Artificial intelligence has been the subject of unprecedented levels of investment and enthusiasm over the past three years, driven by a tide of hype that promises revolutionary transformation across every business function. Yet the gap between this technology’s promise and the delivery of real business value remains stubbornly wide. A recent study by BCG found that while 98% of companies are exploring AI, only 26% have developed working products and a mere 4% have achieved significant returns on their investments. This striking implementation gap raises a critical question: Why do so many AI initiatives fail to deliver meaningful value?
Knowledge gap
A big part of the answer lies in a fundamental disconnect at the leadership level: to put it bluntly, many senior executives just don’t understand how AI works. One recent survey found that 94% of C-suite executives describe themselves as having an intermediate, advanced, or expert knowledge of AI, while 90% say they are confident in making decisions around the technology. Yet a large study of thousands of U.S. board-level executives reported in MIT Sloan Management Review in 2024 found that just 8% actually have “substantial levels of conceptual knowledge regarding AI technologies.”
The only way AI initiatives can deliver significant value is when they are aligned with the organization’s broader enterprise architecture. When I introduced the terminology of “strategic enterprise architecture” back in 2000 (e-Enterprise, Cambridge University Press), I wanted to emphasize the importance of aligning technical architecture with the broader structure of the business as a whole–its purpose, strategies, processes, and operating models. With AI, this alignment is more important than ever. But it relies on the ability of senior leaders to understand both parts of the enterprise equation.
Opportunity costs
The current gap between confidence and competence creates a dangerous decision-making environment. Without foundational AI literacy, leaders simply can’t make informed decisions about how any given AI implementation fits with strategic priorities and the processes and existing tech infrastructure of the business. Ultimately, they end up delegating critical strategic choices to technical teams that often lack the business context necessary for value-driven implementation. The result? Millions of dollars invested in AI initiatives that fail to deliver on their promises.
In addition to project failure, a lack of AI literacy leads to strategic opportunity costs. When CEOs can’t distinguish between truly transformative AI applications and incremental improvements, they risk either underinvesting in game-changing capabilities or overspending on fashionable but low-impact technologies.
What CEOs need to know
Becoming AI-literate doesn’t mean that CEOs need to be able to build neural networks or understand the mathematical intricacies of deep learning algorithms. Rather, leaders need the kind of foundational practical knowledge that lets them align AI initiatives with core business operations and strategic direction.
At minimum, CEOs should develop a working understanding of AI in three broad areas.
1. The Types of AI
CEOs should understand the differences between the four major types of AI, the business applications of each, and their current maturity level.
- Analytical/Predictive AI focuses on pattern recognition and forecasting. This technology has been maturing for decades and forms the backbone of data-driven decision making in domains from finance to manufacturing.
- Deterministic AI systems apply predefined rules and logic to automate processes and decision-making, creating efficiency but requiring careful governance.
- Generative AI—the current hype king—creates new content that resembles human work, offering unprecedented creative capabilities alongside significant ethical challenges.
- Agentic AI is the new kid on the block. It not only analyzes or produces outputs but takes bounded actions toward defined goals. Agentic AI offers the greatest opportunity and the largest risks for enterprise transformation, but is largely untested at scale.
2. Technical Infrastructure Considerations
The infrastructure underpinning AI implementations shapes what is possible and practical for specific organizations.
· Deployment Models determine where and how AI systems operate. On-premises deployments maximize control over data, systems, and compliance but require significant capital investment and specialized personnel. Cloud-based deployments offer scalability and access to cutting-edge hardware but increase exposure to data security and vendor lock-in risks. Hybrid models retain sensitive processes in-house while outsourcing other workloads.
· Open and Closed Systems. Closed AI systems—proprietary systems created by commercial vendors—simplify deployment and provide enterprise-grade support but normally offer limited transparency and customization. Open (or open source) systems provide greater control and flexibility, particularly for specialized applications, but require more internal capacity and ongoing maintenance.
· Computing Resource Needs vary dramatically based on how AI is deployed. Most organizations primarily use AI for inference (using the reasoning capabilities of trained models) rather than training their own models. This approach significantly reduces hardware requirements but limits customization and mission-specific capabilities.
· Data Infrastructure is the foundation for successful AI implementations. This includes data pipelines for collecting and transforming information, storage systems for managing structured and unstructured data, processing frameworks for maintaining data quality, and governance mechanisms for ensuring compliance and security. Organizations with mature data infrastructure can implement AI more rapidly and effectively than those still struggling with data silos or quality issues.
3. The AI Tech Stack
The contemporary AI stack comprises five interconnected layers that transform raw data into outputs designed to create value for the enterprise.
· The Foundation: Data & Storage This foundation captures, cleans, and catalogs both structured and unstructured information.
· The Engine: Compute & Acceleration High-density Graphics Processing Units (GPUs), AI-optimized chips, and elastic cloud clusters provide the parallel processing that deep-learning workloads require. Container orchestration tools abstract these resources, allowing cost-effective experimentation and deployment.
· The Brain: Model & Algorithm This is where foundation models, domain-specific small language models, and classical machine-learning libraries coexist. Organizations must decide whether to consume models “as-a-service,” fine-tune open-source checkpoints, or build custom networks—decisions that involve trade-offs between control, cost, and compliance.
· The Connectors: Orchestration & Tooling Retrieval-augmented generation (RAG), prompt pipelines, automated evaluation harnesses, and agent frameworks sequence models into end-to-end capabilities.
· User Access and Control: Applications & Governance This top layer exposes AI to users through APIs and low-code builders that embed intelligence in user-facing systems.
For further foundational information on AI tech stacks, see IBM’s introductory guide.
Developing AI literacy in the C-Suite
How can busy executives develop the AI literacy they need to lead effectively? Here are some practical approaches to closing the knowledge gap.
Establish a personal learning curriculum. Set aside time for structured learning about AI fundamentals through executive education programs, books, or online courses specifically designed for business leaders.
Build a balanced advisory network. Surround yourself with advisors who bridge technical expertise and business acumen. This might include both internal experts and external consultants who can translate complex concepts into business terms without oversimplifying.
Institute regular technology briefings. Create a structured process where technical teams provide regular updates on AI capabilities, limitations, and potential applications in your industry. The key is ensuring these briefings focus on business implications rather than technical specifications.
Experience AI directly. Hands-on experience with AI tools provides an essential perspective. Work directly with your company’s AI applications to develop an intuitive understanding of capabilities and limitations.
Foster organization-wide literacy. Support AI education across all business functions, not just technical departments. When marketing, finance, operations, and other leaders share a common understanding of AI capabilities, cross-functional collaboration improves dramatically.
True leadership in the age of AI begins with curiosity and the courage to learn.
When CEOs become tech literate, they don’t just adapt to the future—they help shape it.
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