**Robot Service Map** | Sector Analysis | 2025-07
The announcement
New market projections released in July 2025 indicate that the global collaborative robot sector is set to expand at a compound annual growth rate of 12 percent through 2031. The forecast, which originates from industry data compiled by Robotics & Automation News, points to sustained demand for automation across multiple verticals, with healthcare emerging as a particularly significant driver of adoption over the next six years.
The headline figure for collaborative robots — often referred to as cobots — reflects a broader trend toward flexible automation solutions that can work alongside human operators without the need for extensive safety fencing or reprogramming. While the 12 percent CAGR applies specifically to the collaborative robot segment, the underlying data also reveals a much larger and faster-growing adjacent market: artificial intelligence in healthcare. According to the same source material, the AI in healthcare market is projected to climb from USD 36.67 billion in 2026 to USD 194.79 billion by 2031, representing a CAGR of 39.7 percent. The market was valued at USD 25.88 billion in 2025.
This dual-track growth — a steady 12 percent annual rise for cobots and a nearly 40 percent surge for healthcare AI — suggests that the two sectors are becoming increasingly intertwined. The source material identifies several macroeconomic and clinical factors fueling this expansion, including rising provider demand for automation, nationwide labor shortages, increasing clinical complexity, and robust investment in predictive analytics, imaging AI, and generative AI (GenAI) tools.
For robotics integrators, system designers, and end users across Europe, the announcement carries practical implications. The 12 percent CAGR for collaborative robots is not a breakout number; it is a steady, compounding rate that points to gradual but consistent market maturation. By contrast, the healthcare AI figures indicate a sector in rapid acceleration, one where the convergence of robotics and intelligent software is likely to redefine clinical workflows, surgical precision, and postoperative care.
The source material does not specify a particular day for the publication of these figures, but the data was released in July 2025 (2025-07). The forecast period runs through 2031, and the baseline year for the healthcare AI market is 2025, with projections extending from 2026 onward.
Product and availability details
The source material breaks down the AI in healthcare market by function, tools, and end user, offering a granular view of where growth is concentrated and how robotics fits into the broader automation landscape.
By function, the market is segmented into imaging, robotics, AI scribe, telehealth, clinical decision support (CDS), precision medicine, radiation, revenue cycle management (RCM), and cybersecurity. Among these, robotics and imaging are highlighted as particularly dynamic areas, with the source material emphasizing intraoperative guidance, postoperative analysis, and radiation therapy as key application zones.
In the intraoperative guidance segment, the source material points to a clear "inclination toward minimally invasive surgeries and faster patient recovery" as a primary driver of implementation. This is a notable shift from earlier generations of surgical robotics, which often focused on open procedures or required large dedicated operating rooms. The current trend favors smaller, more flexible robotic systems that can assist surgeons in confined anatomical spaces, reduce tissue trauma, and shorten hospital stays. The source material does not disclose specific product names, manufacturers, or availability timelines for these systems, but the functional description aligns with the capabilities of modern collaborative robot arms adapted for surgical use.
Postoperative analysis and recovery represent another growth vector. The source material states that the "ability to predict recovery patterns, identify risks, and provide personalized rehabilitation plans" will boost demand in this segment. This is where AI-driven robotics intersects most directly with data analytics. A robotic system used in surgery generates vast amounts of kinematic data — joint angles, force profiles, motion trajectories — that can be fed into machine learning models to anticipate complications, tailor physiotherapy regimens, and monitor patient progress remotely. The source material does not specify which companies are commercializing such systems, nor does it provide pricing or deployment timelines. What is clear is that the market is moving beyond the robot-as-tool paradigm toward a robot-as-data-platform model.
Radiation therapy is the third major application area identified in the source material. Here, the emphasis is on "motion synchronization and auto contouring," with the stated need for "precise delivery of radiation and minimum exposure to surrounding healthy tissues" reinforcing segmental growth. In practical terms, this means robotic systems that can track a tumor's movement in real time — for example, during breathing — and adjust the radiation beam accordingly. Auto contouring refers to AI-driven segmentation of medical images to identify tumor boundaries and organs at risk, a task that is time-consuming and error-prone when performed manually. The source material does not disclose specific technical specifications, regulatory clearances, or vendor announcements, but the functional requirements are well defined.
By tool type, the market is segmented into machine learning (ML), natural language processing (NLP), and computer vision. These are the underlying technologies that enable the functions described above. Computer vision is particularly relevant for imaging and intraoperative guidance, while ML underpins predictive analytics and personalized rehabilitation planning. NLP is more associated with AI scribe and clinical documentation functions, though it also plays a role in extracting insights from unstructured medical records.
By end user, the market is divided into hospitals, ambulatory surgery centers (ASCs), and payers. The source material does not provide a breakdown of market share by end-user segment, nor does it specify which segment will grow fastest. However, the emphasis on minimally invasive surgery and faster recovery suggests that ASCs — which typically perform same-day procedures — are likely to be early adopters of compact robotic systems. Hospitals, with their larger capital budgets and more complex case mixes, are expected to remain the primary buyers for high-end surgical robots and radiation therapy systems. Payers are relevant primarily as reimbursers and as users of AI for claims processing and fraud detection.
