Unlocking AI’s Trust Dividend

It’s no surprise that artificial intelligence (AI) will dominate discussions in boardrooms and C-suites this year. As organizations’ AI use increases and expands, “it’s imperative for boards to stay current on the related opportunities and risks, including how GenAI and AI agents are being deployed, and how the company is managing and mitigating the risks,” according to KPMG’s analysis of 2026 board priorities.  

Investors and other stakeholders are pressuring senior leadership teams to demonstrate higher returns on AI investments. While board members expect senior executives to have strong AI governance guardrails in place, they also appear more willing to accept risks associated with AI adoption compared to previous waves of technology adoption (such as cloud computing). In some cases, swift deployments of AI designed to gain a competitive advantage are being prioritized over waiting for comprehensive AI security frameworks to be finalized.

Changing board risk tolerance and AI’s distinct risks and opportunities are driving the emergence of a new trust model between AI vendors and customers. More vendors and customers are sharing responsibility for reviewing and validating AI outputs. While AI rules, regulations (such as the EU AI Act and U.S. Executive Order 14365), and standards from bodies like the International Organization for Standardization and the National Institute of Standards and Technology continue to evolve, AI’s learning nature adds new complexity to organizational governance. AI is not a static system, so AI governance and risk management capabilities must also evolve. This makes the trust compact between AI vendors and customers even more important.

The AI vendors that tend to get the most attention these days are the companies that create AI models. However, a much larger collection of software vendors and other technology companies are, including Vertex, applying AI models in domains where they have amassed decades of expertise (indirect tax). Companies that leverage their domain expertise and deep understanding of customer needs when applying AI models will offer much more value than vendors that merely affix AI features to existing solutions without thoughtful integration into workflows.

One way to distinguish between these two types of “AI appliers” is by looking at the user experience across a vendor’s platform: the degree to which a vendor can apply AI to help the user through the processes that the software is intended for. When these tools deeply understand the domain and apply that knowledge across the platform, users are more productive, find the application intuitive, and stay focused on process outcomes. Using AI within the flow of work helps create trust as users know what to expect and can easily validate that it is helping them achieve the outcomes. This application of AI to the user experience fosters trust, and when paired with access to high-quality data, serves as the foundation for the high-ROI AI outcomes that investors and boards are searching for.