From Literacy to Fluency: Upskilling Your Workforce for the Agentic Era

AI is no longer a side project. It is becoming core business infrastructure. As this shift accelerates, the skills your workforce needs are changing too.

Basic AI literacy—understanding what AI is and what it can do—was a good starting point. But it is no longer enough. Organisations now need AI fluency: the ability to work confidently alongside intelligent systems, to supervise autonomous agents, and to apply AI effectively to domain-specific problems.

The difference between literacy and fluency

AI literacy is about awareness. It answers questions like: What is machine learning? What can generative AI do? What are the risks and limitations? Literacy is necessary, but it is passive. It prepares people to understand AI, not to use it.

AI fluency is about capability. It answers questions like: How do I use AI tools to solve problems in my role? When should I trust an AI output and when should I verify it? How do I supervise an autonomous agent and intervene when needed? Fluency is active. It enables people to work with AI, not just alongside it.

Why fluency matters now

The shift to agentic AI is accelerating. Autonomous agents are being embedded into enterprise software, handling tasks that previously required human intervention. By 2026, a significant proportion of enterprise applications will include task-specific agents.

This changes the nature of work. Employees are no longer just users of AI tools. They are supervisors, collaborators and quality controllers. They need to understand what agents are doing and why, recognise when an agent is going off track, know when to intervene and when to let the system run, and provide feedback that improves agent performance over time. These are not skills that come naturally. They must be taught and practised.

Building fluency: a practical approach

Moving from literacy to fluency requires a structured approach. Generic AI training is not enough—employees need to learn how AI applies to their specific roles. A marketing manager, a financial analyst and a customer service agent will use AI differently. Training should reflect this.

Fluency comes from doing, not watching. Give employees access to the AI tools they will use in their jobs and provide structured exercises that mirror real work. Knowing how to prompt an AI is useful, but knowing when to trust its output is essential. Training should emphasise critical evaluation, verification strategies and appropriate scepticism.

As agentic AI becomes more common, employees need to learn how to supervise autonomous systems. This includes understanding agent goals, monitoring behaviour, setting guardrails and knowing when human intervention is required. And because AI tools evolve rapidly, fluency is not a one-time achievement. Build ongoing learning into your workforce development strategy, with regular updates as tools and best practices change.

The role of domain expertise

One of the most important aspects of AI fluency is applying AI to domain-specific problems. A customer service expert who understands AI can identify opportunities that a general AI practitioner would miss. A supply chain specialist can spot data quality issues that would lead to poor model performance.

Fluency is not about replacing domain expertise with AI knowledge. It is about combining the two.

Common mistakes to avoid

Organisations often treat fluency as a one-off training event, when it is an ongoing capability that needs reinforcement. They focus only on technical staff, when every role that interacts with AI needs some level of fluency. They ignore the human side—fluency includes knowing when AI is not the right answer and when human judgement should prevail. And they assume tools will teach themselves, when vendor training is rarely enough and employees need guidance tailored to their context.

What leaders should do

If you are responsible for workforce development, start by assessing current AI fluency levels across your organisation. Identify the roles most affected by agentic AI and prioritise their training. Develop role-specific learning paths that go beyond generic literacy. Create safe spaces for experimentation where employees can practise without fear of failure. And build feedback loops so that learning improves over time.

The organisations that invest in fluency now will be best positioned to take advantage of the agentic era. Those that do not will find themselves with a workforce that understands AI in theory but cannot use it in practice.

The bottom line

AI literacy was the first step. AI fluency is the next. As autonomous agents become embedded in everyday work, the ability to work effectively alongside intelligent systems will be a core professional skill. The time to build that capability is now.