embracing innovative tech solutions to improve the quality of life for livestock and humans alike.

“Dorset’s unique advantages, in particular in relation to access to spectrum, make it the ideal place to test and develop this kind if innovative solution.”

Cllr Nick Ireland, Leader of Dorset Council, said: “The team of Rangers, who do a great job managing and maintaining Dorset’s fantastic Country Parks, nature reserves and open spaces, spend many hours each year removing ragwort by hand. We are delighted this autonomous and environmentally sensitive solution is being tested, evaluated and developed on our land here in Dorset.”

Between field trials, Raggy will be maintained and stored at BattleLab, the heart of an innovation ecosystem that sets challenges to a wide range of large and small company developers and leading academic researchers working on dual-use technologies including uncrewed systems and digital device security.

BattleLab and Dorset Innovation Park are key elements of a South West “Global Autonomy Cluster” recently awarded up to £20 million of Government funding to enhance the region’s reputation as one of the world's best places to develop, test and deploy autonomous technologies.

The park, Dorset’s only Enterprise Zone, has an ambitious plan to create between 300 to 500 new high-value jobs on site by 2031, with the future skilled workforce coming from a research and education centre being co-created with leading university and college partners.

www.robotriks.co.uk

Building on Sanyo Denki’s reputation for precision engineering, the new DB31 Series Stepper Motors are fully integrated motor-and-driver assemblies that deliver exceptional performance in a compact 56mm square form factor. 

The latest addition to Sanyo Denki’s Sanmotion family of motion control solutions, the new DB31 Series systems feature a highperformance stepper motor and driver that ensures precise, repeatable movement within the rated torque range, regardless of load! The motors are vibrationtested between 10–2000Hz across all axes and feature protective functions such as maincircuit voltage monitoring, fuse protection, and CPU fault detection.

The driver integrated with the stepper motor supports a range of standard operation commands including, positioning, continuous rotation and homing. Users can quickly configure key parameters such as position, speed, acceleration, and command selection, enabling fast commissioning and easy optimisation. 

To reduce mechanical shock and improve motion smoothness, DB31 Series stepper motors offer two types of Scurve acceleration/deceleration profiles: A gradual peak acceleration between 25–75% of the cycle that delivers up to 1.25× the acceleration of linear ramps, or an acceleration peak at the 50% midpoint that delivers up to 1.9× the acceleration of conventional linear profiles. The choice of two advanced acceleration modes helps to minimise vibration and stresses during speed transitions.

According to Robert Davies, marketing manager with EAO, integrating the stepper motor with the driver in a single unit both reduces system complexity and enhances reliability: "As DB31 Series Stepper Motors allow direct connection of limit sensors and homing signals enabling control of power and communication to be achieved with as few as five connections, reducing internal wiring and simplifying equipment design."

www.motorsandfans.co.uk/news

THE GLOBAL industrial robot market started to recover in 2025, growing 5.1% in shipments and 0.8% in revenue, driven primarily by Asian markets. Over the next five years, the market is projected to grow steadily as robots are adopted across more manufacturing processes and emerging industries, and as AI-related applications generate new sources of demand. In this insight, we will discuss how the rapid development of AI is impacting the outlook for industrial and collaborative robots, and how robot manufacturers are positioning themselves to capture new opportunities.

AI is driving robot demand in two directions

We see AI influencing robot demand along two distinct axes. First, the growth of AI supply chain industries is generating direct demand for robots in manufacturing. Second, AI-powered software is enhancing robot capabilities and ease of use, enabling robots to perform tasks they previously couldn’t, thereby creating demand in markets that were difficult to penetrate.

The growth of AI-adjacent manufacturing is opening up sales opportunities across several segments:

- Semiconductor manufacturing: AI-driven chip demand is accelerating automation in semiconductor production. We predict robot sales to the semiconductor industry to grow at an average annual growth rate of 7.8% through 2030, outpacing most other sectors. Light-payload articulated and SCARA robots are the primary beneficiaries, while mobile collaborative robots (mobile cobots) used for wafer handling are also rapidly gaining traction. Demand for these emerging mobile cobots is concentrated in Asia, particularly Taiwan, mainland China, and South Korea, and key suppliers in this segment include Universal Robots, Fanuc, Techman, and Jaka.

- Data centre equipment manufacturing: The manufacture of AI data center equipment, from server cabinets to printed circuit boards, is driving robot demand across the supply chain. In addition to traditional industrial robots, collaborative robots are increasingly used for inspection and component handling, valued for their flexibility and ability to work alongside people.

