A new wave of AI readiness concerns is reshaping how executives view technology leadership. A recent Gartner study reveals a striking paradox: while a clear majority of CEOs acknowledge AI’s pervasive impact, many remain uncertain about the AI capabilities of their own CIOs. This tension illuminates a broader leadership and capability gap at the highest levels of corporate governance, underscoring the urgent need for comprehensive upskilling and a strategic rethink about how organizations adopt and scale AI across functions. The study surveyed hundreds of senior leaders worldwide and points to a pivotal moment where AI is not just a tool but a fundamental driver of business strategy and organizational design. The takeaway is unmistakable: recognizing AI’s potential is not enough; turning that potential into measurable, sustainable outcomes requires deliberate, organization-wide learning, governance, and capability-building.
Understanding the Gartner findings: AI’s impact vs. leadership readiness
Gartner’s research involved 456 CEOs and other senior executives operating across diverse industries and geographies. The results underscore a broad recognition of AI’s business significance. A substantial 77% of CEOs said AI is influencing their strategic direction, signaling a widespread consensus that AI is more than a peripheral capability. Yet, despite this awareness, only 44% of these leaders expressed confidence in the AI expertise of their CIOs. This discrepancy highlights a critical leadership gap: it is not merely about whether AI tools exist, but whether the leadership team possesses the skills and confidence to deploy AI in ways that meaningfully alter operating models, customer experiences, and competitive dynamics.
The core issue extends beyond CIO competence. The survey also points to a broader perception among executives that AI-readiness is uneven across the C-suite. Many leaders believe that the entire executive team—CEOs, CFOs, COOs, CHROs, CIOs, and other senior roles—needs a more cohesive, deliberate AI strategy. The implication is that individual talents in silos are less effective than a cross-functional, governance-driven approach to AI adoption. In other words, the problem is less about a single chief technology officer and more about an integrated leadership capability that can translate AI potential into enterprise-wide value.
A striking theme in the Gartner analysis is the sense among CEOs that their current business models are not fully prepared to harness AI’s capabilities. A two-thirds majority of surveyed leaders indicated concern about whether their models can accommodate AI-driven changes. This sentiment reflects a deeper shift in how organizations think about value creation, risk, and execution. AI is no longer a neat add-on for automation or data processing; it is being seen as a transformative disruptor that reimagines processes, decision-making, and the very architecture of value chains. The consequence is a call to action: CEOs must lead with a renewed sense of urgency to embed AI into core strategy, not merely as a technology initiative.
Jennifer Carter, Principal Analyst at Gartner, frames the transition as a movement from viewing AI as a tool to viewing AI as a fundamental way of working. This perspective emphasizes the need for durable structural changes within organizations, not temporary upskilling spurts. As Carter notes, the shift requires more than new hires; it demands a deliberate, ongoing effort to elevate the capabilities of existing employees so they can integrate AI into daily tasks and long-range planning. The practical upshot is that AI readiness becomes an organizational discipline—embedded in roadmaps, governance mechanisms, and performance metrics rather than treated as a stand-alone program.
The Gartner findings also reflect a historical pattern: executives have long voiced dissatisfaction with the digital proficiency of their leadership teams, a concern that predates the current AI surge. The new AI era intensifies this dissatisfaction by raising expectations about speed, scale, and impact. As AI becomes more pervasive across sectors, the pressure to close these gaps intensifies. The study thus not only identifies a current shortfall but also signals a persistent trend that requires sustained attention from boards and executive leadership. The demand signal is clear: leadership must be at the forefront of AI strategy, with clear accountability for measurable outcomes.
Why leadership readiness matters: transforming governance and accountability
The implications of AI readiness go beyond technical proficiency. Gartner’s analysis suggests that the real value of AI will be realized only when leadership aligns around a shared vision for AI-powered transformation. This requires new governance structures, decision rights, and performance metrics that explicitly account for AI-driven outcomes. In practice, this means elevating the role of the CIO from a purely technical steward to a strategic partner who can translate AI opportunities into business cases with clear ROI, risk profiles, and implementation roadmaps. It also means empowering other senior executives to participate in AI strategy, ensuring that AI considerations are integrated into finance, operations, marketing, talent, and customer experience at every level.
