Companies that prioritize responsible AI governance and ethical safeguards achieve measurably better financial returns than those treating AI as a quick competitive advantage. A new report from SAS, developed with research insights from IDC, reveals that organizations investing in trustworthy AI and strong governance see higher ROI, while even the most advanced organizations underperform without these foundational practices.

What Makes AI "Trustworthy" in the First Place?

Trustworthy AI is artificial intelligence technology designed, developed, and deployed with human well-being at its center. Rather than treating ethics and transparency as afterthoughts, leading organizations embed these principles from the start. The SAS report identifies six core principles that define trustworthy AI systems:

• Human-centricity: Promoting human well-being, human agency, and equity in how AI systems operate and make decisions.
• Accountability: Recognizing potential harms before they happen and acting proactively to prevent unintended consequences.
• Transparency: Openly communicating and providing documentation of an AI system's intended uses, risks, and how its decisions are made and monitored.
• Inclusivity: Ensuring that AI reflects diverse populations and that it works fairly for everyone, not just majority groups.
• Robustness: Operating reliably and safely while managing risks in real-world conditions.
• Privacy and security: Protecting the use and application of an individual's data throughout the AI lifecycle.

The report measures trustworthiness across five specific dimensions: data quality and governance, model governance and oversight, explainability and fairness, responsible AI policy, and audit and accountability. Organizations that score high across all five categories are the ones seeing tangible business benefits.

Why Do Companies Resist Building Trustworthy AI Systems?

Many organizations view the framework required to build trustworthy AI as a constraint on innovation. Some leaders worry that time spent on bias testing, fairness checks, and governance frameworks will slow them down and allow competitors to move faster. However, this thinking misses a critical insight: incorporating the necessary safeguards is what determines whether AI actually scales and delivers long-term value in high-stakes situations.

Without explainability, governance, and ethics, confidence in AI systems is misplaced, and risks multiply. When AI systems are transparent and aligned to business goals, leaders and others across their organization gain the confidence to deploy them broadly. Trust then becomes a strategic advantage that accelerates adoption, enables innovation, and turns AI into a reliable driver of impact.

"We have to preserve human judgment if we want to preserve human culture," said Reggie Townsend.

Reggie Townsend, Vice President of AI Ethics, Governance and Social Impact at SAS

How to Build Trustworthy AI Across Your Organization

Leaders who want to move beyond AI ambition and achieve real ROI should embrace three critical tenets that separate mature AI organizations from those merely experimenting with the technology:

• Leadership responsibility: Trustworthy AI starts at the top. Governance, accountability, and ethical guardrails are not just technical concerns; they should reflect leadership's choices about how AI is designed, deployed, and overseen across the entire organization.
• Human oversight at scale: As AI capabilities become more sophisticated and autonomous, it is more important than ever for humans to be meaningfully involved. Organizations that treat AI as a replacement for human judgment amplify risk, because AI does not eliminate failure. Transparency into how models work, clarity on when human judgment is required, and monitoring of outcomes over time are essential guardrails.
• Customer trust as brand protection: When AI influences customer interactions, messages, and personalization, trust becomes inseparable from brand credibility. Organizations must protect customer data as they would any other valued asset and ensure AI-driven customer experiences are fair, explainable, and respectful.

Real-world examples illustrate this approach. Georgia-Pacific scaled digital twins in manufacturing, and health care organizations simulated environments, but neither replaced people. Instead, they brought humans into testing, simulation, and decision-making earlier, before anything happened in production.

"Trust is loyalty's currency in the age of AI," stated Jenn Chase.

Jenn Chase, Executive Vice President and Chief Marketing Officer at SAS

What ROI Actually Looks Like for Mature AI Organizations?

The companies generating tangible ROI from AI are not chasing quick wins; they are thinking bigger and more strategically. While saving money is often a top goal for AI initiatives, it delivers lower ROI than other AI goals. Mature AI organizations, defined as those that have used AI for eight or more years or longer, prioritize process efficiency and decision-making, with cost savings ranking much lower on their list.

Organizations with more strategic AI initiatives significantly expand market share and improve customer experience. This shift in priorities reflects a deeper understanding: AI's potential, when used intentionally, strategically, and scaled across the organization, is enormous. Using AI in isolated ways leaves ROI on the table. The difference between organizations that treat trustworthy AI as a repeatable operating model and those that do not is stark. Trustworthy AI becomes a long-term business advantage that separates those who use AI effectively from those who merely experiment with the technology.

Unfortunately, few organizations today have a centralized team overseeing AI governance or establishing ethics frameworks, fairness checks, data quality monitoring, and bias detection to ensure AI is implemented responsibly. Many companies are deploying AI without the safeguards to make it truly trustworthy. Instead of leaving AI governance to chance, fragmented across teams and systems, it should be a coordinated effort spanning multiple functions with clear accountability, ownership, and structured approvals.