5 Ways to Use AI in Workforce Training 

Whether it’s a Wall Street investment bank or a community nonprofit, every organization likes to save money: and one of the biggest expenses for most organizations is their employees. 

This fact has fueled concerns that organizations will try to use AI to replace humans. It’s a fear that’s already being realized at companies like UPS and IBM, and even when organizations aren’t replacing people with AI robots, they might try to use AI to bring down labor-related costs in other ways: case in point, workforce training.  

In our company’s work helping organizations design and deliver training programs, we’ve seen many clients and competitors get excited over AI tools that promise to reduce the cost of training by cutting human curriculum designers, instructors and coaches out of the equation, and trusting machines to build their people’s skills.

But are organizations wise to do this? Are they simply being innovative and forward-thinking by adopting tools that promise to convert process manuals into e-learning courses or listen on employees’ Slack/Teams conversations to offer advice? Is this the latest stage of productivity-enhancing technology – stretching back from the wheel to the World Wide Web? Or is this another manifestation of many organizations’ ugly tendency to undervalue employee development?

To answer that, let’s look at a few use cases for incorporating AI into workforce training, and the pros and cons of each.

1. AI as a Content Creation Tool

If we’re honest, a lot of the training content developed by organizations may as well have been written by machines. Online e-learning catalogs like Udemy and even LinkedIn Learning are overloaded with cheap courses on workplace safety, cybersecurity, basic project management, or anti-harassment, which reprocess and repackage the exact same information with varying degrees of quality.

And if all we want to do is reprocess and repackage widely available information – that’s something generative AI does extremely well! Where a human course designer might spend an entire day Googling, copying, and pasting from Wikipedia and competitors’ courses to assemble a “new” compliance course, AI can spit out a mostly-finished script in the time it takes a human to fix their first cup of coffee.

So if your organization just needs to churn out the same “standard” training courses every company provides, then tools like Articulate 360 AI and Learnworlds AI might be a viable option.  

But what if you need a course on a topic that hasn’t been covered ad nauseam on the web?  For example, maybe your company developed a completely new product or your consulting firm wants to train clients on an innovative new procurement process – what then?

The challenge with these higher-level course design tasks is that generative AI doesn’t actually “understand” or “write” things – rather, it analyzes a piece of text (e.g. your bullet points for a typical occupational safety course), compares them to other examples (e.g. every other occupational safety course on the web), and then predicts what should come next.  

This means that if your subject is something unique or new, the AI won’t have enough reference points to make accurate predictions, leading to degraded output – and even the dreaded phenomenon of “AI hallucinations”, where the machine starts making up “facts” as it goes.  

So, in short, AI content creation tools are worth checking out for your mundane training needs, but proceed with caution if you’re developing training on anything unique, complex, or new.

2. AI as an Interactive Learning Tool

We’ve discussed how AI can help produce “traditional” learning content – such as e-learning or instructor-led training presentations – quickly and cheaply. But AI also enables other, fundamentally new ways to help humans learn complex skills. 

Organizations have used live role plays and simulation exercises as a training method for years – however it tends to be extremely time and resource intensive. For instance, a sales coach might cost hundreds of dollars per hour to lead salespeople through client role plays, and a government agency or international organization such as the WHO might spend millions to orchestrate a simulated disaster or epidemic that involves hospitals, public health agencies, police, and fire departments – with real actors playing infected patients or earthquake survivors. And that doesn’t even count the preparation time to write scripts for all of the scenarios.

And while organizations have long used computer-based simulations as a lower cost alternative, traditional e-learning was limited to point-and-click, “choose-your-own-adventure” (branching path) conversations and simulations that only present a limited range of choices.

Traditional “Branching Conversation” Activity

AI, however, has no such limitations. Simulations created with generative AI can produce an infinite variety of scenarios with zero pre-programming or prep time, and allow the user to enter whatever course of action they like to take – making the experience feel much more immersive and “real” (as long as the AI interaction designers know how to establish rules for the simulation). This is true regardless of the topic and task, whether it’s helping developers practice interviewing for tech jobs, simulating the response to a wildfire, or imagining the nuances of a high-stakes sales negotiation.

