The frontline of artificial intelligence (AI) transformation is increasingly shifting towards warehouse floors and industrial field operations, engaging the world’s vast workforce of deskless workers, a group numbering 2.7 billion globally. A recent study by MIT’s Center for Transportation and Logistics, surveying over 2,000 supply chain and warehousing professionals across 21 countries, reveals that more than half of the organisations report operating at advanced or fully automated AI maturity levels. This is most evident among larger businesses managing complex, multi-site logistics networks, where AI is playing a crucial role in elevating operational efficiency.
According to the MIT report, companies allocate between 11% and 30% of their warehouse technology budgets to AI and machine learning initiatives, noting a typical return on investment period of two to three years. This rapid return is underpinned by significant gains in accuracy, speed, and control within warehouse operations, with 90% of organisations now leveraging AI/ML to boost these capabilities. The AI revolution in logistics is also reshaping the workforce, prompting the emergence of new roles such as AI/ML engineers, automation specialists, and data scientists. Despite prevalent concerns around job displacement, over three-quarters of surveyed firms observed increased employee productivity and satisfaction post-AI deployment, with more than half actually expanding their workforce, a narrative increasingly used to address AI-related job fears.
Beyond warehousing, AI adoption is extending robustly into industrial field operations. Advances in predictive maintenance, robotics, machine vision, and digital twin technologies are collectively driving a physical AI revolution, one that moves AI applications from virtual models “behind the desk” into the real world where physical tasks are executed. This approach is exemplified by IFS, a European unicorn company headquartered in London, which recently announced strategic partnerships with leading AI and robotics innovators such as Anthropic, Boston Dynamics, Siemens, and 1X Technologies. These partnerships, revealed at IFS’s Industrial X Unleashed conference in New York, spotlight the ongoing industrial AI evolution aiming to harness frontier AI technologies for wholesale automation of workflows in sectors including manufacturing, energy, utilities, and mining.
IFS’s collaboration with Boston Dynamics, for instance, integrates autonomous inspection robots with IFS.ai’s capabilities to create agentic AI systems that combine sensing, predictive decision-making, and autonomous action in field operations. Christian Pederson, IFS’s Chief Product Officer, emphasises that such AI-powered “digital workers” are breaking new ground by performing physical tasks traditionally done by human operators, moving industrial AI from purely digital realms to physical reality. He also stresses the critical need for near-perfect AI accuracy in industrial contexts, where errors can have dire consequences, contrasting the typically higher tolerance for AI “hallucinations” seen in white-collar AI applications.
Another notable development is IFS’s NexusBlack AI lab, established to focus on critical infrastructure use cases involving predictive analytics and maintenance continuity. NexusBlack leverages Anthropic’s Claude models and integrates agentic AI technology via IFS’s acquisition of TheLoops, aiming to pioneer solutions that fuse robotic hardware, high-speed connectivity, and AI into what is termed “physical AI.” Kriti Sharma, CEO of NexusBlack, highlights the unique challenges of deploying AI in industrial environments, where teams often work directly on factory floors or even aircraft hangars, an experience far removed from typical office-based technology rollouts.
The momentum behind industrial AI is further reinforced by HCLTech’s recent launch of a physical AI innovation lab in collaboration with Nvidia in Silicon Valley. This lab, part of HCLTech’s global AI network, combines Nvidia’s advanced hardware and software stacks with HCL’s physical AI solutions to support enterprises in developing autonomous robotics and cognitive AI applications. Vijay Guntur, HCLTech’s CTO and head of ecosystems, notes that the rapid advancement of AI platforms alongside cheaper and more powerful edge-computing capabilities have made large-scale industrial AI deployment more feasible than ever.
This convergence of mature AI technologies, the imperative for smarter, more sustainable industrial operations, and growing enterprise confidence signals a significant turning point. Both Guntur and Pederson foresee that the future of industrial AI will be shaped by collaborative ecosystems, such as IFS’s NexusBlack and HCLTech’s Nvidia lab, serving as critical enablers of breakthroughs in automation, operational intelligence, and workplace safety.
As the industrial AI market is projected to soar from around $20 billion in 2024 to over $90 billion by 2033, growing annually at nearly 19%, bringing AI out of office cubicles and onto industrial floors promises transformative impacts. This shift will not only redefine operational workflows but also reshape the industrial workforce, with technology increasingly becoming a partner in physical tasks. While challenges remain, including the critical necessity for foolproof AI accuracy and addressing workforce integration, the industrial AI revolution is unmistakably gaining steam, heralding a new era of productivity and innovation across asset-intensive sectors worldwide.
📌 Reference Map:
- [1] (Just Food / MIT Center for Transportation and Logistics) – Paragraphs 1, 2, 3, 4, 5, 6, 7, 8, 9
- [2] (MIT Center for Transportation and Logistics) – Paragraph 2
- [3] (IFS Press Release) – Paragraph 4
- [4] (IFS Press Release) – Paragraph 5
- [5] (IFS Press Release) – Paragraph 5
- [6] (IFS Press Release) – Paragraph 5
- [7] (IFS Press Release) – Paragraph 6, 7The next significant wave of artificial intelligence (AI) transformation is unfolding on warehouse floors and within industrial field operations, targeting the world’s 2.7 billion deskless workers. A recent study by MIT’s Center for Transportation and Logistics, based on over 2,000 supply chain and warehousing professionals across 21 countries, reveals that more than half of the organisations surveyed operate at advanced or fully automated AI maturity levels. This trend is particularly pronounced among larger businesses managing complex, multi-site logistics networks.
