January 21, 2025 — Global management consulting partnership Kearney, and Futurum, a research, intelligence and advisory firm, today jointly announced the release of the 2025 CEO AI Management study.The study examined leadership’s stance and status of organizational AI adoption, implementation and roadmaps – revealing alarming backlash effects that CEOs may already experience.
The study is based on surveys and sit-down interview sessions with CEOs of companies with annual revenue over $1 billion around the world. The findings address CEO readiness in perception vs reality, CEOs’ perspectives on the potential of AI, the management dynamics over domain-level expertise and insightful conclusions on management of AI roadmaps and decision-making processes.
Heading into 2025, participating CEOs agreed to address currently debated issues like AI governance and ethical considerations, the advent of Agentic AI, change management and workforce dilemmas pertaining to AI adoption.
Unlike any previous technological innovation, AI sets cross-industry challenges that go beyond the technological customary integration journey. The study found that successful companies adopting AI found it necessary to launch significant change-management plans and initiatives. According to the study, companies point to building cross-functional alignment (62%) and adjusting workflows and processes (63%) as their leading hurdles when integrating AI into daily operations. Beyond cultural resistance, practical challenges like integrating with existing tech stacks (32%) or navigating IP and licensing complexities (24%) often delay or derail promising initiatives.
Additionally, distinct differences between older established companies and younger digital native companies were unearthed in the study. Digital native company CEOs often describe a fast-follower mindset—running lightweight pilots to validate AI solutions and investing heavily in analytics-savvy hires. By contrast, established organizations are more likely to emphasize “sustained viability” (45% vs. 27% for digital native firms) and stronger internal alignment (38% vs. 25%), reflecting a longer-term lens that includes TCO considerations and historical data migrations.
One of the most telling insights was the early shortcomings of AI implementation among companies, where the efforts were specifically spearheaded by the C suite. Data shows that 92% of CEOs not seeing tangible AI results insist on leading AI strategy themselves, compared to only 59% in organizations achieving measurable success. This gap suggests that centralized, top-down control can hamper domain-level expertise and hinder cross-functional collaboration—both critical enablers of sustainable AI. Interviews further reveal that when the CEO remains a strategic guide rather than a hands-on manager, resource allocation and ROI measurement (49% vs. 17% among unsuccessful peers) become more effectively embedded in everyday business practices.
In conclusion and based on the rich insights disclosed through the CEO responses and participation, Futurum and Kearney managed to develop a substantial CEO playbook for AI which features five points that lead to breakthroughs in AI outcomes.
Some key findings from the study:
- CEOs are overconfident and mask organizational under-confidence: Although 78% of CEOs strongly believe in their ability to guide AI, only 28% of mid-level managers share this optimism about their firm’s overall readiness. This mismatch points to a hidden cultural hurdle: Top-down enthusiasm for AI can overlook lingering anxieties about job security, skill gaps, or insufficient governance at the operational level.
- Established organizations see marathon, not sprint in AI: Compared to the digital native firms, established enterprises place heavier emphasis on improving customer satisfaction (76%) and shoring up supply chain resilience (42%) when deploying AI. In addition, 71% weigh total cost of ownership over mere upfront spend—underscoring a multi-year view of AI investments.
- The emerging vs established divide: Digital native company CEOs often described a fast-follower mindset—running lightweight pilots to validate AI solutions and investing heavily in analytics-savvy hires. By contrast, established organizations are more likely to emphasize “sustained viability” (45% vs. 27% for digital native firms) and stronger internal alignment (38% vs. 25%), reflecting a longer-term lens that includes TCO considerations and historical data migrations.
- AI “patch paradox”: only 19% of CEOs say they actively position AI for transformative growth rather than near-term gains. While most leaders see AI as a game-changer for operational efficiencies or cost reduction, few have fully mapped out how to leverage advanced capabilities for higher-impact use cases. That gap partially explains why industries like Energy, Manufacturing, and Technology report the highest rates of AI success—each nearing or exceeding 60%—as they integrate AI into complex workflows more readily such as predictive maintenance and automated quality checks.
