By Carly Aidone
AI is rapidly shifting from technical enablers to core drivers of business decision making. This work explores not only infrastructure and compliance, but also why executive AI literacy determines competitive advantage. Leaders who are proficient in AI risks and possibilities can manage responsibly, adjust strategies effectively, and maintain long-term performance.
As AI continues to redefine business strategy, operations and performance, the ability to understand and control AI itself, the highest level of leadership, is increasingly underestimating its critical capabilities.
Many of today’s executive focuses respond to infrastructure funding, tool selection and regulatory demands. However, even if AI systems don’t fully automate, as they begin to influence strategic business decisions, success depends on the technology deployed and the flow of people who direct them.
AI is no longer a back office enabler. He is becoming a co-pilot in pricing, product strategy, supply chain optimization, customer engagement and overall risk management decision-making. By 2027, 50% of business decisions are expected to be augmented or automated by AI agents. These agents operate at speed and scale, but are not always subject to contextual awareness or human judgment. That’s where management oversight becomes essential.
From delegation to accountability
For years, AI lived straight into the realm of data scientists, technicians and innovation teams. Business leaders focused on outcomes, while technical teams handled model development, deployment and management. However, this model is no longer sufficient.
AI is now shaping core decisions that have a direct impact on revenue, compliance and reputation. Leaders need to move from passive sponsorship to active engagement. It means asking not only what AI systems do, but how to reach conclusions, what data they can get from, and where the risk arises.
This lack of flowing leader can inadvertently approve initiatives they don’t fully understand, overestimate what AI can offer, or overlook important gaps in governance. In a world that is rapidly expanding beyond the reach of AI, the blind spot is inefficient and business risk.
AI literacy is strategic literacy
Executive-level AI literacy does not require coding skills or technical expertise. But it requires strategic intelligence: the ability to question assumptions, assess risks, and align AI deployments with long-term business priorities.
This literacy gives leaders the tools to find flawed logic, exaggerated claims, or narrow use cases that don’t expand. It strengthens better decisions about where to invest, how to govern, and when to pull back. It also improves the quality of dialogue between business and technical teams, ensuring that AI is not implemented for technology, but as a clear factor in value.
For an organization with this understanding, the benefits are concrete. By 2027, Gartner predicts that organizations highlighting executive AI literacy will achieve 20% higher financial performance compared to those not.
From briefings to immersive learning
To build true flow ency, executives need more than status reports and vendor demonstrations. They need to be directly involved in AI systems and see the actual impact of use cases, test prototypes, and AI.
For example, supply chain leaders may test AI agents that dynamically rearrange inventory based on forecast demand. Marketing executives may use synthetic data to model campaign outcomes without relying on sensitive customer data. These experiences sharpen judgment, surface limitations, and build trust in decision-making.
Composite data is one area that explains this very well. It provides innovation and diverse training data that provides privacy, but introduces new risks if not properly managed. Without understanding how synthetic datasets are generated and validated, you may find that leaders rely on AI models that look accurate but fail in important ways. Literacy can ask the right questions before the risks come true.
Governance duties
As AI becomes deeply embedded in operational workflows, its impact on strategic decision-making is growing. It is immediately expected that the board and C suite will manage AI with the same diligence that applies to financial reporting or cybersecurity.
This requires not only high levels of awareness but also informed surveillance. Within a few years, more and more AI-generated insights will be used to challenge management decisions. Leaders who understand how these systems work, how data is structured, how output is generated, and how biases and faults creep up are better positioned to lead with reliability and accountability.
Governance must evolve to include AI as a strategic prioritization, not just technical or compliance issues. Executive Literacy is an enabler.
Global order
Regulatory frameworks vary across regions, from EU AI laws to new standards in Asia and North America, but fundamental requirements are consistent. Companies need to demonstrate not only responsible AI deployments but also competent leadership.
This is not a local conversation. This is a global shift in expectations for how leaders will engage with technology. Leading companies are companies that have the ability to balance innovation and governance, speed and safety, and accountability experiments.
Leading the next phase
The pace of AI adoption will only accelerate. But no matter how advanced the model is, they are as effective and safe as the leadership that guides them.
Today, executive AI literacy is essential to building resilient, moving forward, and high-performance organizations. Today, those investing in these capabilities are best positioned to leverage AI for a sustainable competitive advantage.
Gartner analysts will further explore these insights at the Barcelona IT Symposium/XPO on November 10-13, 2025.
About the author
Carly Aidone He is a VP analyst at Gartner and specializes in data, analytics and AI. She advises clients on analytics and AI strategy, program development, organizational design and software portfolio management. Her research focuses on helping organizations apply advanced analytics and AI to complex business problems and navigate the convergence of data, analytics, and software engineering roles.
