Physical AI and robotics
Deloitte’s State of AI in the Enterprise finds that 58% of companies already make at least limited use of physical AI, such as robotics and automated machinery, and 80% expect to within two years. Only 18% use it to a moderate extent or more today.
Cost is the barrier respondents cite most often, and Deloitte warns that the physical side can far exceed the software. Its illustration is a warehouse automation project that might need hundreds of thousands of dollars in AI development but millions in infrastructure, robots and facility changes. For a robotics or automation supplier, the market is won on integration, service, safety approval and installed cost. Those are the decisions to which Fractionova brings AI-native Opportunity Intelligence and principal-led judgement.
State of AI in the Enterprise: The untapped edge is published by the Deloitte AI Institute. Released in January 2026, it draws on a survey of 3,235 director- to C-suite-level leaders in 24 countries, including India, the UAE and Saudi Arabia, fielded in August and September 2025, and on 15 interviews. Every participating organisation already uses AI daily. Figures are Deloitte’s and reflect what respondents report; the interpretation is Fractionova’s. Fractionova is not affiliated with Deloitte.
In two yearsToday
Asia Pacific
Europe, Middle East & Africa
Americas
At least minimal use of robotics, automated machinery and other physical AI. Asia Pacific leads today and in expectations.
Deloitte describes physical AI as AI systems that perceive the real world, make decisions and drive physical actions through machines or control systems. It sits where AI and machine learning meet sensors, controls and robotics. Four findings describe where adoption stands.
58% of companies report at least limited use of physical AI, and 18% use it to a moderate extent or more. Within two years, 80% expect at least limited use, with 15% expecting extensive use and 3% full integration as a core part of operations.
At least limited use of physical AI · % of companies · N=3,235
71% of Asia Pacific respondents report at least minimal use, against 56% in both the Americas and EMEA, and 20% report moderate use or more. 90% in Asia Pacific expect some use within two years.
Deloitte’s Asia Pacific sample of 475 includes 200 respondents in India. The report does not give results by country.
Deloitte notes that physical AI’s adoption curve is slower than that of agentic AI, which companies expect to rise from 23% to 74% at least moderate use over the same two years. It attributes the difference to higher costs and capital requirements, longer development cycles, stricter safety regulation and the need for specialised hardware and maintenance.
Use cases in controlled settings such as factories and warehouses progress much faster than those in open, real-world environments. Deloitte finds adoption most advanced in manufacturing, logistics and defence, with cobots on assembly lines, robotic picking arms, autonomous forklifts and inspection drones among common uses.
A note on the sample: every organisation Deloitte surveyed already has AI in daily use, and every respondent is involved in AI decisions or implementation. The figures describe AI-active enterprises, which may be ahead of the wider market. The survey was fielded in August and September 2025.
Asked which type of physical AI will have the greatest impact on their industry, respondents spread their answers widely. No single type dominates.
Type of physical AI expected to have the greatest impact on the respondent’s industry · % of companies · other 3% · N=3,235
For a supplier, a split this even suggests the opportunity is shaped more by sector and site than by technology type. The useful question is which customers, in which settings, are ready to buy.
Cost was the barrier to physical AI that respondents cited most often. Deloitte’s illustration: a warehouse automation project might require hundreds of thousands of dollars in AI development but millions in physical infrastructure, robotic systems and facility modifications. For a supplier entering a new market, these are the questions Fractionova would put first, organised by the dimension of the opportunity they affect.
Economics & commercial potential
Deloitte advises evaluating total cost of ownership, not equipment cost alone: facility retrofits, sensors and robots, integration with existing systems, maintenance and spare parts, and downtime during implementation. Companies that underestimate these costs risk delays or abandonment partway through.
What does the full installed cost look like for our target customers, and who in the value chain carries it?
Customer demand
Factories and warehouses progress faster than open environments, and manufacturing, logistics and defence lead adoption.
Which customer sites in our target market are controlled and repetitive enough to adopt now?
Partners & counterparties
Integration with existing systems and workflows, maintenance and spare parts are part of the cost of every deployment Deloitte describes. Someone close to the customer has to deliver them.
Which integrator or service partner can install, maintain and support our systems in the market?
Policy & geostrategic conditions
Physical AI systems often need approval from safety regulators, compliance with industry-specific standards and adherence to liability frameworks that do not always account for autonomous systems. Deloitte notes these requirements add time and cost.
Which approvals, standards and liability terms apply to our product in this market, and how long do they take?
Technology & capability
Because physical AI interacts with people, equipment and infrastructure, Deloitte stresses security, interoperability and resilience, with testing, certification and ongoing monitoring.
Does our offer include the integration, monitoring and support a buyer needs, or only the equipment?