A transformed leadership model for AI entails assembling cross-functional teams that can move quickly from pilot to scale, with a unified plan for data, privacy, ethics, and risk management. It requires a clear view of what success looks like when AI is embedded in core processes, including defined metrics for efficiency, revenue impact, and customer satisfaction. The Gartner study implies that without such governance, AI initiatives risk becoming disjointed pilot projects rather than capable drivers of strategic change. Therefore, boards must oversee a structured AI agenda that links technology investments to strategic objectives, with responsible ownership, transparent reporting, and accountability for results.
Another dimension concerns talent development. If two-thirds of leaders doubt their business models are ready for AI, organizations must build internal capabilities rather than rely solely on external hires. Upskilling—systematic, targeted, and ongoing training—emerges as a central pillar of AI strategy. Upskilling is not a one-off program; it is a continuous capability-building journey that must be woven into talent development, performance management, and career progression. Gartner’s findings illuminate a critical truth: the success of AI initiatives hinges on people as much as on platforms, tools, and data.
The AI upskilling imperative: evidence from global workforce studies
The need for upskilling is echoed by other authoritative sources. The World Economic Forum’s Future of Jobs report underscores that a majority of organizations view reskilling as essential to navigating AI-driven changes. This aligns with Gartner’s perspective that leadership and workforce readiness must evolve together. In practical terms, this means creating structured programs that help employees reinterpret their roles in light of AI-enabled capabilities, while also providing pathways for career growth within AI-enabled functions.
Gartner further highlights a specific forecast related to software engineering: by 2027, around 80% of software engineers are expected to require upskilling due to the integration of generative AI into software development workflows. This projection points to a broader trend: AI is auto-scaling certain tasks while creating demand for new competencies at the intersection of coding, data science, and human-centered design. The implication for organizations is clear—engineering teams must be equipped not only with new technical skills but also with an understanding of how AI can augment creativity, problem-solving, and collaboration with non-technical stakeholders.
Philip Walsh, Director Analyst in Gartner’s Software Engineering Practice, emphasizes that while AI will transform future software engineering roles, human expertise and creativity remain indispensable for delivering complex, innovative software. This reinforces a central theme: technology does not replace human value; it reshapes it. Effective AI adoption therefore requires continuous learning that preserves and enhances human judgment, empathy, and judgment under uncertainty. Walsh’s view adds nuance to the upskilling agenda by highlighting the need to protect the uniquely human elements of software development even as automation accelerates velocity and scale.
The barriers to deploying AI at scale: value, talent, and organizational inertia
Despite substantial investments in AI technologies, many organizations struggle to quantify AI’s value and to translate investments into measurable outcomes. A core barrier is the difficulty in calculating AI value or attributing outcomes to specific AI initiatives. Without a robust framework for measuring ROI, benefits can appear intangible, leading to hesitation or misallocation of resources. Gartner’s insights emphasize that the lack of reliable value measurement undermines progress and can impede competitiveness in fast-moving markets.
Talent scarcity compounds these measurement challenges. Even as organizations buy AI software and platforms, the supply of skilled personnel with the right mix of data science, software engineering, domain knowledge, and change-management capability remains limited. This imbalance between investment and available talent slows deployment, reduces time-to-value, and raises the risk premium on AI programs. The net effect is a reinforcing loop: under-resourced teams struggle to demonstrate value, which in turn undermines executive confidence and funding.
The World Economic Forum’s Future of Jobs findings dovetail with these concerns, illustrating a global demand for reskilling as AI-induced changes reshape job roles and organizational structures. The report’s emphasis on reskilling as a critical lever for navigating AI transitions supports Gartner’s call for deliberate upskilling as part of a comprehensive AI strategy. The combination of value measurement challenges and talent shortages creates a compelling case for a systematic, organization-wide learning program that goes beyond isolated training to embed AI fluency, ethical considerations, and cross-functional collaboration into daily work.
