AI fluency vs. AI literacy: Getting the strategy right

Artificial Intelligence (AI) is fast becoming the engine powering how organisations automate, optimise, and scale their operations today. Across industries, employees are adopting AI tools faster than leaders anticipated.

While C-suite executives estimate that only 4% of employees use generative AI for at least 30% of their daily work, data suggests that number is three times higher. Despite the growing adoption, many employees in organisations reiterate the need for guidance to use AI effectively in their daily roles.

The most overlooked problem here is that most organisations lean towards traditional one-off AI courses only designed for the technical teams–focusing on their technical components, algorithms, and interfaces. However, the complexities of the modern workplace and the rise of generative AI tools like ChatGPT, Perplexity, Claude, and Grok call for more comprehensive AI fluency–it’s about understanding where and how to apply AI within one’s specific function, recognising its limitations, and navigating its ethical and operational implications. This shift from literacy to fluency makes AI adoption go from a passive understanding to active, confident applications.

Evaluating “Where We Stand” as a business

AI topics are the fastest-growing across all business and professional learning categories. Employee enthusiasm signals a workforce eager to leverage AI into their routines, but with a clearer direction. The call now is for leaders to move beyond ad-hoc encouragement and toward structured capability building, with an emphasis on building AI skills across each team.

In order to achieve it, businesses must first assess their current level of AI fluency. This begins with asking the right questions—when to rely on AI, when to pause, and how to anticipate broader implications beyond immediate tasks.

Below are five key areas to evaluate:

Familiarity with AI tools: With AI adoption accelerating across roles, it’s vital to assess how teams are engaging with these tools. Are employees using approved, secure AI platforms or turning to shadow AI solutions that could expose organisational data? Are they aware of the ethical implications of their AI use, or simply relying on it to speed up tasks? This evaluation ensures effective adoption with the right tools, without compromising security.

Value of current upskilling initiatives: Research shows that 48% of employees rank training as the most important factor for generative AI adoption. Yet nearly half report receiving moderate or minimal support. This gap highlights the need for continuous, updated, and nimble learning programs. Businesses should ask: How often are our skill development initiatives refreshed? Are they integrated into the flow of daily work? Do employees have channels to share feedback or seek help?

Relevance of training to specific roles: The demand for AI-focused learning is surging in India, with the consumption of skills like prompt engineering rising by 1,526%. Yet, many corporate upskilling programmes remain generic and outdated, struggling to keep pace with the rapid evolution of AI tools and applications. Employees often understand AI conceptually but lack AI fluency to apply it within their specific roles. Organisations should evaluate how well their AI training maps to functional responsibilities and role-specific use cases, ensuring it drives real impact.

Assessing the current AI state: Amidst widespread AI adoption, many companies struggle to see tangible returns. In fact, 74% of global organisations fail to realise value from their AI investments. A thorough audit can help uncover what’s not working–whether it’s tool misalignment, skill gaps, or lack of measurable outcomes–and guide a more effective path to AI fluency.

Measuring progress through KPIs: A strong AI fluency strategy depends on clarity of measurement. Businesses must define what success looks like: What metrics indicate improved AI proficiency? How do we track ethical usage, productivity gains, or innovation outcomes? Clear KPIs help identify gaps, refine learning strategies, and sustain progress over time.

Building true AI fluency–from awareness to action

Companies that prioritise AI fluency close the widening gap between technological potential and practical execution. The foundation lies in recognising that every employee learns differently and starts from a unique point based on their prior knowledge and experience. This enables companies to identify where AI fits within their function, operations, and learning journey. 

The most effective approach is to establish clear benchmarks for each function within learning and development (L&D) initiatives. Leveraging skill development platforms can provide personalised learning paths to accelerate hands-on learning and make it continuous by integrating it into the daily flow of work. This practical, embedded learning empowers employees to adapt swiftly, think creatively, and choose the right AI tools for their specific needs.

True fluency also requires cross-functional learning agility, where departments learn from each other and share insights beyond traditional boundaries. Creating this interconnected learning culture ensures that innovation scales across the business, enabling teams to experiment, iterate, and build collective intelligence faster. Regular retrospective and knowledge-sharing sessions that celebrate wins and analyse misses help refine strategies and sustain progress.

Most importantly, leadership must serve as the architects of change in the AI adoption journey. Employees today expect clarity, direction, and encouragement from their leaders to uncover how it aligns with the organisation’s broader purpose. Leaders play a defining role in aligning AI with business models, value creation, and culture, while guiding teams confidently through this transition. When strategic foresight meets empathy, leaders create an environment where experimentation feels safe and innovation becomes instinctive.

When foundational understanding, targeted approach, cross-functional collaboration, and visionary leadership align, organisations move beyond basic AI literacy to achieve true AI fluency. And with that fluency comes the ability to turn AI’s potential into measurable performance, opening doors to a future where technology and human ingenuity advance together to create better outcomes.

(Vinay Pradhan, Country Manager & Senior Director, India & South Asia, Udemy)