AI Didn't Create These Problems, It's Just Turned Up the Volume

Last month alone, I spoke with 18 leaders across People, Operations, and AI all involved in increasing AI adoption and building AI enablement strategies in their respective businesses. Personally, I’m very interested in hearing about the approaches for the people doing the work, the opportunities, how their work is changing and where any challenges are arising, but I must admit, I’m starting to get tired of hearing about the problems AI is supposedly causing, without any real plan on how to move forward.

Don’t get me wrong, I’m very aware that AI is having a huge impact in all facets of the workplace, but I think we’re spending so much time talking about the disruption that we’re consistently asking the wrong questions.

Every conversation seems to bring up the same things – the confusion it’s causing, the concerns about time-constraints, the friction in teams, the many question marks. Everyone seems to want the solution, but is it just me or are some people more committed to talking about the disruption than getting close enough to understand where AI genuinely helps and where it doesn’t? We seem to be looking for universal answers to what are actually very specific organizational questions.

As someone who used to find talent in the AI space, and now advises and coaches leaders increasing adoption and enablement across their organizations, I talk about AI a lot, and I think (or at least hope) we’re all now coming to terms with the fact that it’s here to stay, will continue to evolve. AI has been invested in, and that cost doesn’t come without an expectation to actually use it, but using AI isn’t the same as understanding where it creates value, and that’s where I think we’re getting stuck.

The people I spoke to sit in different industries, with very different priorities, but they all seem to be tackling the same question - what are we actually supposed to do now?

The answer for me isn’t to ask where AI fits; it’s to ask what your business is trying to achieve first. Are you crystal clear on your business and team priorities? Then look honestly at where AI can help and where it can’t, and where a human should still be doing the work.

So why did I feel compelled to write this, and why does it matter? Well, after all the conversations I’ve had, one theme is glaringly apparent – a lot of what we are calling an AI problem is actually a clarity problem, and AI isn't just exposing this, but it's now making poor leadership expensive.

I’ll give you the counterargument too, because it’s a very fair one. Some of this is not a leadership problem, but a data problem. Systems that don’t talk to each other, data that’s messy or sitting in the wrong place, and infrastructure that was never built with AI in mind. These are very real issues, and it’s not something a leadership conversation fixes on its own.

But I will say, when you dig into why the data is messy in the first place, you often find a clarity problem sitting underneath it too. Nobody was clear enough on priorities to decide what data mattered, who owned it, or what it was for. So even when the data problem is technical, a lack of clarity often makes it harder to solve.

What I’m seeing now is that AI is doing two things at once. It’s bringing new challenges to the workplace, such as role redesign, loss of connection, loss of job satisfaction and widening skills gaps, but it’s also amplifying issues that were already apparent in businesses, but are now too loud (and expensive) to ignore.

• Decision-making with incomplete information – not new.

• Leading through change and uncertainty – not new.

• Clearly communicating what’s happening and why – not new.

• Upskilling your team to support growth – not new.

• Deciding what data actually matters – not new.

None of these became leadership requirements because of AI, but they have become impossible to avoid.

So why are we treating AI as the root cause for these as if they’re brand new? I think it’s because the leadership infrastructure gap has never been this visible before.

Here’s my hot take: I don’t think we’re tired of hearing about AI because there’s too much AI. I think we’re tired because we’re still treating AI as the story, when it’s not. It’s here and it’s real.

The reality is AI is exposing how our organizations communicate, make decisions, develop leaders, and adapt to change. Many of those challenges were already there, and AI has amplified them to a volume that attracts attention.

The companies that succeed won’t necessarily be the ones with the best AI. They’ll be the ones willing to fix what AI has revealed, while also responding to the genuinely new challenges it is creating. This means prioritizing the upskilling of managers and leaders not just in the technology but in setting clear expectations, making decisions without all the info, and leading their teams through uncertainty.