PwC adds a practical dimension to this discussion by highlighting the importance of cultivating specific mindsets as a prerequisite for successful AI adoption. Understanding the unique mindsets within an organization—and those of the broader workforce—can accelerate adoption, help organizations remain open to growth, and sustain new AI-enabled ways of working. PwC’s perspective suggests that mindset management—how people think about and respond to AI—can be as decisive as technical training itself in achieving durable transformation.
Building a culture of AI learning: mindsets, leadership, and sustainable change
For AI upskilling to be effective, it must be approached with deliberate planning and a well-defined strategy. Merely offering sporadic training sessions or one-off workshops is unlikely to produce lasting changes. A learning culture that is aligned with business goals involves structured curricula, clear milestones, and mechanisms for applying new skills to real work. PwC’s emphasis on cultivating specific mindsets highlights the need to tailor learning programs to the cognitive and behavioral patterns of different teams, ensuring that training translates into day-to-day practice and long-term capability.
Investor sentiment also plays a role in shaping the AI upskilling agenda. PwC’s Global Investor Survey indicates that 61% of investors place a high value on rapid AI adoption, suggesting that market expectations can drive urgency in skill development and adoption timelines. Conversely, more than 20% of workers express doubts about AI’s impact on their roles in the near term, signaling potential resistance or anxiety that must be managed through transparent communication, inclusive change management, and opportunities for employee voice. This dichotomy between investor enthusiasm and workforce ambivalence underscores the need for leadership to articulate a clear, credible vision of AI-enabled transformation and to provide tangible routes for employees to participate in that journey.
Leaders are urged to engage employees in conversations about AI in ways that demystify the technology and connect it to personal and organizational value. By framing AI as an enabler of new capabilities—rather than a threat to jobs—organizations can foster a more constructive dialogue about skill development, career progression, and the evolving nature of work. This approach aligns with the broader aim of sustaining a culture of continuous learning, where employees at all levels are empowered to experiment, share learnings, and iterate on how AI tools are used to achieve strategic objectives.
Practical implications for CIOs, CEOs, and the broader executive team
The Gartner study is not only diagnostic; it also offers a pathway for turning awareness into action. The following considerations emerge as practical imperatives for leaders seeking to close the AI readiness gap and accelerate meaningful outcomes:
- Elevate cross-functional AI governance: Create an AI steering committee or council with representation from product, operations, finance, HR, legal, risk, and IT. This structure ensures that AI initiatives align with enterprise strategy, data governance standards, and ethical considerations, while streamlining decision-making and accountability.
- Integrate AI into strategy and performance metrics: Shift planning cycles to include AI-enabled objectives, with explicit KPIs for efficiency gains, revenue growth, customer impact, and risk management. Tie executive incentives to the realization of AI-driven value and the maturation of AI capabilities across the organization.
- Systematize upskilling with a portfolio approach: Design a layered learning program that covers foundational AI literacy, domain-specific AI applications, ethical and governance considerations, and advanced capabilities for data science and engineering. Provide pathways for career progression that weave AI competencies into role evolution and leadership development.
- Foster a culture of experimentation with responsible governance: Promote safe experimentation through sandbox environments, pilot programs, and rapid feedback loops. Balance speed with risk controls, ensuring that pilots test not only technical feasibility but also ethical, legal, and societal implications.
- Invest in talent pipelines and partnerships: Recognize that internal upskilling must be complemented by targeted external talent acquisition, partnerships with academia and industry consortia, and the development of internal centers of excellence that cultivate AI expertise and best practices.
The road ahead: a strategic blueprint for AI-enabled growth
Looking forward, the Gartner findings offer a roadmap for organizations seeking to harness AI as a catalyst for strategic growth rather than a peripheral capability. The fundamental message is that AI readiness must be built into the fabric of organizational design, culture, and governance. This means moving beyond the perception of AI as a technology project to treating it as a core strategic capability that shapes product strategy, customer engagement, operations, talent management, and risk posture.