3. As a Learning Recommendation Engine

While the mania around AI has intensified in recent years, the technology really isn’t new: people have been using AI tools by other names for decades – whether it’s the assistants in our smartphones or the computers that predict hurricanes and typhoons.

In the world of workforce training, companies have been using “predictive analytics” to assign course content to learners for years, the same way YouTube and Netflix recommend movies and Amazon presents products you might like. Learning Management Systems like Absorb, Docebo, and LearnUpon all offer tools that can recommend specific content and courses to learners.

And while it might seem like a cheap marketing ploy given how everything is called AI now – the label is actually perfectly appropriate in this case. Course recommendation engines have always been AI tools – most of us just weren’t aware of that until now. 

4. As an On-Demand Coach

So far we’ve reviewed how AI can create, enhance, or assign formal training courses – however, AI can also be useful for informal on-the-job learning and support.  

While companies have been using chatbots to answer employee questions for years, older models could only offer a limited range of responses based on keywords – making interactions frustrating if employees had complex, uncommon, or nuanced questions.  

By contrast, generative AI’s ability to engage in natural conversations can provide an experience more akin to interacting with a real, human coach or more experienced coworker – with the advantage of being low-cost (compared to a human), available 24/7, and capable of retrieving massive amounts of information about nearly any question a user can think of instantaneously.

That said, generative AI coaches still require programming (“prompt engineering”) – however that programming often has less to do with the content of the interactions than with the style of interaction.

For instance, our company developed a virtual coach for a corporate leadership program that transitions from empathic listening to collaborative problem solving to helping the user talk through situations and develop their own decision-making skills – just like a skilled human coach. The prompt contained very little specifics about leading a software company versus a hotel chain, instead focusing on more abstract logic like, “If the user appears to already know what they need to do in order to solve their problem, offer some words of inspiration or encouragement, possibly shifting towards advice if they need to clarify their course of action…”

Similarly, when making an otherwise straightforward IT policy chatbot using generative AI, we added some logic to have the chatbot ask the kind of follow-up questions a human IT professional might ask, for instance, “If it appears that the user might have already violated company IT policies, ask non-judgmental follow up questions to confirm whether a violation has taken place, then offer guidance on how the user can get back into compliance…”

The key here is that AI already has access to more information than any human instructor or coach could ever retain: where it needs help is how to present that information to people.

5. As a Job Aid / Productivity Tool

While we’re excited about AI’s potential to enhance workforce training, sometimes what workers need isn’t more training but rather a quick reference guide or a better set of productivity tools.  And AI can help in these areas as well.  

AI-powered productivity tools are already seeing great commercial success. Perhaps the most famous is Grammarly, which not only provides users with feedback on how to improve their writing but can also create drafts of emails on its own. Similarly, GitHub Copilot – which generates programming code on-demand – has been widely adopted in the software industry, allowing developers to bypass the usual hours of internet research to debug problems and simply ask the AI for guidance on their code.

And while some educators worry tools like Grammarly will prevent people from learning how to write properly – or that AI-based medical diagnosis assistants might cause human doctors to get lazy about patient interactionsvarious research studies suggest that, if used with the right mindset, these tools can improve people’s innate skills over time. For example, Grammarly can provide comprehensive writing feedback that can be used to inform the development of stronger, more versatile writing skills. 

AI Training – Convenient Cost-Cutting or a Tool to Take Learning Further?

Hopefully, this article has provided a useful overview of the current state of AI as a workforce training solution. In the end there’s no one “right” way to leverage AI in your training programs: it’s more a matter of looking at your organization’s current challenges, and figuring out which use cases are most appropriate for your team.

If you’d like to discuss how your organization can leverage generative AI for training, or develop custom solutions, please feel free to reach out to Sonata Learning for a consultation.


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