David James is CLO at 360Learning, host of The Learning & Development Podcast and former Director of L&D for The Walt Disney Company.
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It doesn’t take a clairvoyant to know we should all be keeping an eye on generative AI this year—and beyond. It will undoubtedly lead to even faster ways of creating new and novel content by turning simple text instructions into images, videos and audio clips.
But will this be a good thing? I predict a huge deluge of content that is easy to create but sits in learning platforms untouched and unwanted.
So, how do we ensure that we in L&D look back in 12 months and know that we made the right decisions, did the right things and made the right impact? The answer is, we have to plan to.
5 Ways To Make Demonstrable Impact In 2024
Here are five elements that should be part of your 2024 L&D plan.
1. Outcomes, Not Just Inputs
This is a different type of conversation and one that may catch your stakeholders by surprise—in a good way. Stakeholders ask for training and expect to get it. When we say "Yes, and what would you like it to achieve?" we’re starting down the path toward impact, because if we know the specific and observable outcomes that are required, then we can determine the right approach.
Of course, we don’t tell a stakeholder that they’re not getting the training they asked for because there’s no need for it. But if we want to hold ourselves accountable to real results, then we must get past the request for training and explore with them the question, "What for?"
2. Solving Real Problems
We’d like to assume that every request for training is legitimate and well-founded, but some aren’t—and we have to figure out which are which. In my experience, about 80% of training requests are surfaced because there is a legitimate need. If we can understand what the consequences are for things continuing as they are, then we can validate the need and proceed, knowing we can make planned and demonstrable progress.
But, the other 20% of requests are not looking to make more than a cursory difference. It’s up to us to determine the difference between initiatives with hard measures and those without.
3. Seeking Evidence
If we’ve been asked for training, we are rarely afforded the opportunity to undertake rigorous—or even enough—analysis in order to design complete solutions in one go. But that’s okay. The important thing is to analyze from the very first interaction and start to understand the experience of those you’ve been tasked to influence—those responsible for the work, development or change.
The good thing is that this evidence-gathering stage can look like the training. In response to the request from your stakeholder, you can say "Yes, I can run a program for you, and we can start now. Can I have the team for a couple of hours next week?" This looks incredibly efficient to your stakeholder and gives the impression that you’ve understood the brief from the start. If what you actually do in that time is a discovery session to understand the team's current experience, their blocks to achieving what they’re expected to and what they think they need, you can both continue the exploration and begin the solution in that short session.
I’ve done precisely this in the past and been told by one participant that it was the best training course they’d ever completed. It wasn’t a training course, as I’ve laid out above. But it was so much of what people value most about a training course: time out from work and an opportunity to review how things are going, to discuss with peers how things could be better and to create a shared action plan for improvement. At the same time, you can collect an enormous amount of useful and relevant data before recommending to your stakeholder what else needs to be done.
4. Smart Technology
We in L&D are being tasked with making a planned and demonstrable impact on skills gaps and shortages in our organizations, but the planning and assessment part of this is often onerous and out of date almost as soon as it’s done. The absence of skills mapping and skills assessments means that almost all parties are running blind and working too inefficiently to solve these urgent problems in any meaningful way.
However, applying generative AI to the analysis of job roles and company structure means that entire skills ontologies can be mapped and benchmarked both internally and externally, providing career paths for employees and an understanding of their current level of capability and how it compares to the required level. This switches on the lights for L&D and employees and provides valuable data for business leaders, helping the organization understand where it is and how far it needs to go to have a deep talent pool to draw from to truly address its skills gaps.
5. Collaborative Learning
Addressing real problems requires solutions that guide and support employees to do what’s required. This cannot be accomplished through a learning strategy that uses generic content to tell people how to perform highly contextual tasks. What’s required is a collaborative approach, where those who need to develop do so with the benefit of knowing how to grow in the context of their organization.
We have to admit that every organization is different and culture is an integral part of understanding how to do the right things. So every employee can benefit from how others already exhibit the expected and rewarded behaviors. This used to be a time-consuming exercise, but generative AI can now turn proprietary documentation into valuable resources in a few short minutes.
Conclusion
My final prediction for 2024 is that we will see case studies from internal L&D teams that have incorporated generative AI into their practice and are achieving enterprise-wide impact as a result.
Will this be you?
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