Recent discussions with board members and senior leaders around the world indicate practices that can make AI adoption more successful and increase ROI. The following eight actions are based on conversations with hundreds of board members and senior leaders during the regular course of business. These practices identified by AI adopters should not be taken as empirical or research-based. While many are aligned with published studies, many are new or different.
01
While it may seem obvious to some leaders, many board members and senior executives stress the importance of connecting AI adoption to business strategy and related priorities. Experts differentiate automation (replacing human tasks with technology), augmentation (improving human outcomes through technology) and addition (finding new sources of value).
Many early AI adopters report strong ROI often comes from a combination of cost reduction through automation, increased quality through augmentation and competitive differentiation through addition.
Reasons for AI adoption vary widely. For example, cost focus can relate to reduced spending, greater efficiency, greater speed or greater scalability. Value creation can relate to higher quality, greater accuracy, more consistency, less variance, more thoroughness, more intelligence or being more data-driven. Differentiation can relate to more innovation, superior user experience or being more sustainable.
Business leaders suggest that while virtually all AI adoption today involves a focus on cost and efficiency, the most effective implementations also include elements of augmentation and new sources of value tied to company strategy.
02
Contrary to some “legend stories,” many board members and senior business leaders report that their most successful early AI adoptions occurred where their organizations and teams enjoyed strong domain expertise. In cases where AI was used to expand domain expertise or enter new areas, adoption success was more mixed (leaders report learning a new technology and a new domain simultaneously is difficult).
Leaders also report that current AI learning models depend on users to provide the required domain expertise to operate effectively, acknowledging this could change as AI becomes more advanced and companies become more experienced.
03
Many board members and senior leaders report AI adoption generally has been more successful when the organization already has or develops the necessary AI skills to implement and then derive value from AI. They stress the importance of partnering with third parties when it is not practical to build skills inside their own organizations (or such skills are needed only on a short-term basis) and that working with outside experts can be highly valuable and effective. However, they note that over-dependency on outside skills can reduce long-term success.
Several leaders shared that outside experts tended to be more optimistic about AI’s potential impact and capabilities in their organizations (as well as ROI), while internal experts were more cautious as they would be held accountable for outcomes. Many leaders believe that having key technology skills and talent inside their organizations is a long-term source of competitive advantage.
04
According to many board members and senior leaders, AI tools and agents perform well on specific tasks, not broad processes. They know the agentic web may change this, but the most successful implementations start with point solutions that then can be woven together to form an effective, broader process once technology, skills and learning meaningfully advance.
While top-down strategy and process development remain critical, a “top-down meets bottom-up” approach to solution implementation has proven effective to many leaders. Leaders report that taking on too much, getting distracted or forming grandiose plans erodes ROI. They favor a focused approach to drive ROI.
05
While focus is key, many board members and senior leaders report that scalability is vital to successful AI implementations. They suggest that solving the most important and scalable problems drives implementation success and ROI.
The ideal AI point solutions can be scaled and address high-priority, high-impact problems. While such combinations may be rare, organizations that identify and execute against them report materially higher ROI than those that do not.
06
Many board members and senior leaders suggest that the humans-versus-AI debate can be well-intended but misdirected. They report that the most successful implementations reimagine processes and take a “best of” approach, where they consider which processes are best served by AI, humans or, most frequently, both.
In general, early implementations suggest generative AI’s strengths include automation of routine processes; pattern recognition at scale, speed and consistency; data analytics and prediction; complex calculations; and simulated reasoning within boundaries. Human strengths include contextual awareness and application, creativity outside of rules, emotional intelligence, ethical and values-based decision making, flexibility in unpredictable or new circumstances and physical skills. Effective processes draw from both sets of strengths and include new forms of human and AI partnership.
07
Many board members and senior leaders suggest an underappreciated element of successful AI adoption is having the right data, which can be as important as having the right skills. They report that data often is perceived to be a commodity, secondary to technology development, but currently the opposite is true. Data availability, ownership rights, regulatory and governance rules and cyber/data security remain challenges.
Many leaders believe that without access to significant quantities of accurate, secure, ingestible and analyzable data, AI adoption could slow. Even the most sophisticated AI tools and agents cannot perform as intended without the right data.
08
Many board members and senior leaders report that five dimensions of an organization’s culture are essential to effective AI adoption:
Despite variations in why and how businesses approach AI, leaders experience an ability to achieve higher ROI during AI adoption based on a set of practices which likely will continue to evolve as AI usage continues to mature.
A version of this article originally appeared on Forbes on December 11, 2025.
