As artificial intelligence (AI) continues to reshape the workplace, business and information technology (IT) professionals will benefit from structured training programs that equip employees to use these technologies effectively and responsibly. The adoption of AI is more than a technology rollout. It’s a transformation in how work gets done, and training is central to driving value. 

Here are eight actionable steps for training employees on AI in the workplace.

1. Begin With an Assessment of Skills and Business Needs

Before launching an AI training program, organizations should assess both the current workforce’s skills and the company’s business objectives that AI should support. For example, it helps to identify which job roles will integrate AI tools, what those tools will do and what gaps exist in employee readiness. 

By clearly defining team needs, businesses can determine how AI impacts business outcomes such as increasing productivity, enhancing decision-making or improving customer service. Training will then be more targeted and relevant, which in turn increases engagement and effectiveness.

2. Define Clear Training Objectives and Outcomes

After assessing needs, businesses can establish clear training objectives. Employees should clearly understand their next steps after the training. For example, identifying tasks where AI can assist, crafting effective prompts for generative models, evaluating AI-generated outputs and adhering to responsible use guidelines.

Research indicates that many workers anticipate AI will have a significant impact on their work and want organizations to provide training to keep pace. Nearly half feel that formal training is the most effective way to boost AI adoption, and many value pilot programs and incentives like recognition or financial rewards. With almost three-quarters of companies using AI in at least one area, fluency with these tools has become a key differentiator.

Clear objectives enable organizations to measure success through improved efficiency, better decision-making, increased confidence and enhanced ethical compliance. Linking training to business goals and employee development makes programs more relevant, actionable and impactful.

3. Start With Foundational AI Literacy for All Employees

Training should begin with a baseline of AI literacy for all workers, regardless of role. This foundational module should cover what artificial intelligence is, how machine learning and generative AI work, the typical capabilities and limitations of AI and the role of human oversight.

For example, the Digital Transformation Agency in Australia recommends foundational training for all staff on AI fundamentals and responsible use. Understanding AI’s dual potential — as both a productivity tool and a vector for misuse, such as its involvement in over 80% of phishing emails — underscores the importance of this literacy. Employees who understand these risks are better equipped to apply AI responsibly, protect organizational data and utilize the technology effectively.

Establishing a baseline of literacy also lays the groundwork for more advanced, role-specific training later on, ensuring individuals can build deeper technical and analytical skills as they progress.

4. Tailor Training to Role-Specific Use Cases and Tools

After foundational training, the next step is to tailor modules to different departments. This might include marketing teams, human resources, operations, IT or frontline staff — each may use AI differently. Best-practice guidance recommends customized, role-specific modules that align with employees’ responsibilities.

For instance, a customer-service team might learn how to utilize AI to summarize interactions and suggest responses, while a finance team might learn how to apply AI for predictive modeling. Designing training around job-relevant use cases helps link learning to actual outcomes.

Providing practical, role-based examples also increases confidence and motivation, as they can immediately see how AI enhances their daily work and supports overall business goals.

5. Incorporate Hands-On Learning, Experimentation and Simulations

To move beyond passive learning, training should include hands-on labs, guided experimentation, scenario-based simulations and opportunities to practice AI tools in a safe environment. This approach helps employees understand the real-world implications of their actions. 

Interactive sessions also encourage creative problem-solving, allowing individuals to explore how AI can streamline their workflows or uncover new efficiencies. By experimenting with various tools and datasets, participants develop a deeper understanding of AI’s capabilities and limitations.

Additionally, structured feedback during these exercises helps reinforce good practices and correct misconceptions or misuse early. The iterative process turns learning into active engagement, supporting the transition from theoretical understanding to practical mastery.

6. Embed Ethics, Governance and Responsible Use Throughout Training

A critical dimension of AI training is the ethics and governance surrounding the use of AI in the workplace. According to the U.S. Department of Labor, providing AI training for workers is one of the key elements of responsible AI deployment.

Training should cover topics such as data privacy, transparency of AI-driven decisions, human-in-the-loop oversight, bias mitigation and safe data handling. Embedding these topics ensures employees understand how to use AI safely and ethically.

Promoting ethical awareness also builds trust within the organization and with external stakeholders. With consistent and responsible AI principles, businesses strengthen accountability, reduce compliance risks and foster a culture where innovation and integrity advance together.

7. Implement a Blended, Continuous Learning Model

Training should be ongoing. Because AI tools and business needs evolve rapidly, continuous and blended learning is essential. Blended models that combine in-person sessions, e-learning modules, peer learning and microlearning refreshers are recommended. 

For example, a marketing team might attend an in-person workshop on using AI for campaign analysis, complete online modules on data privacy and tool-specific tutorials, and participate in peer-led discussions to share best practices and lessons learned.

Additionally, offering refresher modules and updating content to reflect the latest AI tools keeps the program current and relevant, sustaining engagement and proficiency across the workforce.

8. Monitor Progress, Measure Outcomes and Refine the Program

Finally, training programs must be evaluated and refined over time. Organizations should measure uptake, confidence levels, changes in performance metrics and quality of AI usage. 

Studies show that organizations integrating AI into workflows often adjust training practices to ensure employees can effectively use new technologies, highlighting the critical role of structured learning in successful AI deployment.

Metrics could include tracking module completion, the adoption rate of AI tools in daily workflow, the reduction in manual effort or improvements in decision quality and business outcomes. Feedback loops should capture experiences and identify areas of friction to improve overall performance. The training program then evolves based on data and changing business priorities.

Building a Workforce Ready for the AI Era

For business and IT professionals seeking to equip their workforce for the era of artificial intelligence, developing a structured training program is crucial. By assessing needs and monitoring AI integration, organizations set a strong foundation that respects both the technology and the human workforce. When employees confidently use AI tools in alignment with business goals, the organization unlocks the potential of AI while maintaining trust, accountability and performance.