How to Build an AI-Literate Workforce: A Strategic Roadmap for HR & L&D

Learning how to build an AI literate workforce isn’t optional anymore.  

It’s the defining factor between organisations that will thrive in the age of AI, and those left scrambling to catch up.  

And most companies are approaching AI literacy backwards, treating it as a one-off, siloed training initiative versus an ongoing one.  

But in reality, it’s about knowing where to start, how to scale, and which AI literacy skills matter when tools are evolving at a rapid pace.  

Now, we understand that you might have (a lot) of questions.  

For instance:

• ‘What does responsible AI use actually entail?’

• ‘How do I scale AI literacy across the entire company?’

That's what we’ll unpack in this article; think of it as your ultimate guide for all things AI literacy.  

We’ll cover:  

• What AI literacy means (without the jargon).

• The 4 pillars of AI literacy.

• The difference between AI literacy, awareness, expertise, and compliance.

• Why AI literacy matters for organisations.

• How to train employees on AI literacy.

• The common mistakes when training employees on AI use.

Without further ado, scroll for the full insights!

What AI literacy really means  

Before diving into how to build an AI literate workforce, it’s worth defining what AI literacy actually means.  

AI literacy is the ability to understand how to leverage artificial intelligence (AI) in the workplace safely, responsibly, and effectively.  

And let’s be clear – it has nothing to do with your employees’ ability to learn code or build models.  

Think of AI literacy as a common, shared language. Every employee in the organisation understands the risks, opportunities, and limitations of AI.  

GoodHabitz’s CEO Annabelle Vultee echoed this:

“Both employees and employers want the same thing – to understand what AI is and how to get it working on a practical level.”

Read her full take on AI adoption in the workplace.

Erica Farmer, TEDx speaker on AI and Future Skills, shared the same opinion:

“Understanding who owns or is ‘responsible’ for AI adoption becomes a real test of organisational culture. And the reality is that it’ll always be a joint conversation – not a siloed one where it’s just down to HR or IT.”  

“It’s across the organisation through a solid governance structure – where there is mutual trust built.”  

Erica was a guest speaker on one of our webinars, hosted by GoodHabitz’s General Manager for the UK, Mark Thompson. Tune in and watch the full conversation below.  

Let’s explore some examples to better understand how an organisation might have a shared level of AI literacy across its workforce.  

For instance, let’s take the role of an L&D leader. One of the skills related to AI literacy is prompting.  

An L&D leader might leverage prompting to enhance or improve their strategy, such as conducting personalised learning paths for employees.  

For further inspiration, download our AI prompt guide covering 9 prompts for L&D leaders.  

AI is also influential in blue-collar and non-office work. For instance, AI driven technology can help farmers, with more data-driven, scalable, and sustainable farming practices.  

Third, a shared level of AI literacy across a workforce can be defined by a clear understanding of how to leverage and use different, everyday AI tools already in use, such as ChatGPT, Claude, or Perplexity.  

AI literacy vs AI awareness vs AI expertise vs AI compliance

It is also worth highlighting the difference between AI literacy, AI awareness, and AI expertise.  

• AI awareness focuses on the basic understanding that AI exists and has an influence in daily lives, and whilst it presents opportunities, it is also flawed. Someone who demonstrates AI awareness will understand that AI can be persuasive, biased, and incorrect, all at once.  

• AI literacy goes one step further or deeper from awareness. It focuses on a deeper understanding of how AI can be responsibly and productively leveraged in an employee’s daily work.  

• AI expertise covers is a detailed, technical understanding of the inner workings of AI principles and models. Someone who coins themselves an AI expert will be able to design, train, and monitor the deployment of AI models, such as large language models (LLMs), whilst considering potential ethical implications and data privacy. AI experts require technical proficiency, such as understanding coding languages like Python.

• AI compliance training provides a regulatory baseline. Your organisation adhering to legal, ethical, and industry standards when it comes to developing and deploying AI systems. For instance, documentation or audit trails.  

The 4 pillars of AI literacy

There are four foundational pillars to consider.

1. Understanding: this relates back to AI awareness as discussed earlier.  

2. Using: this focuses on the effective and safe application and use of AI models and tools in the workplace.

3. Evaluating: this is centred around practicing human skills like critical thinking. Can an employee effectively evaluate the output from AI?  

4. Creating: this is around the constant feedback loops. Employees should be able to improve the output and quality with prompts.  

