Picture a typical corporate training session: employees attend a three-hour workshop, complete modules, they might enjoy the free lunch but, after the training, they’ll complain about the time they just wasted.

Artificial intelligence (AI) training can be even worse because nobody knows what material to teach. It’s all so new. Artefact reports that 47% of AI-trained employees say they still need more training, suggesting many feel the training wasn’t sufficient. In the past, companies could tolerate this inefficiency. But AI is not going to be forgiving to slowpokes.

Start With an Audit

For corporate training to be successful in upgrading AI skills, the first step needs to be a baseline audit of your workforce’s AI maturity, because assumptions about AI-readiness can be misleading. Many organizations assume that senior employees might be more ready for AI, but my experience tells me that some juniors might be highly AI-literate, while some seniors may lack even foundational AI knowledge.

I recommend using an external facilitator to give you a real reading of the organization’s AI maturity. Companies often forget that if the audit is run internally, employees may not answer honestly. They may hesitate or try to present themselves as more mature than they are. They may even feel that the survey is a test because it’s coming from their employer.

A useful scorecard for an audit looks at AI maturity in terms of tiers and competency level in each tier. My friend Alok Khatri, co-founder of Tangible Careers, breaks this down as native, foundational and deep skills. Each of these tiers is mapped to four levels of competency: awareness, application, mastery and influence. In this matrix, someone can be at the influence level in native skills but only at the application level in foundational skills.

Native AI skills are the basic skills everyone needs now: how to use AI tools well and how to integrate them into daily work. Foundational AI skills are for people who go one step deeper, like building workflows, solving business problems using AI and understanding how these models behave. Deep AI skills are more specialized, typically for people who want to work on the research or innovative side of AI.

The learning and development (L&D) team must come up with the questions to use for determining mastery level at each tier in the audit survey. For instance, the skills that determine application-level knowledge for an employee at the AI foundational layer at a financial services company might look completely different from the skills required to achieve the same competency level for an engineer who is expected to embed AI into a SaaS product.

And because designing these questions from scratch can be overwhelming, especially across diverse roles, it helps to rely on existing, field-tested templates. Several proven scorecard frameworks already exist, such as:

• HustleBadger’s AI Maturity Model, which offers a clear scoring system across areas like leadership, culture, data readiness and governance, helping teams benchmark gaps and align on what “good” looks like across functions.
• OWASP AI Maturity Assessment, which leans on AI governance-focused criteria — from risk controls to documentation and oversight.

Together, these frameworks help companies replace guesswork with evidence-based scoring and actionable maturity roadmaps. The scoring system that you create for your company will help you see differences clearly instead of assuming skill levels based on age, job title or experience.

Instead of offering random workshops, companies can use this AI audit to build a proper roadmap, from AI native all the way to deep skills, which will make training accountable to both employees and the organization.

Should AI Training Require Coding Skills?

As CEO of Programiz, I’m biased towards code, but hear me out.

Let’s say we give the same problem to two financial analysts. They need to use AI to flag suspicious transactions across thousands of customer records. Both have similar years of experience. But only one of them knows how to code and work with data in Python.

The difference in how they approach the work becomes clear immediately. The analyst who knows coding logic can anonymize sensitive data, write Python scripts with AI assistance and securely run, test and evaluate the fraud detection formula on their systems. While the other analyst might need to run the entire data set through a large language model (LLM), which might be in breach of the company’s policies.

The analyst who knows how to use AI but lacks coding skills might generate a visually impressive report but struggle explain the logic behind the output. The other analyst, who has the toolkit of coding plus AI, can demonstrate exactly how the result was generated, protect customer data and scale the solution for larger datasets. This ability to explain and use AI responsibly will be the most marketable skill in the workforce.

This does not mean everyone must learn coding. It simply means coding is a multiplier. For people in certain roles, it shifts them from simply using AI to accelerate task completion to using AI at scale for innovation.

Conclusion: The Time is Now for AI Training

We are now in a post-AI world. The companies that succeed will be the ones that have employees who understand AI deeply, can explain their work and take responsibility for the decisions made in collaboration with AI. AI-native, AI-ready and AI-expert are not just labels; they are the milestones to future-proof your workforce.

The risk is simple: if you wait, you will be behind and spend years catching up. Start with an audit. Identify where your people really are, not where you think they are. Give them real, helpful training. Help them move up and across competency levels. Because in the post-AI world, where everybody has access to the world’s knowledge at their fingertips, the companies with workforces that know how to use AI to their competitive advantage will lead the future.