The next big AI transformation will be on warehouse floors and within industrial field operations as AI deployment brings automation to the world’s 2.7 billion deskless workers.
A study published today (1 December) by MIT’s Center for Transportation and Logistics, drawing on responses from over 2,000 supply chain and warehousing professionals across 21 countries, found that over half surveyed report operating at advanced or fully automated maturity levels, especially among larger businesses with complex multi-site logistics networks.
The MIT study also found most companies dedicate 11%-30% of their warehouse technology budgets to AI and machine-learning initiatives, with the ROI period averaging two to three years.
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In the wider field operations market, AI adoption is being driven by technology advances that have enabled effective prediction maintenance for operational efficiency, a shift to physical AI driven by advances in robotics and connectivity which are streamlining industrial and manufacturing production, the deployment of machine vision systems for a more granular control of operations and, advances in digital twin technology for optimising processes.
The MIT report cites human job displacement fears as one of the greatest barriers to adoption. More than three-quarters of surveyed organisations saw a rise in employee productivity and satisfaction after implementing AI tools, and over half reported growing the size of their workforce.
The positive employee survey results, along with the emergence of new roles including AI/ML engineers, automation specialists, process-improvement experts, and data scientists, reflect the most common industry narrative used to address job displacement fears.
The ‘hottest’ AI companies are going industrial
Businesses on the frontier edge of AI, now entering the industrial sector, will be beneficiaries of a market opportunity that is set to reach $90.28bn by 2033 from $20.02bn in 2024 growing at a CAGR of 18.6%, according to some estimates.
On Nov 13, Industrial AI company IFS announced partnerships with Anthropic, Boston Dynamics Siemens and 1X signalling a wider trend for some of today’s most innovative AI and robotics companies setting their sights on industrial AI as the next frontier for scaling their technology.
The partnerships were announced at IFS’s Industrial X Unleashed conference in New York, where CEO Mark Moffat noted the vast sums of up to $10tn being spent on rebuilding the industrial world with AI. But the gap between this massive capital investment and the practical application of AI into industrial processes is just beginning in earnest, according to Moffat.
The partnerships demonstrate a shift towards AI integration “coming out from behind the desk” to serve the majority of the global workforce operating within industrial settings, says IFS chief product officer Christian Pederson. As one of Europe’s rare unicorns, the privately held $16bn Swedish company, headquartered in London, is pursuing partnerships with headline-grabbing deep tech companies like Anthropic and Boston Dynamics. But away from the headlines, a quiet AI revolution is happening as the industrial sector seeks to harness frontier AI for wholesale workflow automation.
The IFS Boston Dynamics collaboration focuses on serving industries where field operations are critical, including manufacturing, energy, utilities, mining, and other asset-intensive sectors. The partnership aims to unlock value in field operations, an area underserved by generic AI applications, says Pederson.
“It means that what we think of as digital workers can now do physical things. They are not only creating a work order for someone but they’re sending instruction to a robot to do the work for them,” Pederson told Verdict.
Pederson proffers that the white-collar nature of the technology industry itself and the mindset of its leaders, has meant that it has, naturally, hitherto focused on non-industrial areas. Pederson notes that from a product perspective, AI has, in general, reached a level of quality with a lower hallucination rates that should allow for the wholesale deployment of AI into industrial field processes. Unlike white collar enterprise AI, industrial settings require hundred percent accuracy. “In the industrial world, any hallucination means people will die,” explains Pederson.
IFS and its AI partners demonstrate what NexusBlack CEO Kriti Sharma considers an underserved area and the one area where AI has actually been underhyped. IFS launched its AI lab, NexusBlack, in April this year to address critical infrastructure use cases with predictive analytics for maintenance and continuity planning. The company’s new tool, Resolve, is built on Anthropic’s Claude models and benefits from IFS’s strategic acquisition, earlier this year, of agentic AI company TheLoops.
Physical AI – the confluence of robotic hardware, advanced connectivity and AI – has the potential to provide an “infinite workforce” and solve some of the world’s most difficult industrial challenges, according to Sharma. The NexusBlack team take the physical nature of their work seriously and are often, quite literally, living in aircraft hangers or sleeping on the production line floors of their customers’ industrial facilities. These are not the technology rollouts that most technology professionals have been accustomed to in their air-conditioned offices.
Pederson says some of the partnerships that bridging some of the “hottest” AI companies with those within the industrial field in a more asset-based setting are based on knowledge transfer. Anthropic, for example, is not used to working in an industrial setting and IFS can help the company train its models on industry relevant data and scenarios.
Applied AI Leader at Anthropic, Garvan Doyle, says there’s a broad set of steps the company takes to prioritise who with and where it works. “It’s based on our global footprint. It’s based on the different segments we operate in. It’s a pretty multi-factor step, but we try ensuring that we’re scaling our teams with thought,” he says.
A critical moment for industrial AI deployment
And while industrial AI has been deployed for some time, the market is at a critical juncture, says Vijay Guntur, CTO and head of ecosystems at HCLTech. Guntur cites the rapid advancement of AI platforms, combined with the increased availability of powerful simulation, robotics, and edge-compute technologies, means enterprises are finally able to bridge the gap between digital models and real-world deployment.
Manufacturing, logistics, energy, utilities, mining, and hi-tech, are all areas where digital twins, autonomous systems, and AI-driven automation create substantial improvements in productivity, resilience, and sustainability.
These autonomous systems connecting the physical and digital worlds mean industrial facilities running preventative maintenance scheduling, predictive failure analysis and automated anomaly detection. Workers out in the field can feed data back to enterprise systems, triggering autonomous decision making and action at source, as well as sending instructions for action required in the field, all within a single integrated platform.
Guntur says that notably, edge inference has become dramatically more affordable, removing a major barrier to deployment, with cost-per-inference at the edge dropping by 4x-20x over the past two years thanks to hardware leaps.
On 17 November, HCLTech launched a physical AI innovation lab in collaboration with US chipmaker Nvidia in Silicon Valley. The lab in Santa Clara, California, will help enterprises explore, incubate and scale industry physical AI and cognitive robotics applications.
Integrated with HCLTech’s global AI Lab network, the dedicated facility will combine the Nvidia technology stack and its core platforms with HCLTech’s set of physical AI solutions.
Guntur says of HCL’s Nvidia partnership and its timing – along with other leading partnerhips in the space – that it really does signal the beginning of a new era for industrial AI adoption. “The convergence of mature AI hardware/software stacks, the pressing need for smarter and more sustainable operations, and growing enterprise confidence in digital transformation are all driving the wider take-off of physical right now.”
Guntur says that the drive for organisations to stay competitive will mean many more collaborations within the area of industrial AI which he says will take the form of collaborative ecosystems like the HCL/Nvidia lab and IFS’s Nexus Black, adding: “They are emerging as key enablers for real-world breakthroughs in automation, safety, and operational intelligence.”
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