The Most Important Role in the Age of AI? Chief Trust Officer

Executives are scrambling to make sense of the generative AI wave. Companies are overwhelmed by tools and under pressure to show results that simply haven’t materialized at scale. McKinsey’s State of AI report confirmed what many executives already know: despite widespread adoption, fewer than a quarter of companies are seeing a measurable bottom-line impact from AI.

However, beneath the surface of missed KPIs and stalled pilots is a deeper, more structural challenge: trust. Customers, employees, and partners are watching organizations automate at speed and wondering: Am I speaking to a human or a machine? Do I know how my data is being used? Is this video even real, or is it AI-generated? Ambiguous answers can ultimately take a toll on a business’s bottom line. That’s why trust, not efficiency, will ultimately determine whether AI advances or erodes business value.

To address this rising unease, some organizations are testing a new leadership role: the Chief Trust Officer (CTrO). But unless the role carries real authority, there’s a risk it becomes little more than a symbolic gesture. For a CTrO to make an impact, the position must go beyond title and optics to tackle algorithm ethics, data practices, and – most importantly – the everyday confidence of customers and employees.

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The emerging mandate of the Chief Trust Officer

The CTrO is more than a compliance function or a branding gesture. It’s a recognition that AI has blurred the lines between technology, ethics, and governance. The role’s mandate spans both internal systems and external impacts, covering every domain where AI relates to data, decisions, and human outcomes.

This starts with defining AI uses and ensuring transparency. The first duty of a CTrO is thus straightforward but often overlooked: distinguishing between transactional and relational AI. Many executives I’ve spoken to haven’t even considered the difference.

Transactional AI refers to systems that automate structured, repeatable tasks in which rules and outcomes are well defined. It offers the clearest path to efficiency gains: automating invoices, optimizing supply chains, or handling routine customer service inquiries. Because these interactions don’t depend on personal connection, machines can deliver speed and accuracy without undermining trust. In fact, by stripping away low-value tasks, transactional AI can strengthen trust internally by giving employees more time for meaningful work.

Relational AI refers to tools that operate in contexts in which trust, empathy, and authenticity are central to the interaction. These include healthcare conversations, employee feedback, conflict resolution, or sales relationships. In these settings, AI can be valuable, but only in support of human judgment and empathy. For example, in sales, AI might recommend which prospects to prioritize, but it’s the salesperson who earns trust and closes the deal. In HR, AI might surface patterns in employee feedback, but a manager must have the conversation that builds credibility with staff. Misusing AI in these relational contexts risks eroding trust rather than building it.

Research confirms that people respond differently to AI depending on whether the context is transactional or relational. A recent study in JAMA found that patients grew uneasy when AI chatbots delivered sensitive health information without disclosing that the response was machine-generated. Yet the same patients had no issue with AI scheduling appointments or processing insurance claims, tasks they perceived as purely transactional.

Too often, companies overlook this distinction, treating every use case as transactional and assuming efficiency depends on minimizing human involvement. That approach may generate short-term speed, but it undermines the very trust that determines whether AI succeeds in the long run.

Ensuring Responsible AI Systems

For a Chief Trust Officer, defining and overseeing responsible AI isn’t a side function—it’s the core of the mandate. Building public and internal confidence in AI depends on proving that systems are accurate, fair, private, and safe. That requires rigorous governance across four critical areas:

1. Preventing AI hallucination and misinformation
   Generative models are powerful but fallible. When systems invent facts or generate misleading content, they erode confidence quickly. A Chief Trust Officer ensures that every deployed model has undergone stress testing to evaluate factual consistency, context awareness, and output reliability. This includes instituting pre-launch evaluation frameworks, ongoing performance monitoring, and rapid response protocols for any hallucination-related incidents.
2. Protecting data integrity and privacy
   Trust begins with data. The CTrO enforces strict policies to ensure that training data is accurate, ethically sourced, and protected throughout its lifecycle. That includes verifying data lineage, anonymizing sensitive information, and aligning data practices with evolving global privacy regulations. Effective data integrity frameworks prevent bias from entering at the source and ensure every model decision is traceable and defensible.
3. Eliminating discriminatory outcomes
   Unchecked algorithms can encode and amplify bias. The CTrO establishes audit pipelines and fairness testing to detect disparities in model outputs—whether in hiring recommendations, credit approvals, or predictive analytics. Mitigation strategies, such as model retraining or rebalancing datasets, must be continuous, not reactive. By embedding bias detection into development cycles, the CTrO turns fairness into an engineering standard rather than a legal safeguard.
4. Testing and validating externally deployed AI systems
   Before any AI product or capability reaches the market, the Chief Trust Officer ensures it passes through rigorous safety and compliance checks. These may include simulated real-world testing, red teaming to expose vulnerabilities, and explainability assessments to confirm decisions can be understood by end users and regulators alike. Post-deployment, the CTrO oversees monitoring to ensure performance doesn’t drift and that models remain aligned with ethical and operational expectations.

Collectively, these responsibilities form the backbone of organizational AI trust. They move the CTrO beyond communication and ethics into the realm of operational assurance, ensuring that every AI system deployed is not just innovative, but responsible by design.

A strategic advantage

The temptation with AI right now is to chase efficiency at all costs. But efficiency alone doesn’t build confidence in AI. Trust grows when companies show they’re using automation responsibly, by automating transactional tasks while ensuring that relational interactions remain human-centered. The real opportunity is to combine transactional gains with stronger human connection, signaling to employees, customers, and partners that AI is there to support them, not replace them. That balance is what builds the confidence needed for long-term adoption.

I’ve seen firsthand that efficiency and empathy are not opposites. Used well, technology can create space for more authentic human relationships. But trust is the hinge: without it, efficiency looks like cost-cutting; with it, efficiency becomes an investment in people. In the age of AI, that trust is the most valuable currency leaders have.

About The Author Of This Article

Victor Cho is CEO at Emovid

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