- Humanoid robots as a cobot customer segment: Cobot vendors are supplying robot arms to humanoid robot manufacturers, particularly in China, where some emerging companies prefer to focus on software development and source hardware off the shelf. Cobot vendors serving this market include Rokae, Jaka, and Fairino.

AI enhancing robot capabilities

In the meantime, AI is making robots more capable and easier to use – an important development, as difficulties relating to programming and integration complexity are among the barriers to robot adoption most often cited. In our recent Voice of Market research, integration complexity emerged as the top obstacle to automating material transport in factories, surpassing even upfront cost concerns.

Specifically, we notice four key areas where AI is applied in robotics: (1) AI-powered machine vision for robot guidance (2) AI-enabled robot instruction and programming (3) AI-driven path optimization and multi-robot coordination, and (4) emerging applications in remote robot monitoring and maintenance.

AI allows robots to understand and respond to human language and supports low-code or no-code programming, making robots easier to use. It also empowers robots to handle complex tasks in flexible environments. For example, Fanuc demonstrated AI-powered dual control of two cobot arms sorting cables, which is a task traditionally difficult for robots, and showcased AI-powered robots tracking moving parts for precision tasks such as screw tightening, highlighting the potential of AI to improve robot adaptability.

That said, AI-driven robotics is still in its early stages and faces challenges, such as cybersecurity risks in remote monitoring, limitations of low-code programming for complex tasks, and high R&D costs for AI-enhanced software. Broad commercialization will take time and depend on further innovation, as well as demonstrated value across a range of industrial applications.

How robot vendors are responding

Industry leaders are actively capitalizing on the opportunities created by advances in AI. On the one hand, robot manufacturers are launching new solutions tailored to the electronics and semiconductor industries. For example, ABB introduced its Lite+ small robot series in 2025, with electronics identified as one of its key target sectors, while STEP recently launched dedicated wafer handling robots.

On the other hand, robot vendors are increasingly integrating AI capabilities into their products to improve performance, flexibility, and ease of use, helping them remain competitive. For instance, both Universal Robots and Fanuc have announced collaborations with NVIDIA to develop AI-powered robot programming and simulation tools.

These developments reflect manufacturers’ efforts to address key barriers to robot adoption, particularly the challenges associated with robot integration and programming.

Final thoughts

AI is proving to be both a demand driver and a capability multiplier for the robotics industry. The expansion of semiconductor and data center manufacturing provides a robust new customer base, while AI-powered software is steadily lowering the integration barriers that have historically limited robot deployment beyond highly-structured environments. To capture these opportunities, robot manufacturers are actively investing in tailored solutions for AI-related industries, while embedding AI into their existing products. Through our ongoing discussions with industry participants, we will continue to watch the commercial adoption of AI-powered robots and assess how AI shapes the broader growth story of the robotics market.

Samantha Mou is a senior analyst at Interact Analysis

interactanalysis.com

MANUFACTURERS ACROSS the UK are investing heavily in AI, automation, and digital systems. The budgets are committed, the vendors are engaged, and the roadmaps are signed off. Yet results consistently fall short of expectations. According to leadership development specialists Kiki Clements and Jane Atkinson of Activate Business School, the explanation is both simple and uncomfortable. "This is not a technology issue," said Clements. "This is a leadership challenge."

The scale of underperformance is striking, with an estimated 80% of AI projects failing to deliver the value they were implemented for. The statistic points not to flawed technology, but to flawed implementation. "These projects are not failing because the technology is fundamentally flawed," asserted Clements. "They're actually failing because of how they're implemented, how they're led, and how they're adopted within the entire organisation."

Compounding the problem is that AI is not infallible. It produces convincing outputs that are not always accurate, can lack contextual understanding, and introduces risks that go undetected without sufficient human oversight. For manufacturers operating in regulated, safety-critical, or high-precision environments, that oversight gap carries serious consequences.

Root causes 

Clements and Atkinson identified three interconnected drivers behind digital transformation failures:

- The first is culture. Transformation creates uncertainty about employee roles, skills, and the future. The response from the shop floor is rarely outright resistance, but rather something quieter and harder to address. "What we consistently see is not necessarily open resistance, but quiet disengagement," said Clements. "People become cautious, they hold back, they wait to see what happens." When leaders lack confidence in digital topics themselves, they stay at a high level and delegate technical conversations rather than engaging with them. The result is a workforce that feels change is happening *to* them rather than *with* them.

