AI is accelerating across every sector, from health care diagnostics to customer service chatbots. But with every new use case, a question lingers just below the surface: should we be trusting systems that we barely understand?
Too often, trust is framed as a question of compliance: Will users accept this? Will regulators approve that? However, those are all surface-level concerns. The real question should be: How do we build systems where trust is embedded from the ground up?
Trust is the new experience. It cannot be retrofitted into AI after deployment. Instead, it functions like infrastructure – shaping how technology interacts with people, organizations and society at scale.
So how might we be accountable to the impact of emerging technology in the age of “programmable trust”?
Trust as infrastructure
Thinking about trust as infrastructure shifts the conversation away from isolated features and to systemwide design. Like a power grid or transport network, AI requires standards, governance and ongoing maintenance to operate reliably at scale.
Trust cannot be added on after the fact. It would be akin to reinforcing a bridge after it has already been opened to traffic. Foundational decisions, such as data sourcing, workflows and user interfaces will have already begun to shape how people interact with the system.
You therefore need to be designing trust from the ground up. A strategic, service design approach maps every interaction between people, processes and technology. This approach not only identifies where confidence is built, but also where it is prone to fracture.
Trust is therefore embedded into the architecture of AI systems across customer journeys, internal workflows and broader service ecosystems. This is where trust becomes a force multiplier: enabling AI to reinforce relationships, protect privacy and scale responsibly.
Take enterprise-grade recommendation engines. Designed without guardrails, they amplify bias. Designed with transparency and feedback loops, they build credibility with users and regulators alike. Embedding trust at the system level is not a one-off effort. It is a strategic investment in resilience, scalability and a long-term reputation builder.
Undervalued power of human skills
AI may be capable of processing vast amounts of data, but its effectiveness ultimately hinges on human intelligence. That’s why trust in AI is not solely a technical challenge. It depends on the (often undervalued) cultural, ethical and interpersonal skills of the people designing, deploying and interacting with them. Without these human capabilities, even the most sophisticated AI can fail to gain adoption or deliver meaningful outcomes.
Teams that apply cultural intelligence and ethical reasoning design AI that works across diverse markets and anticipates risks early. Product teams can test whether recommendation systems treat all customers fairly, while operations teams build workflows that keep AI aligned with human values. Together, these skills turn technical systems into experiences people trust and want to engage with.
By investing in people alongside the technology, organizations can build AI systems that are resilient, accountable and adaptable, while also generating competitive advantage through authentic engagement and more intelligent, human-centered outcomes.
Authentic intelligence
as a competitive advantage
In the AI arms race, speed and sophistication are no longer enough. What sets leaders apart is their ability to cultivate relational intelligence: the ability to build systems that people trust and want to engage with.
Consider customer engagement: two companies might deploy similar AI chat systems. However, one hides it behind jargon and opaque decision-making. The other discloses when AI is in use, explains logic in plain language, and provides users with meaningful feedback channels. Which one earns long-term loyalty?
Or take marketing automation: AI can personalize at scale. When AI is grounded in authentic intelligence, values, transparency and consent, it transforms brand interactions into relationship-building moments.
In a marketplace where mistrust spreads quickly, that relational strength can be a real competitive advantage.
How can leaders build trust into AI?
AI requires more than technical capability. It depends on authentic intelligence, which involves the integration of human values, cultural understanding and shared accountability into system design.
This transforms trust from an abstract principle into a tangible, organizational advantage – the difference between technology that feels experimental and technology that people embrace, scale and, ultimately, rely on.