Boards and executives should expect more rigorous measurement and transparent reporting on AI initiatives. They should require evidence of sustained capability building—across data, governance, talent, and operational execution—and demand alignment of AI programs with broader strategic priorities. As AI technologies evolve rapidly, governance frameworks must be flexible enough to adapt to new capabilities, new risks, and new opportunities, while remaining anchored in ethical principles and regulatory expectations.
Moreover, the integration of AI into business models will require a reimagining of what constitutes value. For many organizations, the payoff will come not from isolated efficiency gains alone but from AI-enabled capabilities that unlock new revenue streams, improve customer experiences, and enable smarter, faster decision-making across the enterprise. This shift will demand a holistic approach to transformation, where leadership continuously reassesses strategic goals, investment priorities, and talent development pathways in light of evolving AI capabilities.
Sectoral nuances and global context
While the Gartner study speaks to a global audience, the implications of AI readiness vary by sector and geography. Industries characterized by complex regulatory environments, high data sensitivity, or stringent safety requirements may experience longer gestation periods for AI adoption and integration. Conversely, sectors with fast-moving data cycles and clear customer benefits may realize rapid value from AI-enabled processes, provided they invest in robust data governance, privacy, and ethical safeguards.
Global contexts also influence how organizations approach upskilling and AI strategy. Regions with stronger digital literacy ecosystems, more mature data infrastructures, and more mature AI markets may progress faster in building AI-ready leadership. Others may face challenges related to talent supply, regulatory clarity, and the need to build foundational data capabilities before attempting large-scale AI deployments. Recognizing these differences is essential for tailoring AI roadmaps to local realities while maintaining a coherent global strategy.
The evolving role of investors and broader market expectations
Investor sentiment plays a pivotal role in shaping corporate AI agendas. The PwC indicators that a majority of investors prize rapid AI adoption signals a market that rewards speed and impact in AI initiatives. This pressure can catalyze C-suite alignment and resource mobilization but also risks pushing organizations toward premature or poorly governed AI deployments. Sound governance, disciplined experimentation, and transparent reporting become essential to balance market expectations with prudent risk management.
The workforce dimension remains a critical determinant of AI success. If a sizable minority of workers doubts AI’s impact on their roles, organizations must deploy strategic change management to align perception with reality. This involves not only reskilling but also redesigning work processes, providing clarity about role evolution, and ensuring that AI tools empower employees rather than overwhelm them. Leaders who communicate a compelling, credible narrative about AI’s potential and who demonstrate tangible benefits through early wins are more likely to secure buy-in and sustain momentum.
Conclusion
In a landscape where AI is recognized as a strategic force by the majority of CEOs, the gap between awareness and readiness is the defining challenge for many organizations. Gartner’s findings reveal that while AI’s influence is acknowledged, confidence in CIOs’ AI capabilities remains limited, underscoring a broader leadership and capability gap that spans the C-suite. The path forward demands more than isolated upskilling; it requires a holistic overhaul of governance, strategy, and culture to embed AI into the fabric of how businesses operate.
Upskilling emerges as the central lever to close this gap. By cultivating a learning culture, aligning AI initiatives with strategic goals, and creating structured, scalable programs that develop the AI fluency of leaders and employees alike, organizations can translate AI potential into measurable outcomes. The evidence from the World Economic Forum and PwC reinforces the need for deliberate mindset shifts, disciplined execution, and ongoing investment in talent, data governance, and ethical considerations.
Ultimately, AI readiness is not a one-time project but a continuous organizational discipline. For CEOs, CIOs, and the broader leadership teams, the imperative is to transform how work gets done—integrating AI into daily operations, decision-making, and long-term strategy in a way that preserves human ingenuity while expanding the boundaries of what is possible. In this moment of convergence between ambition and capability, the organizations that commit to sustained learning, robust governance, and purposeful execution will be best positioned to harness AI’s transformative potential and to create durable, competitive advantage in the AI era.