AI is everywhere. Scaled AI is rare. The difference is organizational literacy.
Enterprise AI has a scaling problem—and it is not about the technology.
In one composite example I have seen repeatedly: a Fortune 500 company spent $4M building an AI model that predicted inventory demand with 94% accuracy. Eight months later, it sat unused—warehouse managers did not trust it, procurement could not interpret its outputs, and finance could not justify the ROI to the board. The technology worked. The organization did not.
This pattern is pervasive. According to McKinsey, 88% of organizations report regular AI use in at least one business function—up from 78% a year ago—yet only about one-third have begun scaling across the enterprise. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027—often due to escalating costs, unclear business value, or inadequate risk controls.
The root cause is overlooked: companies invest heavily in AI technology while neglecting the organizational literacy required to use it effectively. Having led cross-functional teams of 450+ members building AI products at Google, Snowflake, and LiveRamp, I have watched this pattern repeat across industries. The World Economic Forum underscores the urgency: 77% of employers plan to upskill their workforce, while 41% anticipate workforce reductions where AI automates tasks. Enterprise-wide AI fluency has become a critical competitive differentiator.
Why Literacy Outperforms Expertise
Most organizations approach AI as a technical problem. Hire data scientists. Build a machine learning team. Deploy tools. This creates islands of capability surrounded by oceans of confusion.
The real bottleneck is not building AI—it is using AI effectively across the organization.
Research from Accenture reveals organizations with fully modernized, AI-led processes are 3.3 times more likely to succeed at scaling generative AI use cases and report 2.5 times higher revenue growth. The implications span every level:
- Executives without AI understanding approve projects they cannot evaluate, then abandon them when results lag
- Managers lacking literacy cannot assess AI proposals, leading to wholesale approval or rejection
- Individual contributors who distrust outputs default to familiar manual processes
- Cross-functional teams miscommunicate due to lack of shared vocabulary
The companies getting this right invest in literacy before technology. Unilever trained 23,000 employees in AI usage by the end of 2024; industry reporting has cited results such as creative briefs completed 21% faster and design teams gaining eight days per month back. JPMorgan has made AI training mandatory for new hires and integrated AI tools into daily workflows; Mary Erdoes says this is saving some analysts two to four hours daily by eliminating rote tasks. Both recognized the same truth: technology deployment without organizational readiness is expensive disappointment.
Literacy is the multiplier on AI investment.
The Four Levels of AI Literacy
AI literacy exists on a spectrum. Different roles require different depths. A progressive framework provides practical structure:
Level 1: Awareness — Understanding what AI does and why it matters. Target: all employees. Success looks like articulating AI’s role in company strategy.
Level 2: Evaluation — Assessing AI proposals, understanding limitations, and asking the right questions about data quality and model reliability. Target: managers and team leads. Success looks like critically evaluating vendor pitches and internal AI requests.
Level 3: Steering — Setting AI strategy, prioritizing investments, governing organizational AI use. Target: executives and board members. Success looks like making informed investment decisions and establishing appropriate governance.
Level 4: Direct Use — Hands-on operation of AI tools—writing prompts, interpreting outputs, knowing when to trust results and when to verify. Target: individual contributors and power users. Success looks like daily productive use with appropriate skepticism.
As Microsoft CEO Satya Nadella has emphasized, long-term career relevance depends on being a “learn-it-all” rather than a “know-it-all”—and that means embracing AI as a tool rather than fearing it.
Role-Specific Training
Generic AI training wastes resources. Role-specific programs consistently outperform one-size-fits-all approaches—and companies with strong learning cultures typically see materially higher retention.
For Awareness (All Employees): Focus on what AI does and why it matters to the business. Effective formats include company-wide briefings and short video modules. Common pitfall: skipping this level and jumping to tools.
For Evaluation (Managers): Focus on assessing AI proposals and understanding limitations. Effective formats include workshops with exercises and peer learning groups. Common pitfall: assuming documentation alone suffices.
For Steering (Executives): Focus on strategic implications and decision frameworks. Effective formats include short sessions, peer case studies, and board briefings. Common pitfall: technical deep-dives they will not retain.
For Direct Use (Power Users): Focus on hands-on operation—writing prompts, interpreting outputs, knowing when to verify. Effective formats include hands-on tool training and job-specific applications. Common pitfall: generic training disconnected from actual work.
Industry Considerations
Literacy challenges vary by sector:
- Financial Services: Explainability requirements and audit trails add compliance complexity
- Healthcare: Clinical contexts demand emphasis on AI limitations in high-stakes decisions
- Manufacturing: Frontline workers need focus on human-machine collaboration
- Retail: Customer-facing roles must understand AI-driven personalization
Measuring AI Readiness
Effective measurement combines quantitative indicators (adoption rates, project success rates, approval velocity) with qualitative signals (vocabulary consistency, question quality, proposal realism).
Diagnostic Questions: (1) Can you explain what AI does in your role? (2) How do you determine when to trust AI recommendations? (3) What would you do if AI produced an incorrect result? (4) How would you request an AI capability?
Building a Sustainable Program
Start with leadership. If executives lack literacy, they make decisions that undermine adoption.
Design for roles. Develop content aligned with actual job responsibilities.
Connect to real work. Train on actual tools, actual projects, actual decisions.
Build peer networks. Create communities of practice around AI.
Iterate continuously. AI capabilities evolve rapidly. Literacy programs must too.
The Future of AI Literacy
As AI systems evolve from assistive tools to autonomous agents, literacy requirements will intensify:
- Agentic AI: Employees will need literacy in oversight, guardrails, and exception handling
- Multimodal AI: Expanded literacy spanning text, images, video, and audio applications
- AI-to-AI Collaboration: Literacy in coordinating multiple AI systems working together
Organizations building foundations today will adapt faster tomorrow.
Key Takeaways
- Literacy is your competitive moat — Unilever and JPMorgan invested in literacy first; competitors who skip this step will spend more to achieve less
- Structure beats randomness — The four-level framework (Awareness → Evaluation → Steering → Direct Use) prevents wasted training budgets
- Generic training destroys ROI — Role-specific programs outperform one-size-fits-all approaches, which breed cynicism
- Your industry has unique gaps — Financial services needs explainability literacy; healthcare needs limitation literacy; retail needs personalization literacy
- Start now or fall behind — Agentic AI will intensify requirements; organizations building foundations today own a 12-month advantage
The question facing enterprise leaders is not whether they can afford to invest in AI literacy. It is whether they can afford not to.
Your move this week: Ask your top 10 executives one question: “If an AI system gave you a recommendation tomorrow, what would make you trust it?” Their answers will reveal your organization’s literacy gaps faster than any assessment. Build your program from there.
Onil Gunawardana is a product leader with over 15 years of experience building AI-powered enterprise products at Google, Snowflake, and LiveRamp. He has led cross-functional teams of 450+ members and developed mentorship programs for hundreds of professionals. He holds an MSc from Stanford and an MBA from Harvard Business School.