As a collective, these pillars form an adequate level of AI literacy.  

The specific depth and focus of AI literacy varies by role.  

For example, someone working in customer support using an AI chatbot support tool needs different knowledge than a procurement manager using AI supplier risk assessment software.  

But the underlying principle remains the same: people should understand the tools they're using well enough to use them properly.

Why AI literacy is now a business requirement  

AI is no longer limited to specialists.

According to Azumo, around 58% of employees regularly use AI tools in their daily work.

The takeaway is simple. AI is shaping how everyone works. Without the right skills, that creates a gap between potential and reality.

With this in mind, here are three reasons why understanding how to build an AI literate workforce is a business requirement:

Improve employee productivity

AI can boost productivity and decision-making. But only when people know how to use it well.

Employees need more than access to tools. They need guidance, practice, and clear use cases.

Iris Cremers, GoodHabitz’s Chief Human Resources Officer, echoed this:  

“It's a common assumption that once a new strategy is announced, people will instantly know how to work differently. Once leadership says, ‘we’re doing AI’, the perception is that everyone will suddenly work smarter and more efficiently.”

“In reality, new technology can feel complex, or even scary, without proper support.”

Remy Reurling, GoodHabitz’s AI Programme Manager, shared more on the productivity paradox:

“We’re spending more on AI than ever before, yet productivity gains are shrinking. Why? Because we’re force-feeding technology without teaching people how to use it.”  

Let’s take your finance team as an example.  

They don’t need to understand machine learning algorithms, but they do know how to interpret AI-generated forecasts, and spot potential errors.  

Curious to hear more from Remy?  

He broke down the full play-by-play on how to build an AI literate workforce in a webinar; watch it here.

Ensure no shadow AI use

Employees are already using AI. The risk is that it happens without visibility or guidance.

Many organisations don’t know which tools are being used or how safely they’re applied. At the same time, confidence is high while training is low.

According to a recent global study by KPMG, 61% of employees haven't received AI-related education or training, yet 3 in 5 believe they can use AI effectively.

Furthermore, the implementation of AI tools brings great risk.

Research by Riskonnect revealed 93% of companies recognise the risks associated with using generative AI, yet only 9% say they’re prepared to manage risk.

Remy puts it clearly: “There are a lot of people that use AI, but don’t really know what it does and how it does it… they share sensitive information over ChatGPT.”

AI literacy helps bring usage out of the shadows. It gives teams clear boundaries, safe practices, and confidence in what to use and when.

Reduce the risks of AI use  

AI creates opportunity, but also real risk when people don’t understand how it works.

Without the right skills, organisations are exposed to:

• Data breaches: employees may input sensitive company, customer, or financial data into AI tools. That data can be stored, processed, or exposed through external systems, creating serious compliance and security risks.

• Bias and discrimination: AI systems learn from historical data. If that data contains bias, the output will reflect it. This can lead to unfair decisions in areas like hiring, promotions, or performance management.

• Over-reliance on AI outputs: employees may accept AI-generated answers without questioning them. This reduces critical thinking and can lead to poor decisions based on incomplete, misleading, or context-free information.

• Hallucinations and inaccuracies: AI can generate confident but incorrect information. Without the skills to verify outputs, employees may act on false data, damaging credibility and decision-making.

These risks aren’t theoretical. They show up in everyday work:

• A recruiter uses AI to screen CVs and unintentionally filters out qualified candidates due to biased training data

• A lawyer submits an AI-generated legal brief that includes completely fabricated case references

• An employee pastes sensitive client data into a public AI tool to “speed things up”

• A marketer publishes AI-generated content without proper review, resulting in inaccurate or off-brand messaging

As Iris highlights: “I’m seeing a lot of employees making decisions based on AI-generated content, without questioning the source, the data, or the logic behind it.”

AI literacy helps employees spot these issues early. It builds the judgment needed to use AI as a support tool, not a decision-maker.

The 3-phase roadmap for building AI literacy  

Now that we’ve set the groundwork foundations, let’s dive into the three-phase approach on how to build an AI literate workforce.  

Phase 1: Establish the baseline  

It’s always handy to do a sanity check, before approaching or implementing AI literacy training programmes.  

What’s the baseline reality of AI use and adoption across the company?

Answer questions like:

• What tools are employees already using?

• Where are the current and potential risks?

