AI has reached the point where potential isn’t enough and proof is everything. Protiviti’s Jim DeLoach argues that the organizations turning AI into enterprise value are the ones building real discipline around it — ethical governance, formalized risk oversight and boards that treat AI as a standing strategic priority rather than a technology issue — and he lays out the questions directors and executives should be asking now.
AI is at an inflection point — a moment when significant, often sudden, change occurs in a business or industry. The technology is transitioning from theoretical potential to widespread adoption with profound economic, societal and geopolitical impacts. Organizations are scaling AI deployments beyond pilots, achieving measurable ROI in areas like healthcare, finance and logistics, while generative AI tools are revolutionizing content creation and customer service.
The focus has shifted from innovation to monetization, with businesses and governments investing heavily in AI infrastructure and sovereign capabilities. At the same time, AI is expanding into the physical world through robotics and autonomous vehicles, reshaping industries like transportation and manufacturing. Regulatory frameworks, such as the EU AI Act, reflect growing recognition of AI’s transformative power and the need for ethical governance. As scrutiny around fairness, transparency and performance intensifies, this inflection point underscores AI’s role as a game-changer, offering immense opportunities while posing significant challenges for organizations and societies alike.
But companies are at different stages of the AI journey. Recent Protiviti research, conducted in collaboration with BoardProspects, offers perspectives from 772 board members and C-suite leaders worldwide about their organization’s AI maturity and ROI progress. The message was clear: As AI advances and delivers measurable ROI, directors and executives need to address integration, governance, misinformation risks and deployment challenges. Furthermore, AI should be a regular board agenda item.
The survey asked leaders to place their companies on a five-stage curve — from an initial stage of recognizing AI’s potential without a strategy, through experimentation, integration and optimization, to full transformation, where AI reshapes the business and its industry.
What we found is that the companies reporting the strongest returns are the ones that built the discipline to earn them: 95% of the most confident organizations say they’re seeing significant return on investment (ROI), compared with just a third of those still struggling to show value. The same gap runs through governance — 93% of high-ROI organizations trust that they’re deploying AI ethically vs. 42% of their low-ROI peers — and into the boardroom itself, where 63% of high-performing boards treat AI as a standing agenda item, against only 13% at the low end.
Action items for executive management and boards
Overall, our report indicates that company leaders and their boards must do more to become better-educated about AI and position themselves and their organizations to address their AI strategy and challenges and move swiftly, given stakeholder demands to generate sufficient measurable returns from AI initiatives.
Following are action items for directors and executives to consider when engaging strategic conversations in the boardroom and C-suite and improving the AI oversight process.
Engaging in strategic conversations in the c-suite and boardroom
Understand how “successful integration” of AI is defined and measured. Constructively engage and, if necessary, challenge management on where AI is being deployed, how outcomes are measured and whether AI initiatives are delivering consistent, scalable value rather than one-off gains. Consider such metrics and indicators as percentage of AI use cases deployed at scale in core operations and ROI performance of AI initiatives compared to business cases.
Emphasize ethical and responsible AI as an enterprise governance priority. Incorporate defined accountability, risk oversight and integration into the organization’s broader governance framework. Consider such metrics and indicators as existence and maturity of an ethical AI governance framework, management’s confidence in responsible AI deployment and the frequency and severity of AI-related risk issues escalated to senior executives and the board.
For organizations early in their AI journey, encourage a shift in focus. Move beyond process efficiencies, cost savings and productivity gains to a more transformative emphasis, e.g., improvements in customer experiences, products and services that drive revenue growth and market share. Consider such metrics and indicators as distribution of AI investments across efficiency vs. strategic growth use cases; AI initiatives directly tied to customer experience, revenue or market expansion; and management articulation of AI’s role in go-to-market and product strategies.
Evaluate whether the nature, extent and timing of the board’s oversight of management’s AI governance framework is fit-for-purpose. In this assessment, the board should consider the scope and scale of the company’s AI deployments. Consider such metrics and indicators as evidence that the governance framework is functioning effectively and is periodically recalibrated as the organization becomes more AI-mature.
Ensure that AI risk governance has been formalized and integrated into the enterprise risk management (ERM) process. Ensure regular executive team and board-level visibility as AI initiatives scale. Consider such metrics and indicators as a percentage of major AI initiatives subject to documented risk and governance review and the nature of AI-related risks incorporated into the ERM process.
Evaluate the organization’s end-to-end AI roadmap that links AI aspirations to enabling, ongoing investments in technology infrastructure and workforce capabilities. Consider such metrics and indicators as progress against the approved roadmap (with emphasis on the technology modernization and capability enhancement milestones); investments in AI training, upskilling and talent linked to management’s objectives; and percentage of AI initiatives delayed due to infrastructure or skills constraints.
Improving the rigor of the AI oversight process
Make AI a standing board and executive team agenda item linked explicitly to enterprise strategy, value creation, innovation priorities and competitive positioning. Consider such metrics and indicators as the frequency with which AI appears as a standing agenda item at full board or designated board committee meetings and management’s ability to articulate how AI initiatives support strategic objectives, innovation priorities and competitive positioning through a consistent reporting cadence.
Calibrate AI oversight priorities with the organization’s positioning on the AI maturity and ROI continuums, adjusting discussion topics as these factors evolve. As noted in our survey, high-ROI organizations focus on integrating AI into strategy, innovation and competitive position; low-ROI organizations concentrate on identifying opportunities and use cases and establishing governance frameworks, likely because foundational elements are not yet in place. Consider such metrics and indicators as management’s clarity in positioning the organization on the AI maturity and ROI continuums and how executive team and board discussion time is allocated across AI strategy and competitive positioning, AI implementation progress and measures of success, and governance frameworks and foundational capabilities.
Evaluate the executive accountability framework for AI transformation and the board-management engagement model and, if necessary, take steps to strengthen them. Consider such metrics and indicators as clarity and consistency of executive ownership for AI outcomes, as reported to the board, and alignment of the board oversight model used (full board vs. distributed committee model) with AI’s strategic importance to the organization.
It’s “Show me the money” time
AI’s path has arrived at a fork in the road, a time at which the “show me the money” era of AI has eclipsed the technology’s experimentation phase. It is time to move beyond trial-and-error mode into practical, revenue-generating applications of AI in the enterprise. The focus has shifted from potential to proof, with organizations and governments alike prioritizing scalable deployment, ethical governance and demonstrable value creation. This moment is characterized by both immense opportunities and challenges as AI reshapes the future of work, innovation and global competition.
If there is a single takeaway from our global survey, it is that board and executive team AI oversight is dynamic and evolves. As the organization’s AI maturity evolves, board and management discussions should shift noticeably beyond foundational questions, with fewer repeated debates about what AI is to more focus on scaling, optimization and strategic impact. AI-related risk discussions should be integrated with assessments of AI value-creation opportunities, rather than treated as an afterthought, shifting from ad hoc and reactive updates to more forward-looking strategic dialogues.
As boards and management teams focus more frequently on AI ethics, transparency and trust, their confidence in the organization’s AI strategy and its connection to long-term value increases, especially when AI is viewed as a business priority, not as a mere technology issue. Advanced maturity is supported by clearly defined AI oversight roles across the board, while regular briefings, independent learning and continuous education help both board members and executive team members build AI fluency.
Jim DeLoach, a founding 
