{"id":65526,"date":"2026-06-08T15:17:43","date_gmt":"2026-06-08T15:17:43","guid":{"rendered":"https:\/\/www.elixirr.com\/en-gb\/?p=65526"},"modified":"2026-06-08T15:41:06","modified_gmt":"2026-06-08T15:41:06","slug":"from-automation-to-physical-intelligence-why-robotics-is-becoming-an-operating-model-and-data-question","status":"publish","type":"post","link":"https:\/\/www.elixirr.com\/en-gb\/from-automation-to-physical-intelligence-why-robotics-is-becoming-an-operating-model-and-data-question\/","title":{"rendered":"From automation to physical intelligence: why robotics is becoming an operating model and data question"},"content":{"rendered":"\n
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Robotics, or \u201cPhysical AI\u201d,\u00a0is\u00a0entering a new phase. The question is no longer simply whether robots can perform more tasks. It is whether\u00a0organisations\u00a0can redesign\u00a0work\u00a0so robots create reliable,\u00a0safe\u00a0and economic value at scale \u2014 and whether they can capture the real-world data needed to improve those systems over time.<\/span>\u00a0<\/span><\/p>\n

That is why physical robotics is no longer simply a hardware story. It is becoming an operating model story, and increasingly, a data story.<\/span>\u00a0<\/span><\/p>\n

By physical intelligence, we mean AI-enabled systems that can perceive,\u00a0reason\u00a0and act in the physical world. But their commercial value\u00a0depends on\u00a0much more than the machine itself. It depends on the workflow, safety model, data layer, human-machine hand-offs\u00a0and operational system around the robot.<\/span>\u00a0<\/span><\/p>\n

The market momentum is already significant. More than 542,000 industrial robots were installed globally in 2024, bringing the operational installed base to 4.66 million systems worldwide. The International Federation of Robotics<\/a> expects annual installations to exceed 700,000 by 2028, with China accounting for 54% of global deployments in 2024. <\/span>\u00a0<\/span>\u00a0<\/span><\/p>\n

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This is no longer a niche technology trend. It is an infrastructure shift in how work gets done.<\/h2>\n

Several major technology and commercial shifts are now converging:<\/span>\u00a0<\/span><\/p>\n

Amazon has deployed more than one million robots across its operations network and introduced DeepFleet<\/a>, a generative AI foundation model designed to coordinate robot movement and improve fleet travel efficiency by 10%.\u00a0<\/span><\/p>\n

Google DeepMind<\/a> is advancing robotics foundation models and vision-language-action systems through Gemini Robotics, which it describes as enabling robots to perceive, reason, use tools and interact with humans across different robot forms.\u00a0<\/span><\/p>\n

Nvidia and ABB are investing heavily in simulation, synthetic data and digital twins. ABB\u2019s RobotStudio HyperReality<\/a>, enabled by Nvidia Omniverse libraries, is designed to close the sim-to-real gap and support virtual training before real-world deployment.\u00a0<\/span><\/p>\n

SoftBank is also making one of the most consequential strategic moves in the sector. It has agreed to acquire ABB\u2019s Robotics division for $5.375 billion<\/a>, subject to regulatory approvals, with completion expected in mid-to-late 2026. SoftBank frames the acquisition as part of a broader strategy across AI chips, AI robots, AI data centres and energy.\u00a0<\/span><\/p>\n

This matters because SoftBank\u2019s play is not just a bet on robots. It is a bet on the full Physical AI stack: compute, industrial robotics, AI models, data infrastructure, deployment channels and operating intelligence. ABB said its robotics division generated $2.3 billion in 2024<\/a> revenue and employed around 7,000 people, making this a major industrial platform rather than a speculative robotics start-up.\u00a0<\/span><\/p>\n

The\u00a0organisations\u00a0that succeed in this next phase will not be defined by machine capability alone. They will be defined by how effectively they redesign workflows, integrate robotics into operations, create safe human-machine hand-offs, manage fleets reliably at\u00a0scale\u00a0and capture proprietary real-world data.<\/span>\u00a0<\/span><\/p>\n

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From automation to physical intelligence<\/h2>\n

Not all robotics markets are maturing at the same speed.<\/span>\u00a0<\/span><\/p>\n

Industrial robotics, autonomous mobile\u00a0robots\u00a0and collaborative robots are already delivering measurable value in structured environments such as manufacturing,\u00a0logistics\u00a0and warehousing.<\/span>\u00a0<\/span><\/p>\n

Humanoids and embodied AI systems\u00a0remain\u00a0strategically\u00a0important, but\u00a0are commercially earlier-stage.<\/span>\u00a0<\/span><\/p>\n

 <\/p>\n\n\n\n\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t
Category<\/th>Current maturity <\/th>\n<\/tr>\n<\/thead>\n
Industrial robots<\/td>Mature and scaling<\/td>\n<\/tr>\n
Autonomous mobile robots<\/td>Scaling rapidly<\/td>\n<\/tr>\n
Collaborative robots<\/td>Selectively mature<\/td>\n<\/tr>\n
Facility service robots <\/td>Scaling in selected workflows<\/td>\n<\/tr>\n
Humanoids<\/td>Early commercial stage<\/td>\n<\/tr>\n
Embodied AI \/ robotics foundation models<\/td>Emerging<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

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The near-term value\u00a0remains\u00a0concentrated in repeatable operational environments where workflows, safety boundaries,\u00a0economics\u00a0and data capture are clear.<\/span>\u00a0<\/span><\/p>\n

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Where value will accrue<\/h2>\n

The key question is no longer whether robotics adoption will grow. It already is.<\/span>\u00a0<\/span><\/p>\n

The more important question is where value will sit across the robotics value chain.<\/span>\u00a0<\/span><\/p>\n

Historically, competitive advantage\u00a0centred\u00a0on hardware engineering and automation capability. Increasingly, however, value is shifting toward the operational layer around the robot:<\/span>\u00a0<\/span><\/p>\n