GenAI and quantum computing feel like they’re pulling us out of an era when computers were reliable. You put in inputs and get consistent, predictable outputs. Now? Not so much.

Both tease us with incredible potential but come with a similar problems: they’re unreliable and hard to scale.

Quantum computing works on probabilities, not certainties. Instead of a clear “yes” or “no,” it gives you a “probably yes” or “probably no.”

Generative AI predicts based on patterns in its training data, which is why it can sometimes be wildly wrong or confidently make things up.

We’ve already opened Pandora’s box with GenAI and are needing to learn to live with the complexities that come with its unreliability (for now at least).

Quantum Computing? Who knows when a significant breakthrough may come.

Either way it feels like we’re potentially entering an era where computers are less about certainty and more about possibility.

Both technologies challenge our trust in what a computer can do, forcing us to consider how we use them and what we expect from them.

So, is “computer says maybe” the future we’re heading towards? What do you think?

I want to share a story I often use when coaching new leaders – a personal anecdote about a lesson I learned the hard way.

Back when I was at university, I spent a couple of summers working as a waiter in a restaurant. It was a lovely place – a hotel in Salcombe, Devon (UK), with stunning views of the estuary and a sandy beach. It was a fun way to spend the summer.

The restaurant could seat around 80 covers (people). It was divided into sections and waiters would work in teams for a section.

I started as a regular waiter, but was soon promoted to a “station waiter.” This role had to co-ordinate with the kitchen and manage the timing of orders for a particular section. For example, once a table finished their starters, I’d signal the kitchen to prepare their mains.

Being me, I wanted to be helpful for the other waiters. I didn’t want them thinking I wasn’t pulling my weight, so I’d make sure I was doing my bit clearing tables.

Truth be told, I also had a bit of an anti-authority streak – I didn’t like being told what to do, and I didn’t like telling others what to do either.

Then it all went wrong. I ordered a table’s main course before they’d finished their starters. By the time the mains were ready sitting on under the lights on the hotplate, the diners were still halfway through their first course.

If you’ve worked in a kitchen, you’ll know one thing: never piss off a chef.

I was in the shit.

In my panic, I told the other station waiter what had happened. Luckily, they were more quick witted than me. They told me to explain to the head chef that one of the diners had gone to the toilet, and to keep the food warm.

So I did.

The head chef’s stare still haunts me, but I got away with it.

That’s when I realised what I’d been doing wrong. My section was chaotic. The other waiters were stressed and rushing around, and it was clear that my “helping” wasn’t actually helping anyone.

My job wasn’t to be just another pair of hands; it was to stay at my station, manage the orders, and keep everything running smoothly. I needed to focus on the big picture -keeping track of the checks, working with the kitchen, and directing the other waiters.

Once I got this, it all started to click. People didn’t actually mind being told what to do, in fact it’s what they wanted. They could then focus on doing their jobs without feeling like they were also panicking and running around.

What are the lessons from this story?

The most common challenge I see with new leaders is struggling to step out of their comfort zone when it comes to delegation and giving direction.

Leadership is about enabling, not doing. Your primary role isn’t to do the work yourself; it’s to guide, delegate, and create clarity so your team can succeed. Trying to do everything means you’ll miss the big picture, creates confusion and stress.

It’s tempting to keep “helping” or to dive into the weeds because it feels safer. But that’s where things start to unravel – and where many new leaders experience their own “oh shit” moment.

And remember, giving direction doesn’t mean micro-managing, it’s about empowering. Set clear priorities, communicate expectations, step back and allow people to do their jobs.

And yes, sometimes it’s OK to be quite directive – that clarity is often what people need most.

Are GenAI copilots helping us work smarter – or just faster at fixing the wrong problems? Let me introduce you to the concept of failure demand.

The most widespread adoption of GenAI is copilots – Office365 CoPilot and coding assistants. Most evidences suggests they deliver incremental productivity gains for individuals: write a bit more code, draft a doc faster, create a presentation in less time.

But why are you doing those tasks in the first place? This is where the concept of failure demand comes in.

Originally coined by John Seddon, failure demand is the work created when systems, processes, or decisions fail to address root causes. Instead of creating value, you spend time patching over problems that shouldn’t have existed in the first place.

Call centres are a perfect example.

Most call centre demand isn’t value demand (customers seeking products or services). It’s failure demand: caused by unclear communication, broken systems, or unresolved issues.

GenAI might help agents handle calls faster, but the bigger question is why are people calling at all?

The same applies to all knowledge work. Faster coding or document creation only accelerates failure demand if the root issues (e.g. unclear requirements, poor alignment, unnecessary work) – go unaddressed.

Examples:

– Individual speed gains might mask systemic problems, making them harder to spot and fix and reducing the incentive to do so.

– More documents and presentations could bury teams in information, reducing clarity and alignment.

– More code written faster could overwhelm QA teams or create downstream integration issues.

There’s already evidence which suggests this. The 2024 DORA Report (an annual study of engineering team performance) found found AI coding assistants marginally improved individual productivity but correlated with a downward trend in team performance.

The far bigger opportunities lies in asking:

– Why does this work exist?
– Can we eliminate or prevent it?

Unless GenAI helps addressing systemic issues, it risks being a distraction. While it might improve individual productivity, it could hurt overall performance.