2: Glass half full
In my first email (subject: Learning & Development 2.0), we acknowledged how AI is forcing L&D to change. We can no longer rely on knowledge transfer and skill building, and we must start to take seriously the environment in which people and their trusty AI tools operate.
To quote someone more profound than me: “what got us here won’t get us there”.
And for most of us, this is confronting. L&D has long relied on capability building as the primary lever for performance improvement, even though it didn’t always work.
Of course, this isn’t solely our fault - the business asks us for workshops and eLearning and courses and videos - because they think that's what they need. We’re usually just giving them what they’ve asked for.
And it's not their fault either! We’re all products of an education system that assumes ‘learning’ happens when we sit quietly, listen to the teacher, and remember as much as we can to pass an exam. So, it’s no wonder training is top of the list when there's a problem - we've all been conditioned by the same classroom model.
But as AI becomes a fundamental part of how we work, we’re facing the reality that the business will rely less on L&D for information - however it's packaged.
And as most work becomes ‘AI augmented’, we’re also realising that when it comes to performance, focusing solely on an individual's capabilities is risky. We must also consider how the work is designed.
Because performance is no longer only down to people - it’s also down to AI.
So, what does that mean for us?
Well, this is where things get interesting. Because the need for L&D to deliver information is disappearing. Why would anyone go looking for content when AI serves up timely, personalised answers?
So, anyone whose career, business or identity is built on content or a knowledge-first approach may soon be forced into a rethink.
Not to mention, the focus on skill building is also being more heavily scrutinised. AI can't compensate for ambiguity like we can. So, as AI takes on more of the work, treating skills as the primary driver of performance becomes more difficult to justify.
So, anyone whose career, business or identity is built on learning experiences or a skills-first approach may also be forced into a rethink.
And whilst that may be a concern (let's be honest, how many of us haven't fallen back on trusty old ‘training’ as a stop-gap solution), to look at the situation from the perspective of what we’re losing is to somewhat miss the point.
Because the need for performance improvement is about to skyrocket!
Why?
Well, because AI is delivering inconsistent results. Individuals are being given powerful new tools, with most still figuring out how to use them in their work.
There are pockets of excellence.
But they’re the exception, not the rule. When you scratch the surface, you find different approaches, duplicated effort and uneven results.
And this isn’t down to a lack of effort. It’s because the work hasn’t been defined.
Sure, there’s the odd occasion we document a process and do it the same way each time. But most knowledge workers operate on the fly - which is why two people using the same AI tools can produce dramatically different results. Or why the same person using the same AI tool can produce different results depending on whether they ate their Weetabix that morning.
So, if success is driven by heroic effort, rather than a systematised, repeatable approach, then of course, performance will be inconsistent (because even heroes have bad days).
And whether or not they realise it yet, businesses are crying out for guidance on defining work so AI can be leveraged effectively (ask any leader about their strategic priorities and they'll undoubtedly mention getting their people effective with AI).
So, instead of fear and panic, let's embrace the opportunity - this is an exciting moment for L&D!
Because who better to define the work than those of us who've spent our entire careers figuring out what drives performance?
More soon,
- Ant
