The Rise of Generalist Robots
Sophisticated robots that can flexibly perform diverse tasks are becoming cheaper, promising efficiency gains but also disrupting employment.
5.0 years
expected time to materialize
What does this trend encompass?
Autonomous, AI-enabled machines are learning to adapt and operate effectively in more complex environments. These generalist robots, capable of moving beyond single-purpose tasks, are performing diverse physical operations with unprecedented flexibility. Falling costs are making robotics commercially viable even in low-wage economies, promising efficiency gains but raising questions about the scope for workers to be redeployed in complementary roles. This trend is a subset of the 2025 trend, Deploying Autonomous Systems at Scale.
Why is it important?
As costs decline and the technology matures, the deployment of robotics is set to expand beyond manufacturing into other sectors currently dependent on human labor. Proactive transition programs, safety frameworks, and inclusive policies will be vital to ensure the benefits of generalist robots are broadly shared.
How can stakeholders prepare?
As highlighted by DET survey respondents, preparing for the materialization of this trend at the country level depends on the following key drivers:
Digital Infrastructure: reliable edge computing, safety sensors, and secure fleet management form the backbone of generalist robots, enabling real-time data exchange, adaptive learning, and safe, scalable operations across environments.
Industry Digital Transformation: industrial processes and workflows need to be redesigned to allow for the flexible, efficient, and safe integration of general robots in production systems that allow for human-robot collaboration rather than replacement.
Digital Innovation: open-source models, shared simulators, and reference designs accelerate experimentation through wider and cheaper access, driving cost-saving and efficiency innovations in the development of generalist robots.
Digital Capabilities: specialized training in robot operation, safety, and maintenance is necessary to build the workforce expertise required for safe and effective human–robot collaboration.
Digital for Health and Education: generalist robots are likely to offer extensive opportunities in the health and education sectors (e.g., through assisted care for the elderly and personalized tutoring for special-needs students), countries should therefore begin establishing the conditions necessary for safe pilot testing in these sectors to ensure effective and rapid deployment when available.
Impacts on the horizon
Prospective turning points that could catalyze this trend into rapid, widespread materialization
Open foundation models and simulation libraries
become the default research-to-production path for robotics; major universities and research centers adopt open-source reasoning, vision-language-action models and cutting-edge physics libraries, speeding skill transfer across robot bodies
The EU Machinery Regulation updates standards,
requiring clearer risk tests, emergency stops, and safe spacing between humans and robots. This ensures that robotics development does not prioritize innovation speed over human safety
General-purpose robots
handle mixed tasks in logistics and assembly under using unified and modular tools, combining foundation models with high-end edge compute
Recommendations
Private sector
Establish cross-industry pilot consortia
to deploy generalist robots in controlled, high-value settings such as warehouses, assembly lines, and healthcare support, sharing safety protocols, liability frameworks, and lessons learned to accelerate responsible scaling.
Partner with governments and labor organizations to co-design transition programs
that retrain displaced workers for robot supervision, maintenance, and exception handling roles, transforming workforce investment into a strategic competitive advantage.
Co-fund shared training for frontline teams
on supervision, exception handling, and maintenance while jointly setting clear rules for human handoffs, safe spacing, stop procedures, and incident reporting.
Build human-robot collaborative environments
through "co-bot" redesign of industrial processes and workflows, integrating modular/swappable tools and codified safety measures before scaling deployment.
Public sector
Create clear liability frameworks
assigning responsibility to manufacturers for design defects, deployers for safe configuration and monitoring, and operators for proper supervision and incident response, with easy-to-audit records and proportionate penalties.
Co-develop safety certification standards with industry
covering emergency stops, human-robot spacing, and risk assessments, offering supervised trial zones and recognition for compliant deployments.
Co-fund pilot hubs in emerging economies
to demonstrate affordable deployment and workforce transition pathways, ensuring smaller firms and developing regions can participate in and benefit from automation advances.
Jointly fund large-scale reskilling and just transition programs
for affected communities, prioritizing hands-on training in robot operations, supervision, instruction, and maintenance to prepare the workforce for emerging roles and to foster readiness and social cohesion before the large-scale deployment of robots.
IGOs, IOs, and others
Establish a global robotics commons
that publishes open reference models, shared simulation environments, and plain-language safety checklists as global public goods, lowering adoption costs for emerging economies.
Convene multi-stakeholder forums
to harmonize liability principles, ethical guidelines, and interoperability standards, publishing open playbooks, case studies, and incident lessons across languages.
Develop an open-source robotics foundation model library and ethical deployment playbooks
that democratize access to foundational automation tools and prevent technological stratification between developed and emerging markets.
Read the Digital Economy Trends 2026 report
Explore the full insights and analysis of the 2026 research.