Companies like Anthropic and Boston Dynamics, which are well-known in the deep tech industry, are seeking to expand their operations in the industrial sector through strategic partnerships. The upcoming significant shift in AI technology will primarily impact warehouse operations and industrial field activities as the implementation of AI leads to automation for the vast number of deskless workers worldwide.
A recent study conducted by MIT’s Center for Transportation and Logistics, based on feedback from more than 2,000 professionals in supply chain and warehousing across 21 countries, revealed that a majority of respondents, particularly those from larger enterprises with complex logistics networks, are operating at advanced or fully automated levels. The study also highlighted that many companies allocate a significant portion (11%-30%) of their warehouse technology budgets towards AI and machine-learning projects, with an average return on investment period of two to three years.
In the broader industrial field operations market, the adoption of AI is being fueled by technological advancements enabling predictive maintenance for enhanced operational efficiency. This includes a shift towards physical AI driven by robotics and connectivity improvements, the integration of machine vision systems for better operational control, and advancements in digital twin technology for process optimization.
Despite concerns about potential job displacement due to AI adoption, the MIT report indicated that a large percentage of organizations observed increased employee productivity and satisfaction following the implementation of AI tools. Additionally, many reported an expansion in their workforce size.
Businesses at the forefront of AI innovation are now venturing into the industrial sector, anticipating significant growth opportunities. Estimates suggest that the market for industrial AI is projected to reach $90.28 billion by 2033 from $20.02 billion in 2024, growing at a compound annual growth rate (CAGR) of 18.6%.
Recent partnerships announced by Industrial AI company IFS with Anthropic, Boston Dynamics Siemens, and 1X reflect a broader trend where leading AI and robotics companies are targeting industrial AI as their next area for expansion. These collaborations signify a shift towards integrating AI technologies into industrial settings to cater to the majority of the global workforce operating within such environments.
The collaboration between IFS and Boston Dynamics is specifically focused on industries where field operations play a critical role, such as manufacturing, energy, utilities, mining, and other asset-intensive sectors. Industry experts point out that historically, the tech industry has primarily concentrated on non-industrial sectors but is now exploring opportunities within industrial domains.
Advancements in AI platforms coupled with improved simulation capabilities, robotics technologies, and edge computing have enabled enterprises to bridge the gap between digital models and real-world applications. Various sectors including manufacturing, logistics, energy, utilities, mining, and hi-tech are witnessing significant enhancements in productivity, resilience, and sustainability through digital twins, autonomous systems, and AI-driven automation.
The affordability of edge inference has significantly decreased over the past few years due to hardware advancements which has removed a major obstacle to deployment. This reduction in cost has made deploying autonomous systems for preventive maintenance scheduling, predictive failure analysis, and automated anomaly detection more accessible for industrial facilities.
The combination of mature AI hardware/software solutions along with the growing demand for smarter and sustainable operations is propelling the widespread adoption of physical AI technologies currently. Experts predict that as organizations strive to remain competitive, more collaborative efforts within industrial AI ecosystems will emerge such as initiatives like HCLTech/Nvidia lab and IFS’s Nexus Black project.