Almost every manager out there can name two or three people on their team that they’re legitimately developing. They get PowerPoint guidance, if not a formal development chat. The rest get a quarterly check-in and maybe access to that shared folder of PDF modules. This isn’t neglect – it’s math. There are only so many hours, and personalized coaching doesn’t scale when you’re the manager of 20, or 50, or 100 people. AI doesn’t solve the leadership part of that problem, but it does solve the scalability part. And that’s the distinction worth building your strategy around.
What AI Actually Does In A Training Context
Before you start pushing anything out the door, it’s easier to navigate the hype and confusion if you’re brutally straightforward about where AI starts to pay off, and where it doesn’t. The most obvious applications tend to look like three things: figuring out what people need to learn, serving up the answer at the best possible moment for it to be absorbed, and keeping an eye on whether it did the job. Skills gap analysis tends to be where the lights get switched on. Instead of relying on annual performance reviews, which are subjective, delayed, and often shaped more by recency than reality – AI can pool together your role evaluations, project outcomes, even workflow data to see where your team is falling short of what your business actually needs from you in the next 12 months.
Most often, those business estimates indicate that 44% of your team’s skills will be irrelevant in the next five years (source: The World Economic Forum, Future of Jobs Report 2023). That’s not some disconnected future, by the way. That’s a planning problem sitting on your desk top this second. When the shortages are identified, adaptive learning platforms can create entire tailored curricula for you, without L&D susceptibility to creating everything themselves at some stage for every single role or level. Generative AI can handle a bundle of the early heavy lifting around drafts, scenarios, and assessments and leave you developing in days what formerly took you weeks in the form of instructional design. If you’re comparing tools at that stage, understanding what’s available specifically for AI for employee training will save you from evaluating platforms built for use cases that don’t match your needs.
The Parts Managers Still Own
Where integration goes wrong is when you hand your L&D function over to the algorithm without maintaining control. These AI systems are trained on recorded data and if that data is biased, the AI will make biased suggestions at scale.
For example, if more men than women in your company have traditionally been promoted into certain roles, and an AI makes suggestions about potential career paths based on that trend, it will automatically suggest those paths less frequently to women employees. This isn’t the AI’s fault. It’s a logical result of the function it has been programmed to perform.
All of this means nothing more than the fact that managers need to have one eye on AI-generated suggestions at all times and regularly question: who is this path not being offered to?
Human-in-the-loop review isn’t an optional extra though. It’s the difference between having AI as a training assist and AI as the trainer. Today’s versions simply throw up patterns in the data and suggest delivery opportunities. Human operators are required to determine if those patterns are revealing something we want out of our training or something we’d like to correct.
There’s a more subtle point at play as well and that’s the creation of a culture where employees view AI as a spy in the camp, assessing their every move and decision in preparation for reporting back to management. People who see their AI this way won’t engage in training with it in good faith. The answer to this, as usual, is better education: making employees AI literate, showing them how to deploy the tool, what to look for and how to interpret the results. When they feel in control, they won’t feel surveyed.
Measuring Whether It’s Working
Previously, the Return on Investment (ROI) on training was hard to determine due to the amount of time between when employees took a training seminar and when it often impacted their performance and results. Additionally, it is challenging to determine how much performance change is due to training versus other factors. AI training can measure the effectiveness of training earlier in the chain and provide greater detail on links between training and performance. For example, predictive analytics can identify which classes or programs might have the strongest ties to what you want to achieve.
Start With One Problem, Not A Platform
Effective managers who successfully implement AI solutions into workforce development do not begin by making a purchase. Instead, they carefully pinpoint a single, real pain point within their organization – such as unnoticed skills gaps or overly burdensome compliance tracking – and then source a lightweight, easy-to-use tool to address it. This approach ensures that their team has a positive, tangible confrontation with AI as a helpful, rather than disruptive, technology.
Moreover, it ensures that you will have real performance data to consider before any decision to scale the implementation. Ultimately, the objective is not the adoption of AI itself, but rather achieving a more capable, skilled, and engaged team or organization this year than last. AI is simply one of several increasingly effective tools available to help achieve that goal.
Lynn Martelli is an editor at Readability. She received her MFA in Creative Writing from Antioch University and has worked as an editor for over 10 years. Lynn has edited a wide variety of books, including fiction, non-fiction, memoirs, and more. In her free time, Lynn enjoys reading, writing, and spending time with her family and friends.
