Consider an AI tool that delivers consistently accurate results and demonstrates impressive capabilities. If the people who are expected to rely on it can’t understand how decisions are reached or challenge outcomes that appear wrong, why would they trust it with an important decision? Would an organization be willing to integrate it into a critical system? Would regulators be confident that its risks could be understood and managed?

Technical capability is important, but it’s not sufficient to ensure adoption or scale. The same is true in emerging technologies. Quantum technologies, for example, have the potential to transform computing, communications, and measurement. Yet their broader adoption will depend not only on what they can achieve, but on whether they can be deployed securely, reliably, and with confidence.

For technologies to move from breakthrough to widespread diffusion, people need trust in the systems they use. Businesses need to know that technologies will perform reliably and securely. Governments and regulators need credible ways to understand risks and assess outcomes. Innovation may begin with technical capability, but its ability to scale depends on something broader: Trust.

I’ve seen this directly through my work in standards and cybersecurity. As emerging technologies accelerate digital transformation, they create significant opportunities. But they also introduce greater complexity and interdependence. Technologies don’t operate in isolation. They connect with other systems, rely on data and shared infrastructure, cross borders, and depend on networks of suppliers and partners.

Trust, in that environment, can’t simply be claimed. It must be demonstrated.

From performance to confidence

Returning to that AI tool: Knowing that it performs accurately is important, but it tells us only part of what we need to know. How was it developed? What data does it depend on? How are risks identified and managed? How will it behave in the context in which it’s actually deployed? How can its performance be assessed over time?

Claims of trustworthiness alone can’t answer these questions. We need common reference points that establish clear expectations and provide credible evidence that those expectations have been met.

This is where standards become an important part of providing a common viewpoint. Terminology creates a common understanding. Management system and life-cycle standards help organizations govern risk. Technical standards establish agreed-upon approaches to areas such as safety, security, data quality, and interoperability. Testing and conformity assessment can provide evidence that requirements have been met. Together, these mechanisms broaden the focus from what a technology can do to whether it can be trusted.

We can already see this standard’s foundation taking shape around AI. International standards for AI address terminology, management systems, risk management, and impact assessment, alongside requirements for audit and certification bodies. These horizontal foundations can connect with the particular needs of different sectors and applications, because trust in an AI system depends not only on the technology itself but also on where and how it’s used.

Similar foundations are beginning to emerge in quantum technologies. Standards in computing, communications, metrology, sources, and detectors are creating a shared language while the technologies themselves are still developing—laying the groundwork for reliable, secure, and interoperable markets.

Trust in one technology becomes inseparable from trust in the wider ecosystem around it.

Scaling across ecosystems

Trust in an individual technology is important, but it’s not sufficient for adoption at scale. Modern digital ecosystems depend on products, services, platforms, and organizations working together. An AI tool, for example, may rely on data from one provider, cloud infrastructure from another, and software from several others. It might also need to exchange information with systems beyond the ownership or control of its developer.

As those connections multiply, the complexity increases. Interconnected infrastructure, cross-border data flow, automation, and cloud-based services can make economies more productive and services more responsive. But they also make resilience, identity, data integrity, privacy, and supplier assurance concerns more complex as they cut across sectors. Trust in one technology becomes inseparable from trust in the wider ecosystem around it.

Interoperability is therefore essential. Without common approaches that enable technologies to work together, systems become fragmented, integration costs increase, and choice is reduced. Technologies that perform well in one environment may struggle to operate in other organizations, sectors, or markets. Standards help address this challenge by establishing agreed-upon approaches that enable products and services from different vendors to work together reliably and securely. In doing so, they reduce friction, support wider adoption, and help translate confidence in an individual technology into trust throughout the broader ecosystem.

Trust has to move, too

There is one final complication: None of this stands still. It’s constantly evolving. Cybersecurity has taught us this lesson repeatedly. Threats evolve. Technologies change. Incidents reveal risks that were difficult to anticipate. An approach that supports confidence today may need to evolve as new evidence, risks, and dependencies emerge.

Standards need to evolve alongside technology, informed by operational experience, incidents, scientific progress, and changing societal expectations. Trust isn’t something we establish at the point of adoption and then take for granted. It must be continually tested and renewed.

This is one reason I welcome the opportunity to contribute to discussions on emerging technologies and AI at the ISO Annual Meeting 2026. It’s a chance to explore what building and maintaining that trust means in practice. How do we enable innovation while managing new and evolving risks? How do we build trust across increasingly interconnected digital ecosystems? And how can international collaboration help technologies scale responsibly across borders?

No single actor can answer those questions alone. Sustained collaboration is required among industry, governments, academia, civil society, and standards organizations. The emerging technologies are only the beginning. What will ultimately determine whether innovation can scale is whether others have credible reasons to trust it.

From AI to quantum, trust will shape how far emerging technologies can go. Discover how standards can help innovation scale at the ISO Annual Meeting 2026.

Published Aug. 26, 2026, by ISO.