• Who in your company needs training first?

Then, once you’ve understood where you are in terms of AI maturity, define what AI literacy means in your organisation.  

Erica said:

“Your people need to feel engaged – it's the ‘what’s in it for me’. You’ve got to have some great leadership steering on this from the top-down.”  

“I often see and hear that the transformation lead or IT lead is promising or committing to the C-Suite that they’ll be an 80% take-up of AI tools in 90 days. And then, the 90 days come and go, and the reality has only been 30%.”

Phase 2: Roll out structured training  

The deployment phase is where most AI literacy initiatives will either gain momentum, or stall completely.  

So, when thinking about how to build an AI literate workforce, consider these best practices for structured training:

Role-based learning

This ensures that employees engage with AI capabilities that directly impact their responsibilities, making adoption immediate and relevant.  

For instance, a sales development representative (SDR) might want to understand how to use AI for prospect research or personalised outreach.  

Remember: it’s vital to connect AI to employees’ daily work. This helps learnings more tangible.  

Safe experimentation

How are you fostering a culture of trust via responsible AI use?  

It starts with transparency.  

Gather regular feedback loops and provide avenues for your people to use AI safely, as part of the training programme.

Foster a culture of psychological safety whereby your workforce feels safe and secure to be open and honest about how they’re using AI.  

When employees are guided on how to develop AI literacy, it removes knowledge barriers and demystifies AI.  

L&D and HR leaders have a responsibility to enable their employees to embrace this form of change management.

Erica agreed, stating:  

“It’s about reassuring employees that the company wants you to experiment and innovate with it. Give your people the time and space to really embrace AI and realise how it could elevate their roles.”

Phase 3: Embed and evolve  

Reinforcement

This is important to ensure that AI literacy shifts from short-term awareness to long-term learning.  

One way you can do this is through reinforcement tactics, such as identifying an AI champion within your organisation.  

Remy explains:  

"Find someone who is powerful enough in the organisation to align across the organisation how and why AI can be used. This approach means you can get everyone up to speed on the same page and ensure that guardrails are being followed consistently."

Find someone who can be trusted to lead by example and demonstrate the fundamentals of AI literacy to other peers and colleagues.  

This helps build a concrete learning culture around AI literacy.

Measurement

With any effective AI literacy training programmes comes proving ROI of learning.  

Track leading indicators like:  

• Tool adoption rates: how many employees are actively using approved AI tools in their work? How are they being used?  

• Confidence self-assessments: do employees feel empowered to use AI effectively in their roles?  

• Cross-functional communication: it’s that knowledge sharing piece we covered earlier. Are your employees sharing strategies and learning from each other?  

Policy alignment & ongoing updates

Proactiveness makes for a strong approach when considering how to build an AI literate workforce.  

Revisit the training programme frequently, to ensure there’s alignment with evolving policy and regulatory changes.  

This is how you’ll ensure your organisation’s AI maturity improves over time.

For instance, ensure that employees can see when training content was last updated, and what’s changed.  

Furthermore, create channels for learners so they can report outdated examples, missing examples, or confusing perhaps even irrelevant content.  

How to scale AI literacy across the workforce

Understanding how to build an AI literate workforce starts with knowing how efforts will scale.  

Choose the right training format

This is especially important given we’ve established that AI literacy skills are for everyone, including non-technical employees.  

And that means flexibility and building a training programme that is accessible and your employees want to complete.

Neha Lagoo Ratnakar, Educational Designer at GoodHabitz, explained more in the video below.  

So, for the context of AI literacy training for employees, what does this look like?  

Well, it’s rooted in short, practical learning formats. This allows for continuous learning, rather than one-off, or ad-hoc training.  

And here’s the real challenge: AI and regulations around AI are evolving faster than training cycles.  

So, how do you keep training current without constantly pulling people off the job for updates?  

How do you avoid your training becoming outdated before the next cohort completes it?  

The answer lies in microlearning, that can be deployed quickly.  

For example, Goodlearn offers a gamified training app with scalable rollout.

It’s designed for non-technical employees, focused on real-world, practical workplace scenarios.  

You also need organisational buy-in that AI literacy isn't a project with an end date—it's an ongoing capability that requires sustained investment.

Iris shared more:  

“Goodlearn gives people a simple and safe way to build confidence, knowledge, and the skills needed in order to work with AI in a responsible and effective way.”  

