The AI ethics trust engine

For years, enterprise leaders have debated the business case for AI ethics. New research from the IBM Institute for Business Value (IBM IBV) helps resolve this debate.

In a survey of 915 global executives, we found that organizations investing more in AI ethics consistently achieve higher operating profit, stronger return on investment (ROI), and measurable competitive advantage from AI.

This research is part of a three-year initiative sponsored by the IBM AI Ethics Board in collaboration with the Notre Dame–IBM Tech Ethics Lab and the IBM IBV. It builds on our findings in a previous report, Why invest in AI ethics and governance? Organizations taking a holistic approach to AI ethics—combining practical business discipline with genuine ethical commitment—are, in fact, asking a different question. It’s not just “should we invest in AI ethics?” It's “how do we integrate ethical practices into AI value creation?” Organizations embedding trust can capture more business value from their AI investments—including direct financial benefits, a positive brand and culture, and new capabilities that deliver long-term advantages.

Organizations see measurable business results when AI ethics becomes integral to value creation.

Yet many companies remain trapped between good intentions and lagging execution, preventing them from capturing ROI. Only about one-third of the executives in this recent study say their organizations are applying a set of core AI ethics-specific tools today. And more than half cite trust, bias, and explainability as constraints to AI adoption. Leaders cannot afford inaction—especially where business cases are strong. The promise of agentic AI—which also comes with greater autonomy and complexity—only amplifies the urgency to address these challenges strategically.
 

This report explores key questions every leader must answer to realize the value of trustworthy AI.

What is the business case for AI ethics investments?
Investing in AI ethics directly correlates with superior business performance. Organizations in the top quartile of AI ethics spending, as a percent of AI spend, demonstrate 30% higher operating profit attributable to AI than the lowest quartile over the past two years. Investments across the other three spending quartiles correlate with increasing operating profit as well.

The trend applies to other measures of financial value from AI, including ROI. Executives also report that AI ethics investments deliver advantages beyond financial value. The top three benefits cited: increased trust (61%), strengthened brand reputations (57%), and mitigated reputational risks (54%). These benefits, in turn, translate into performance gains.
 

Executives report AI ethics has improved key performance metrics over the last 12 months.

What is limiting AI’s value?
Our research confirms that persistent trust issues are stalling AI adoption. More than half of executives cite ethics-oriented concerns as key barriers and say building trust is a significant internal challenge. AI that is not trusted does not get used, undermining even the most robust business case.

Over half of enterprises understand that AI ethics issues matter and impact the business, but only 41% have established approaches for integrating AI ethics into their AI strategy, and only 49% report fully transparent AI decision-making processes. 

The connection between AI ethics investment and business performance runs through a critical pathway: trust.

 
Diving deeper, implementation of AI ethics best practices is limited. While basic visible frameworks and some foundational tools may be in place, most organizations lack advanced operational capabilities such as impact assessments and ambassadors embedded throughout the business. The path forward requires treating principles as starting points, not destinations—immediately pairing each ethical commitment with specific operational mechanisms that make it actionable.

How should organizations operationalize AI ethics for optimal impact?
AI governance operates on two distinct levels: within executive leadership and across operational roles. To be effective, AI ethics needs to bridge that boundary. Yet too often, efforts are siloed. Our research shows that primary accountability for AI ethics is concentrated across leadership functions, with no single entity dominating. But the real power to shape AI ethics outcomes is distributed across multiple operational teams.

53% of organizations find their AI ethics governing bodies ineffective, perhaps because they're trying to govern a cross-functional capability through single-function accountability models. This approach can result in ethics guidance that is detached from operational reality—creating friction rather than value. Conversely, when board-level oversight combines with cross-functional collaboration, ethical considerations become embedded in AI decisions rather than applied as external constraints. The organizations that will win with trustworthy AI know that collaboration transforms ethical intentions into business results.

Download the full report to explore the findings in more detail. An action guide—organized by four primary motivations for AI ethics investments—suggests how organizations can leverage their strengths and move toward a more holistic approach that creates a strong business case for AI ethics, while benefiting society as a whole.