Timing & market readiness
Respondents expect at least limited use to rise from 58% to 80% within two years, with Asia Pacific furthest ahead. Deloitte expects a slower curve than for software-based AI.
Is this the moment to enter, or to build the partner and service base that a later entry will need?
Bring the question in the form your board would ask it.
Is there a market for our robots or automation in India, and through whom do we reach it?
Should we assemble and service locally rather than export?
Which customer segment is ready to buy now, and at what installed cost?
Which integrators could carry our product, and how do we qualify them?
This is a hypothetical illustration, not a client engagement or a claim of achieved outcomes.
A European maker of collaborative robots sees demand from India’s automotive component suppliers, many of which run controlled, repetitive production lines. The robots are proven. Integration, service and installed cost will decide whether customers buy, and leadership needs to decide how to enter.
Fractionova’s AI engine would connect segment demand and site readiness with total installed cost, integrator capability, safety and certification requirements and the economics of local assembly and service. Principal-led review would establish which route supports the next commitment and what evidence would justify it.
Enter through a distributor and integration partnersSell through a national distributor with a network of integrators. The questions are which partners have automotive references, how service is assured and what margin the channel needs.
Partner with a systems integrator for turnkey cellsOffer complete robotic cells with one integrator responsible for installation and performance. The questions are which integrator, which applications to start with and how risk is shared.
Assemble and service locallyEstablish local assembly, spares and service to cut cost and lead times. The questions are the volume that justifies it, the partner or site, and the incentives available.
You do not need to choose a format first. The starting point is the decision your business needs to make.
Concentrated intelligence for a consequential choice.
A focused investigation around a defined decision, culminating in a decision brief with the recommended direction, unresolved questions and conditions for commitment.
For example: Decide which Indian customer segment and entry route to pursue first.
A coordinated mandate to develop market access and commercial relationships.
An integrated investigation into market priorities, entry structures and counterparties, with prioritised partners and preparation for engagement.
For example: Qualify integrators and distributors with automotive references, and prepare engagement.
A retained intelligence capability around your strategic agenda.
Recurring investigation and monitoring, supported by agreed business context and structured principal-led reviews.
For example: Track physical AI adoption, approvals and competitor entry across target markets.
Each engagement is commissioned against a defined business objective. Scope and fees reflect the analytical depth, market coverage, counterparty work and continuity required.
Founder & Principal Prabha Shankar leads the technical-commercial judgement behind Fractionova’s work. An engineer and international business specialist, he brings nearly three decades of experience across automotive engineering management, advanced mobility, new energy, international market development and complex programme execution.
At Fractionova, that experience shapes the questions put to the AI engine, the assumptions challenged during investigation and the commercial significance assigned to its findings.
Deloitte describes physical AI as AI systems that perceive the real world, make decisions and drive physical actions through machines or control systems. It brings together AI, machine learning, sensors, controls and robotics.
In Deloitte’s survey of 3,235 leaders, published in January 2026, 58% reported at least limited use of physical AI and 18% moderate use or more, and 80% expect at least limited use within two years. Every surveyed organisation already uses AI daily, so the wider market may be behind these figures.
Cost was cited most often. Deloitte advises counting total cost of ownership, including facility retrofits, sensors and robots, integration, maintenance and spare parts and downtime, which can significantly exceed the investment in AI models and software.
If the physical side of a deployment carries most of the cost and risk, the entry decision depends on local integration, service, safety approval and installed cost, not only on the product. Those factors decide which customers buy and which partners an entrant needs.
Fractionova brings AI-native Opportunity Intelligence and principal-led judgement to decisions on robotics and automation markets: which segment to pursue, which integrators or partners to engage and whether to assemble or service locally.
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Deloitte finds 77% of companies weigh where an AI solution comes from. What sovereign AI changes for cross-border sales, local partners and market entry.
A market to enter. An integrator to qualify. A local base to evaluate. A business case to test.
Start with what you are considering, what is at stake and what you need to determine.
Sources and notes. Figures are from the Deloitte AI Institute, State of AI in the Enterprise: The untapped edge (January 2026): a survey of 3,235 director- to C-suite-level leaders across six industries and 24 countries, fielded between August and September 2025, with 15 interviews. All participating organisations have AI in daily use. Physical AI findings are from figures 9 to 11 and pages 22 to 27; percentages may not sum to 100 because of rounding. Regional sample sizes are as Deloitte reports them. Fractionova is not affiliated with Deloitte.
Fractionova equips clients with intelligence and considered recommendations to inform their decisions. Investment and implementation decisions remain with the client; execution and outcomes depend on the organisations involved and prevailing conditions.