As organizations adopt AI, much of the training has focused on the fundamentals: prompt writing, acceptable use policies, and data privacy and security. These topics are essential for responsible AI use, but they primarily teach employees how to use AI safely.

As AI becomes embedded in everyday work, training should also help employees understand the economics behind it, such as when AI creates business value, what it costs to use and where human judgment remains essential.

The AI Economic Model

AI isn’t a free productivity tool, even though it may feel that way. Unlike a traditional software license, every prompt, document analysis and automated workflow carries a measurable cost.

As a result, many organizations have begun tracking AI usage through token consumption and other cost metrics. While these measures provide useful data, they do not answer the more important question: Is AI being used where it creates the greatest value?

Employees should understand how AI tools are priced, what drives usage costs and which types of work justify that investment. Some tasks deliver significant value when accelerated with AI. Others can be completed more quickly, more accurately or at a lower cost without it.

Training should help employees think critically about questions like:

• Does AI improve the quality or speed of this task?
• Is the value created greater than the cost of using AI?
• Would a simpler model (or no AI at all) produce the same result?
• Is this work appropriate for AI given the legal, compliance or reputational risks involved?

These are business decisions as much as technology decisions, and employees need guidance on how to make them.

Human Judgment Remains Essential

As AI becomes more capable, one misconception continues to surface: that better AI means less need for human involvement.

The opposite is proving to be true. AI can draft content, summarize documents, analyze information and generate ideas, but it cannot replace human judgment. Employees are still responsible for determining whether AI-generated outputs are accurate, ethical, compliant and appropriate for the situation at hand. This means employees need to understand the difference between what AI can do and what it should do.

There are countless decisions where human judgment remains indispensable, such as handling sensitive employee matters, making compliance determinations, navigating ethical questions, managing customer relationships, approving policy decisions or making high-consequence business choices. AI training should prepare employees to recognize these situations and understand where human expertise adds the greatest value.

AI Training Should Be Continuous

Because AI capabilities, regulations and organizational policies continue to evolve, AI training cannot be treated as a one-time software rollout.

Employees need ongoing education that covers responsible AI use, governance, business value, emerging risks and changing regulations. As AI tools become more capable, organizations must continually update training to reflect new opportunities and new challenges.

Many organizations aren’t there yet. Traliant’s recent AI Governance Gap survey found that only 51% of organizations provide training on responsible AI use, while just 45% offer AI literacy training to all employees. The findings suggest a widening disconnect between AI adoption and employee readiness, particularly as organizations increasingly rely on AI for compliance, policy development and workplace decision-making.

Without continuous training that includes all aspects of AI and how it’s best used, employees are left to make increasingly complex business decisions with little understanding of the costs, risks or limitations of AI tools.

The organizations that realize the greatest value from AI won’t necessarily be those that deploy the most models or automate the most workflows. It will be the ones that equip employees to understand how AI creates value, where it delivers the strongest return on investment and when human expertise remains indispensable. That starts with training.

The next generation of AI training should move beyond basic digital literacy to include the economics of AI, sound business judgment and responsible decision-making. This broader approach helps employees maximize AI’s business value while minimizing unnecessary costs and risk.