Robotics Developers Focus on AI Software to Address Performance, Study Finds

Robotics development is changing as systems become more software‑driven, AI‑enabled, and increasingly deployed alongside humans, according to the Inside the Robot: Architecture Benchmark Report, from QNX, a division of BlackBerry Limited. 

Based on a survey of 1,000 developers from around the world, the research revealed a growing focus on software foundations to address performance, security, and scalability challenges.

Key findings from the study include:  

• About 89% of respondents said that physical AI is critical to their future plans.
• Deterministic, real-time behavior is essential for 95% of robotics developers.
• Despite rising safety and security demands, 91% still rely on general-purpose operating systems (GPOS) to run real-time or safety-critical workloads.
• Highlighting concerns about suitability, 86% of respondents using a GPOS say they are open to changing their OS. 

The research also found that 27% of developers named software architecture and integration as their biggest performance bottleneck, compared with just 16% who point to hardware.

According to QNX, the research shows that future progress hinges less on new hardware and more on building systems that are predictable, secure, and capable of handling mixed levels of criticality. As robots move more widely from controlled environments into dynamic, real‑world settings such as city streets and factory floors, developers are recognizing that software foundations are the deciding factor as to whether innovations succeed or stall.

Looking ahead, 85% of developers also expect software to play an even greater role in robotics over the next three to five years, with teams anticipating their biggest investments will be in AI-driven decision making and cybersecurity—both at 51%— followed by operating systems and real-time control software at 37%, further reflecting how software foundations are becoming strategic assets as robotics systems grow more complex, interconnected, and distributed, according to the company.

Robotics teams are already feeling the impact, as 83% of respondents said their systems are now deployed alongside humans. Among those not yet deployed alongside humans today, 67% expect they will be within three to five years. This expanding presence in less controlled environments, from surgical suites to busy shop floors is driving higher expectations around reliability, safety, and predictable behavior, with 95% of respondents saying deterministic, real‑time execution is important to the systems they develop.

However, most development teams continue to rely on software not designed for real‑time or safety‑critical use with the research revealing that 91% of respondents run these workloads, at least in part, on GPOS, even though safety‑certified commercial solutions are rated as the best fit for their needs. As a result, 86% of these GPOS users say they are open to changing their OS; a contrast that QNX said encapsulates the growing tension between flexibility and the need for predictable, guaranteed behavior as robotics deployments scale. 

Regulatory and compliance demands further intensify these challenges, as 66% reported project delays due to certification processes, rising to around 70% in the UK and Germany. In contrast, only 56% report delays in China, where regulatory requirements are far less stringent. These delays directly affect development costs, delivery timelines, and commercial risk. Cybersecurity and functional safety standards are among the most challenging areas to comply with, cited by 51% and 49% of respondents respectively.

Despite these pressures, ambition and optimism across the industry remain high, QNX said. Physical AI is firmly on the roadmap, with 89% of respondents saying AI‑enabled robots that can perceive, reason, and act autonomously in the physical world will be critical to their organization's strategy over the next three to five years, with China leading the pack globally. Confidence in the long‑term potential of Physical AI is strong, but readiness remains uneven. Only 29% of respondents feel "very confident" in their ability to make safe, predictable decisions in real‑world environments.

"Robotics teams are clearly pushing toward more intelligent, autonomous systems, but the data shows they are also running up against the very real limits of architectures that were never designed for this level of complexity or accountability," said Jim Hirsch, global vice president of sales at general embedded markets at QNX. "Developers consistently cite four core challenges: integration complexity, certification delays, functional safety risks in human‑machine interaction, and ensuring predictable behavior when it matters most. The good news is that these are all solvable problems and by focusing on stronger software foundations, developers can set the stage for faster innovation and a new generation of safe, reliable, and highly autonomous robots." 

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