Here's the problem with how L&D uses AI right now 👉 It's driven by efficiency.
Now there's no problem with being efficient, as long as the thing you're trying to do more efficiently is the right thing you should be doing.
Learn how to find practical uses for AI in this webinar with HowNow's Nelson Sivalingam and Talaera's Mel MacMahon.
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0:00 Effectiveness & efficiency
9:12 Finding problems to solve
18:52 Commercial understanding and tech
22:14 Organisational context
27:27 Is that tech a good fit?
35:22 Personalisation using AI in L&D
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Mel shared a great quote that “it's the difference between doing things right versus doing the right things.”
And it’s not an either or issue, because nobody is going to argue that you shouldn’t do the right things or try to do things right.
“I think the problem is though that it's easier to do things right. Being efficient is easier than being effective. And I think part of the reason for that is we just have much more practice at it.”
Imagine you enter a new role, you get given a set of tasks, and then you’re taught to perform those tasks to reach a specific outcome. Then you can bring in tech to perform them more efficiently as it emerges.
“The problem is that you don't get as much opportunity to practice being more effective to really step back and think, what are the right things that I should be doing or that we should be doing as an organisation?”Â
And Mel believes the rise of AI is an opportunity to address this…
“As organisations, we need to rethink what is effective. What are the right tasks to be done? And I think there's two traits of companies that will not just survive, but excel off the tailwinds of this change.Â
“The companies that understand this is the new paradigm that we're in right now and change needs to happen. And then the companies that are able to capitalise on that change effectively leverage AI to adapt and move forward.” - Mel MacMahon.
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“Recent research by Deloitte said that companies that were predominantly using AI for automation were actually seeing 50% lower return on investment versus organisations who are using AI for strategic benefits.
“And that’s supported by a report that IBM recently put out saying that organisations who are using AI for innovation, rather than just cost cutting, we're six times more likely to see financial gains.” - Nelson Sivalingam.
The trouble is that a lot of early AI adoption has been about being more efficient, which could mean that we’re doing the wrong things more efficiently.
And as the data shows, we need to focus on being strategic and doing the tasks that support the business in reaching its goals.
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One step is simply to broaden your perspective of what’s possible and optimal. When we think we’re already in that position, it becomes easier to prioritise cutting the costs of doing that over finding the true potential of what’s possible.
Take a lecture as an example. One person in front of a class is the most efficient way to distribute information but not the most effective way of learning and achieving the outcome.
“It's somewhat doing what we need, but it's not the global maximum that we need to solve. So that's when we step back and ask ourselves in L&D… What is the highest peak for us?”
And if we can find those complex problems to solve, that’s where we can use AI effectively!
“What is the actual problem that I need to solve that's going to help me have the biggest impact on the organization? And let me leverage and apply AI to be able to tackle that.
“It's not about using AI to climb that small peak, which you could already climb, but AI is helping you climb that peak slightly faster, with a lot less sweat. What you want to do is be able to climb that highest peak that you wouldn't have been able to climb, without the help of AI.” - Nelson Sivalingam.
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“Love the problem, not the solution. And I'm a big believer in that because the problem is what we're there for, it's the motivation, it’s what we're trying to solve. And it's solving the problem that gets us the outcome that the desired outcome that we want to see.Â
And if leveraging AI is going to help you solve that problem, go for it. The trouble is that we’re seeing people trying to fit AI into everything.
“What will help us prioritise how we leverage AI will come down to those big, meaningful problems that we need to solve… and the organisation that will win is the one who knows the customer's problem better than the customer does.” - Nelson Sivalingam
And those fundamentals won’t change, regardless of the latest tech advancement or trend.
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“AI is going to do better with the more data you can provide to it… We talk about personalisation, that comes up a lot with AI, and if you’re going to personalise something, you need know about that person… you need to understand as much as you can about them.”
Start with individual information, like their performance reviews and job descriptions.
But also look at how that person fits into a team. What are the objectives of that team? And how does that team fit into the wider organisation.
“If you have the data infrastructure that's capturing all of this in a structured manner, then you can feed it into AI that really will allow you to be able to generate more personalised learning or understand what different components of an organisation need to learn or need to do in order to get where you want them to be.” - Mel MacMahon.
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“It's people with domain expertise that are going to be able to understand how AI can help them achieve their tasks to be most effective in their role.
“And so in order for somebody to be able to embrace that, then the culture of the organisation needs to be one that will embrace learning, embrace experimentation, embrace innovation, and to an extent, embrace failure because that comes along with experimentation and innovation.” - Mel MacMahon
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At HowNow, we’re helping companies become skills-based organisations, and a huge part of that is helping solve a historically tough challenge.
How can they map and measure skills at the speed and scale required?
“The first question we need to be able to answer is what skills does this person need to have? And to what level of proficiency do they need to have that? And this is where generative AI can really help.
“We essentially look at millions and millions of job market data points to identify what are the tasks that are typically associated with a job…Â
“And knowing which tasks you need to do as a part of your job role helps us infer what the skills are. And what are the required skill levels you need to have in order to be able to execute that task at a level you need.”
After that, it’s about measuring the current skills and proficiency levels to find gaps and closing that with personalised learning - something that AI can help us do at speed and scale.If you want to know more, head here and meet HowNow AI - helping you tackle those big and scary L&D challenges.
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Learn how to map skills for every role, at speed and scale, with HowNow AI
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