Converging Frontier Technologies

The convergence of diverse frontier technologies has the potential to bring
about groundbreaking cross-sectoral innovations.

2.2 years

expected time to materialize

What does this trend encompass?

Digital and physical technologies are beginning to converge, especially where AI acts as an integrating layer across manufacturing, robotics, biotechnology, and materials science. Examples include AI-accelerated drug discovery and embedded medical devices; blockchain-secured quantum communication; AI-driven materials informatics; digital twin simulations; and robotic lab modules. While maturing at different speeds, these intersections of technologies all depend on trusted and secure digital infrastructure, from cloud platforms to automation systems and advanced compute for simulation dynamics. By allowing data, models, and physical processes to operate in coordinated loops, convergence is enabling more autonomous workflows and increasing the share of economic activity executed through software, models, and automated decision systems. It is deepening the digital economy’s reliance on integrated data flows, secure infrastructure, and reliable governance frameworks.

Download the 2026 report (PDF)

Why is it important?

Convergence matters because it reshapes how value is created, reconfigures production systems, creates new interdependencies among value chains, and alters competitiveness. Firms and countries that integrate these combinations gain structural advantages in capability, cost, and efficiency. Convergence also challenges governance, as decisions in one domain (e.g., AI data regulations) start to influence outcomes in others (e.g., biotech research or materials design). This necessitates integrated regulatory approaches and coordinated safety frameworks.

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 Innovation: cross-disciplinary R&D that links AI models, domain-specific datasets, digital twin simulation tools, and automated experimentation environments enable frontier technologies to be developed as integrated workflows rather than in isolation.

Digital Infrastructure: convergence depends on digital infrastructure that allows different technologies to operate in connected workflows rather than as standalone systems. This entails interoperable data and connectivity standards, synchronized sensing networks, and secure edge-to-cloud computing architecture so technologies can exchange data, coordinate tasks, and run jointly.

Digital Capabilities: promoting literacy in systems engineering, data integration, simulation workflows, and cross-domain problem solving will enable diverse teams of scientists, engineers, policymakers, and business leaders to work cross-functionally and design, test, and operate combined frontier technologies with minimal integration failures.

Industry Digital Transformation: shared design frameworks, common data models, and coordinated testing cycles create a unified foundation for development. This will enable AI models, automation systems, digital twins, and emerging materials or biotech processes to operate against consistent production requirements.

Digital Policy and Governance: harmonized standards, incident reporting, cross-sectoral sandboxes, clear regulations, and governance frameworks that focus on measurable results all support the safe, privacy-preserving, and accountable development of converging technologies.

Impacts on the horizon

Prospective turning points that could catalyze this trend into rapid, widespread materialization

2026

Factory lines integrate AI vision models

for defect detection, digital twins for production planning, and collaborative robots for plan executions. Vendors ship standardized packages combining these technologies for industry-specific use-cases.

Decentralized physical infrastructure networks

are projected to grow to a US$3.5 trillion market. They provide more resilient, efficient, and democratic digital systems using a combination of decentralized AI models and blockchain technology to coordinate activities and resources (compute, storage, bandwidth, and sensor data) for wireless networks, energy systems, and transportation platforms

2028

2030

Materials informatics develops a closed R&D loop:

AI models propose candidates and predict properties; digital twin simulations screen them for viability; robotic lab modules synthesize and test them; and the results flow back to train the AI models, improving advanced material discovery

Recommendations

Private sector

Establish cross-industry convergence labs

that co-develop integrated technology stacks combining AI, robotics, spatial computing, biology and advanced materials that accelerate time-to-market while reducing fragmentation.

Build collaborative ecosystems rather than isolated prototypes

to unlock scalable solutions that reshape value chains, compress innovation cycles, and create new competitive advantages.

Invest in workforce integration and literacy programs

that foster systems thinking and equip professionals to confidently design, test, and operate complex, converged systems comprised of hardware, software, connectivity and biological components.

Partner with public sector entities to pilot converged technologies in regulated industries

co-creating harmonized standards and incident-reporting mechanisms that meet safety, privacy, and accountability expectations.

Public sector

Champion the development of agile, human-centered governance frameworks

by establishing cross-sectoral task forces dedicated to the ethical deployment of converged frontier technologies.

Pool public-private funding

for shared compute, reference datasets, and digital twin infrastructure accessible to SMEs.

Co-create regulatory pathways

that span digital, physical, and biological domains to ensure equitable access and public trust.

IGOs, IOs, and others

Convene multi-stakeholder coalitions

to publish open reference architectures, cross-domain ontologies, and assurance frameworks that ensure compatibility across borders.

Establish shared principles for responsible deployment in sensitive domains

like healthcare, materials discovery, and critical infrastructure.

Coordinate capacity-building initiatives that prioritize emerging markets'

access to compute, datasets, test-bed environments, and cross-disciplinary research funding.