- Soft demand by customers: Many CEOs report minimal direct pressure from customers to adopt AI—only 24% cite explicit client requests for AI-based solutions—yet over half acknowledge feeling a strong internal imperative to prepare for AI-driven disruption. Leaders stressed that waiting for external demands could leave their organizations behind the curve once consumer expectations shift, which they broadly expected them to do soon. Consequently, despite customer silence, 59% of firms say they are actively investing in “foundational” AI pilots to build up data readiness and upskill teams, aiming to be prepared when market pressures inevitably rise.
- Strategic alignment: A striking 64% of leaders without a formal AI roadmap report minimal returns from their initial pilots, underscoring the need to tightly anchor AI efforts to core business objectives.
- Data readiness and integration: Nearly two-thirds of CEOs cite disconnected or low-quality data as the main barrier preventing AI solutions from scaling beyond pilot phases, underscoring how critical robust data readiness is to any AI initiative. Siloed infrastructures, fragmented technology stacks, and inconsistent governance all limit AI’s capacity to deliver meaningful insights.
- Change management: Despite only 39% of high-performing AI adopters citing dedicated change management frameworks as a key success factor, leaders who invest in this area see smoother rollouts and stronger adoption.
- Effective AI governance: Only 22% of organizations with AI governance councils consistently track bias detection metrics, signaling that oversight is still evolving. While many firms rely on existing committees or compliance teams, formal governance frameworks—complete with cross-functional councils—prove more adept at curbing risks like bias, regulatory pitfalls, and ethical missteps.
- The Talent equation: Despite active recruitment efforts, 57% of surveyed companies still lack sufficient internal expertise to meet current AI needs.
“In one of the most expansive studies conducted with the CEO community, our survey manages to get a glimpse into the corporate boardrooms where decisions over AI’s role in the future of the organization are addressed.” Said Daniel Newman, CEO of Futurum. “My time spent speaking with CEOs during the in-depth interviews as well as year round, indicates the gravity in which the AI junction is dealt with. Today’s AI journey decisions make or break a CEO’s legacy in ways that don’t fall short of major leadership management decisions of growth, culture, operations, market impact, product development and ultimately, revenue” he added.
“These findings reveal a critical AI implementation gap as CEOs face mounting pressure to deliver results. Successful strategies require a fundamental shift in leadership approach, robust ROI measurement, and strategic patience – factors currently missing in most enterprise AI initiatives. With 95% of companies focused on quick wins rather than transformative opportunities, they run the risk of sacrificing long-term competitive advantage. This myopic approach, combined with fragmented data infrastructure and cultural resistance, is creating a widening gap between AI’s promise and actual business impact and value creation” said Ramyani Basu, Senior Partner and Global Lead for Data & AI at Kearney. “Our research highlights that CEOs who drive a measured, systematic approach to AI adoption with a tomorrow-back lens – focused on data readiness, workforce engagement, and measured rollouts – consistently outperform those pursuing aggressive, catch-up strategies.”
This study is based on a combination of quantitative survey data and qualitative insights from in-depth CEO interviews, offering a comprehensive view of how business leaders are navigating AI adoption. The dual approach ensures both breadth and depth, capturing overarching trends while delving into nuanced leadership perspectives that bring the data to life.
The survey component of this research included 213 CEOs from organizations with annual revenues exceeding $1 billion. Respondents were screened to ensure they held significant leadership roles, such as regional or global CEO positions, reflecting a strategic level of decision-making authority. The survey covered a wide range of industries, including financial services, manufacturing, retail, and healthcare, and was designed to explore AI adoption across various functional areas and challenges. Latin America, and other regions were included to provide a global perspective. The survey employed a structured questionnaire with Likert-scale, multiple-choice, and open-ended questions, generating statistically significant data across key themes.
Complementing the survey, 20 CEOs participated in in-depth interviews conducted during November and December 2024. These 30-minute interviews offered an opportunity to explore individual experiences, challenges, and strategies in greater detail. The interviews were structured around core themes such as AI governance, change management, integration challenges, and talent acquisition. Insights from these discussions were qualitatively analyzed to uncover emerging patterns, specific examples, and unique perspectives that enriched the broader survey findings.
By combining the quantitative scale of the survey with the qualitative richness of CEO interviews, this study provides a balanced and actionable analysis of how leaders are approaching AI. The methodology ensures that the results are both statistically robust and contextually grounded, offering valuable insights for organizations navigating AI’s complex and transformative landscape.