The study highlights that companies allocate between 11% and 30% of their warehouse technology budgets to AI and machine learning initiatives, with returns on investment typically realized within two to three years. Enhancements are being seen in operational accuracy, speed, and control, with 90% of organisations now integrating AI and machine learning to optimise warehouse operations. Contrary to fears about job losses, the report notes that over three-quarters of organisations experienced rises in both employee productivity and satisfaction after implementing AI. Additionally, more than half observed workforce growth, as new roles like AI/ML engineers, automation specialists, and data scientists emerge.
Beyond logistics, AI adoption is now accelerating in industrial field operations where advances in robotics, connectivity, machine vision, and digital twin technologies have enabled predictive maintenance and streamlined production. This “physical AI” is beginning to transform industrial workflows by merging robotic hardware with intelligent automation in real-world settings. One European unicorn, IFS, headquartered in London, epitomises this shift. At its recent Industrial X Unleashed event in New York, IFS unveiled strategic partnerships with leading AI and robotics players including Anthropic, Boston Dynamics, Siemens, and 1X Technologies. These alliances aim to deploy AI at scale across sectors such as manufacturing, energy, utilities, and mining.
IFS’s collaboration with Boston Dynamics focuses on integrating autonomous inspection robots with IFS.ai’s agentic AI capabilities to enable robots that can sense, decide, and act autonomously within field operations. Christian Pederson, IFS’s Chief Product Officer, explained that these “digital workers” extend AI’s reach from purely digital tasks to physical execution, offering a significant leap for industrial automation. He stressed the critical need for near-perfect AI accuracy in industrial environments, where errors could endanger lives, contrasting with tolerances seen in white-collar AI applications.
IFS also launched NexusBlack, an AI innovation lab dedicated to predictive analytics and maintenance within critical infrastructure. NexusBlack harnesses Anthropic’s Claude models alongside agentic AI technology acquired through IFS’s purchase of TheLoops to address complex industrial challenges. Kriti Sharma, NexusBlack CEO, underscores the physical demands of industrial AI deployments, where engineering teams often work on factory floors and in harsh environments, a departure from typical tech industry conditions.
Further driving the industrial AI revolution, HCLTech has launched a physical AI innovation lab in partnership with Nvidia in Silicon Valley. Integrating Nvidia’s advanced hardware and software platforms with HCLTech’s physical AI solutions, this lab supports enterprises in scaling cognitive robotics and autonomous systems. Vijay Guntur, HCLTech’s CTO, noted that the convergence of advancing AI platforms, affordable edge computing, and enterprise demand for sustainable operations signals a critical moment in industrial AI deployment.
Both Pederson and Guntur foresee collaborative ecosystems like IFS’s NexusBlack and HCLTech’s Nvidia lab emerging as vital enablers of breakthroughs in automation, safety, and operational intelligence. With the industrial AI market projected to grow from $20 billion in 2024 to over $90 billion by 2033, expanding at nearly 19% annually, the promise of AI-led workflow automation is set to transform industries reliant on physical field operations. This evolution not only boosts productivity and resilience but also redefines workforce dynamics, positioning AI as an indispensable partner for frontline workers across asset-intensive sectors worldwide.
📌 Reference Map:
- [1] (Just Food/MIT Center for Transportation and Logistics) – Paragraphs 1, 2, 3, 4, 5, 6, 7, 8, 9
- [2] (MIT Center for Transportation and Logistics) – Paragraph 2
- [3] (IFS Corporate News) – Paragraph 4
- [4] (IFS Corporate News) – Paragraph 5
- [5] (IFS Corporate News) – Paragraph 5
- [6] (IFS Corporate News) – Paragraph 5
- [7] (IFS Corporate News) – Paragraphs 6, 7
Source: Noah Wire Services
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The narrative presents recent developments in AI adoption within industrial operations, referencing studies and partnerships from late 2025. The earliest known publication date of similar content is November 2025, indicating a high freshness score. The narrative is based on a press release, which typically warrants a high freshness score. However, the presence of similar content across multiple outlets suggests potential recycling. No significant discrepancies in figures, dates, or quotes were found. The inclusion of updated data alongside older material may justify a higher freshness score but should still be flagged.
Quotes check
Score:
9
Notes:
Direct quotes from IFS’s Chief Product Officer, Christian Pederson, and HCLTech’s CTO, Vijay Guntur, are present. These quotes appear in recent press releases from IFS and HCLTech, indicating they are not recycled from earlier material. No variations in wording were found, suggesting consistency in the reporting.
Source reliability
Score:
9
Notes:
The narrative originates from reputable organisations: Just Food, MIT’s Center for Transportation and Logistics, IFS, and HCLTech. These sources are well-established and credible, lending strength to the report. No unverifiable entities or fabricated information were identified.
Plausability check
Score:
8
Notes:
The claims regarding AI adoption in industrial operations are supported by recent studies and press releases from credible sources. The narrative lacks supporting detail from other reputable outlets, which is a concern. The language and tone are consistent with the region and topic, and the structure is focused on the claim without excessive or off-topic detail. The tone is formal and appropriate for corporate communications.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The narrative presents recent developments in AI adoption within industrial operations, supported by credible sources and consistent reporting. While the presence of similar content across multiple outlets suggests potential recycling, no significant discrepancies or issues were identified. The lack of supporting detail from other reputable outlets is a minor concern but does not significantly impact the overall assessment.