“With GoodHabitz employees, we saw that it helped reduce anxiety and replace it with curiosity and excitement, as Goodlearn was implemented into day-to-day work.”  

Watch the video from Remy to discover more:  

And if you’re after supplementary, deeper skill development?  

With lessons covering AI for professionals through to Generative AI use cases for managers, our online library provides comprehensive training to help your employees (regardless of their roles) apply AI safely and confidently.  

Discover GoodHabitz’s AI literacy training library

Common mistakes organisations make  

Organisations investing heavily in AI literacy training programmes are also facing common mistakes.  

And often, most companies don’t realise these errors until it’s months into rollout.  

With that in mind, here are three mistakes to consider:

Treating AI literacy as a compliance tick box  

The moment you frame AI literacy training programmes as ‘just another compliance training’ that your employees need to complete, you’ve already lost momentum.  

AI literacy skills are vital to ensure organisations are ready with regulations, such as the EU AI Act, as well as proposed legislation in the UK and US.  

However, it’s important to reiterate that AI isn’t a separate domain, like other compliance topics.  

It’s woven into the fabric of how your employees work daily.  

From mistakes in generative-AI written emails to the next customer interaction handled by your AI chatbot.  

The risk remains ubiquitous.  

Overestimating employee understanding  

Usage isn’t the same as adoption.  

Just because your employees have been using tools for months, doesn’t mean they’re AI literate.  

Organisations tend to overlook the responsible use of AI.  

Remy emphasised:

"Throughout the whole process of AI implementation, be accountable, fair, informed, safe, and transparent… It’s no longer 2022, where AI was in the Wild West era. Anything that we try out now has far reaching implications."

On a more practical and tangible level, an example of irresponsible AI use could be an employee working in healthcare, who inputs sensitive data like health information or addresses, into unsecure AI tools.  

The fix? It can be as simple and powerful as ensuring your employees’ data controls in ChatGPT are turned off, so the AI doesn’t train on commercially sensitive data.

The bottom line?  

There is a balance between human accountability and AI assistance.  

Blocking instead of enabling employees  

At first glance, the idea of blocking certain tools can be seen as an attempt to manage risk.  

But it’s counterintuitive, because you end up creating a shadow AI ecosystem where you’re left in the dark on the tools your employees are using.  

The fix?  

Transparency.  

That is at the heart of how employees can successfully disclose or explain AI usage.  

Iris shared more on how this operates at GoodHabitz, with the example of employees using unapproved tools:

“Instead of blocking everything, we chose a more open and supportive approach. We invited anyone who was already using an external tool to share it with us.”  

“We then checked if it could be used safely. If it passed the safety check, we made it available to the whole organisation so others could benefit too.

"This built trust, reduced risks, and showed that our goal was to help people work smarter — not to police them.”

The takeaway?  

Empower rather than restrict.  

Frame AI literacy as enabling employees to use tooling confidently, rather than scaring them away.

It all links back to the importance of fostering a culture of psychological safety.  

Furthermore, documentation and clarity matter.  

Not only can your current employees consistently upskill and be aware of AI tools and how they should be used, but it’s also for the benefit of new employees who are onboarding.  

It becomes embedded in the processes and practices of an organisation, versus becoming an afterthought.  

Conclusion: AI literacy is a long-term capability, not a project

We’ve covered a lot of groundwork, so let’s recap the core points around how to build an AI literate workforce:

1. AI literacy is the ability to responsibly and effectively use AI in day-to-day work.  

2. AI literacy is a vital business requirement due to 3 core reasons:  
   
   1. Improves employee productivity
   2. Ensures no shadow AI use  
   3. Reduces business risks such as data breaches or reputational damage

3. There are three phases to ensure successful roll-out of an AI literacy training programme:  
   
   1. Establish a baseline
   2. Roll out structured training  
   3. Leverage data to report, improve, and reiterate

4. To scale AI literacy training, consider what the right training format is for your learners. For instance, hands on practice versus deeper, theoretical training.  

Erica offered her final two cents as to why understanding how to build an AI literate workforce is so vital right now:

“There’s a lot of negative connotations around the term ‘artificial intelligence’. And the reality is that this isn’t just another business transformation or tech deployment.”  

“Yes – building a training programme IS important. But how are the feelings towards AI in the working environment? Especially if you consider the context of multi-generational workforces, for instance.”