The source material also notes "expanding regulatory support, deeper EHR AI integration" as additional growth factors, though the text is cut off at that point. It does not specify which regulatory bodies are expanding support, nor does it provide details on EHR integration timelines. What can be inferred is that regulatory clarity is seen as a positive catalyst for market growth, and that embedding AI and robotics into electronic health record workflows is considered a prerequisite for widespread clinical adoption.
What it means for buyers
For buyers — whether they are hospital procurement officers, ASC administrators, or robotics integrators — the source material offers a mixed picture of opportunity and caution.
On the opportunity side, the 12 percent CAGR for collaborative robots signals a stable, predictable market environment. Buyers can plan capital expenditures with reasonable confidence that the technology will remain relevant and that vendor competition will keep prices in check. The healthcare AI figures, with their 39.7 percent CAGR, suggest that early adopters may gain a competitive advantage by integrating AI-driven robotics into their workflows before the market becomes saturated.
The source material identifies labor shortages as a key driver of automation demand. For buyers, this translates into a clear business case: robots are not replacing skilled clinicians but rather filling gaps left by an insufficient workforce. In surgical settings, a collaborative robot can assist with repetitive tasks such as retraction, suturing, or camera control, allowing the surgeon to focus on higher-level decision-making. In radiation therapy, motion synchronization reduces the need for manual patient positioning and re-imaging, freeing up technicians for other duties. In postoperative care, AI-driven rehabilitation plans can be delivered remotely, reducing the need for in-person therapy sessions.
Clinical complexity is another factor buyers should weigh. As surgical procedures become more sophisticated and patient populations older and sicker, the margin for error narrows. Robotic systems with AI-enhanced imaging and decision support can help standardize care, reduce variability, and catch potential complications earlier. The source material does not provide clinical outcome data, so buyers should not assume that these systems automatically improve patient outcomes. However, the direction of the market is clear: automation is being adopted to manage complexity, not to add it.
Investment in predictive analytics, imaging AI, and GenAI is cited as a strong driver of market growth. For buyers, this means that the software layer of robotic systems is evolving rapidly. A robot purchased today may receive over-the-air updates that add new AI capabilities, provided the vendor supports such updates. Buyers should inquire about upgrade paths, data integration capabilities, and interoperability with existing EHR systems. The source material does not disclose specific vendor policies on software updates, so buyers should verify these details directly with manufacturers.
The source material also highlights the need for precise radiation delivery and minimal exposure to surrounding healthy tissues. For buyers in oncology, this is a critical differentiator. Systems that offer motion synchronization and auto contouring can reduce treatment times, lower the risk of collateral damage, and potentially improve patient throughput. However, these systems are likely to carry a premium price, and buyers should conduct a thorough cost-benefit analysis that accounts for training, maintenance, and patient volume.
One area where the source material is notably silent is on the collaborative robot market's specific product segments. The 12 percent CAGR figure is presented without a breakdown by payload capacity, reach, or application. Buyers should not assume that all cobot segments will grow at the same rate. It is plausible that smaller, tabletop cobots for surgical assistance will grow faster than larger industrial units, but the source material does not provide such granular data. Similarly, the source material does not disclose regional variations — whether Europe, North America, or Asia-Pacific will lead growth is not specified.
Another gap is the absence of pricing information. The source material provides market size figures in USD but does not indicate average selling prices for robotic systems or AI software licenses. Buyers should expect significant price variation depending on the application, the level of AI integration, and the vendor's service model. Some vendors may offer robots as a service (RaaS) with monthly fees, while others may require upfront capital purchases. The source material does not address these business models.
Regulatory support is mentioned as a growth factor, but the source material does not specify which regulations are expanding or in which jurisdictions. Buyers in the European Union should be aware that the Medical Device Regulation (MDR) and the AI Act will have implications for AI-driven robotics. The source material does not provide details on compliance timelines or certification requirements, so buyers should consult with regulatory experts and their legal teams.
Finally, the source material notes "deeper EHR AI integration" as a driver, but the text is truncated. Buyers should interpret this as a signal that seamless data exchange between robotic systems and EHR platforms will become a standard expectation. Proprietary systems that lock buyers into a single vendor's ecosystem may become less attractive over time. Interoperability should be a key criterion in any procurement decision.
In summary, the source material paints a picture of a market that is growing steadily in the collaborative robot segment and explosively in the healthcare AI segment. Buyers should approach procurement with a clear understanding of their clinical needs, a realistic assessment of the total cost of ownership, and a preference for systems that offer flexibility, interoperability, and a clear upgrade path. The source material does not provide specific vendor recommendations, product comparisons, or implementation timelines, and buyers should not rely solely on these market projections when making purchasing decisions. Instead, these figures should serve as a strategic backdrop for more detailed due diligence.
Published by Vigla Media OÜ (Estonia).
Sources
- https://roboticsandautomationnews.com/2025/07/04/global-collaborative-robot-market-forecast-to-grow-12-percent-a-year-to-2031/92901/