- The second is process. A persistent misconception in digital transformation programmes is that technology will resolve existing inefficiencies. It will not. "If processes are unclear, inconsistent, or poorly understood, automation does not solve that – it accelerates the issues," warned Atkinson.

- The third, and most significant, is the leadership capability gap. Leaders are making strategic decisions while their teams are navigating operational realities, but without a shared language around data, systems, and AI, those two perspectives fail to align. "This makes it really difficult to prioritise effectively, build trust in new initiatives, and sustain momentum in transformational programmes," said Atkinson.

Targeted development

Organisations successfully navigating Industry 5.0 are not necessarily the most technologically advanced, but hey are the ones whose leaders can guide technology confidently through the organisation. Programmes such as the Level Four Manufacturing Leadership Programme and the Level Four AI and Automation Practitioner Programme are designed to build exactly that capability, not at a deep technical level, but at the level required for informed decision-making and effective leadership.

"Leaders learn how to assess whether automation adds value, how to question data rather than simply accept it, and how to manage risk when working with AI systems," explained Clements. Crucially, the learning is applied directly to participants' own processes and transformation challenges, generating measurable improvements in productivity, project implementation speed, and cross-functional collaboration.

The barrier most commonly cited is cost, but with many programmes now accessible through the Growth and Skills Levy at little or no direct cost to employers, that barrier is largely surmountable. The harder question is whether organisations can afford not to act.

"When you consider the cost of failed projects, delayed transformation, and underutilised technology," said Atkinson, "the real risk is not investing in your leadership capability. The real risk is continuing without it."

www.activatelearning.ac.uk

Energy now accounts for 23% of operating costs for businesses surveyed. More than half (54%) say rising energy prices pose a moderate or major threat to profitability, a figure that has grown with each successive EEM survey since they began in 2022. In sectors with the highest energy intensity, the exposure is considerably more acute. For these businesses, coping with a permanent energy crisis, managing energy use and managing financial performance have become the same task.

The response across industry has been decisive in intent. Nearly all organisations surveyed (98%) are already investing or actively planning to invest — up from 93% in 2024. Half are targeting Net Zero within five years. But financial discipline is shaping what gets funded and what gets deferred: 83% require a return on energy efficiency investment within five years, and 40% within two years. Nearly a third (31%) lack the specialist resource to implement projects. A further 29% report a digital skills gap, and almost a quarter (23%) say they do not have sufficient data to justify investment internally. The infrastructure for action is being built; the capacity to act on it is lagging behind.

"The cost of inaction is now harder to justify than the cost of investment, and businesses know it," said Mike Umiker, managing director of the Energy Efficiency Movement. "The share of organisations citing upfront cost as their primary barrier has fallen from 53% in 2024 to 43% today. That ten-point drop tells us something important: the financial argument for energy efficiency is landing. But progress is not accelerating to match. The barriers that remain are structural; a shortage of skills, a lack of specialist expertise, and in too many cases, insufficient data to make the internal business case. Those are not problems you can solve with a cheque."

The report also points to a significant broadening of where businesses are directing investment. The share of organisations prioritising transportation and logistics has risen from 37% in 2024 to 49% in 2026, reflecting a move away from individual asset upgrades toward a system-wide view of energy use. More than 60% have invested in energy audits and cloud data management to build the foundation for that wider approach.

"Energy efficiency is no longer a sustainability topic in isolation, it has become a test of industrial competitiveness and resilience," Umiker added. "81% of organisations say better financing or government incentives would increase their investment, and more than half say they need external support across four or more areas. The tools and the technologies exist. What is needed now is a stable framework across policy, finance, and industry to deploy them at the pace this moment demands."

The findings land at the centre of a growing global debate on industrial energy security. The IEA's Global Conference on Energy Efficiency convenes on 29-30 June, in Montreal, bringing together government ministers, industry leaders, and financial institutions to address exactly these challenges. EEM will utilise the report findings for the conference, connecting the voice of 2,000 senior business decision-makers directly to the policy and investment conversations underway.

The full report – Energy Efficiency Investment Report 2026: Rising energy costs are outpacing the efficiency response – covers major energy-intensive sectors across North America, Europe, Asia-Pacific, and Latin America. Survey respondents were senior decision-makers at small, medium and large organisations.

Download the full report here