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Pain and Consequences: How Jane Austen Might Have Understood the Problem with AI
Why solving the problem in front of us doesn't always remove the pain, and how AI can create consequences nobody anticipated.
I've been thinking a lot about pain and consequences this week.
I've also spent an unreasonable amount of time supervising AI agents, which may explain why it's on my mind.
When AI solves the wrong problem
I had one agent researching 23 different areas. Ten of the outputs looked solid. The remaining thirteen weren't good enough. A subtle change in context meant that much of the remaining research missed the mark.
I spent time checking the work, revisiting the instructions and getting things redone. I'm sure the agent will nail it one day, but for now, it still needs rather more supervision than either of us would probably like.
If I'd passed that research to another agent, it could have carried out the next stage perfectly well. But it would have been working with unreliable research.
A rather efficient way of making a mistake.
Between that experience and conversations with friends about their own AI frustrations, I started thinking about the problems we're actually trying to solve.
People are often good at spotting what's wrong. They're much less certain about why it's happening or what's worrying them.
I've noticed that people sometimes mistake the problem for the pain.
The problem isn't always the source of the pain
Take an executive worried about losing a key team member.
On the surface, that's the problem. But dig a little deeper, and their real concern may be entirely different.
That person's departure could delay a critical programme. The delay could jeopardise customer commitments or financial targets. The executive could have to explain to the board why something they were responsible for failed.
Or, to put it plainly, they might worry about getting sacked.
The potential departure is the problem they've identified. The pain is what they fear will happen as a consequence.
But that still doesn't explain why the business is so exposed.
Perhaps the organisation has become too dependent on one individual, with too much critical knowledge and responsibility concentrated in one place.
Finding a replacement might address the immediate problem without removing the underlying vulnerability.
Sometimes you have to go quite far from the original problem before you find out what's bothering someone.
Pain is a powerful motivator. It shapes people's priorities, the risks they'll take and the decisions they'll avoid.
Often, the anticipated consequences of a problem, rather than the problem itself, create the greatest anxiety.
AI doesn't have to live with the consequences
I don't believe that AI experiences pain. Happy to have that argument another day.
It can reason about the fear of losing your job, missing a revenue target or disappointing a customer. It can describe the emotions involved and suggest appropriate responses.
But it doesn't have to live with the consequences when things go wrong.
People don't always explain what's actually troubling them, even when they're speaking to another human.
And if we haven't properly understood the problem ourselves, what exactly are we asking AI to solve?
What if we've misunderstood the problem and our solution ends up causing trouble somewhere else?
When AI moves the pain rather than removing it
We automate a slow process. Work is completed in seconds, but the person responsible for checking it now has twice as much to review.
We remove repetitive tasks from junior roles. Productivity improves, but we may also remove some of the experiences through which people develop the judgement they'll need later.
Sometimes we've simply moved the pain to somebody else.
And those are only the immediate effects.
What happens further down the line?
I'm most interested in the consequences of those consequences.
A faster process doesn't necessarily mean the business has become more effective.
Removing a bottleneck is of limited value if we've created another one downstream.
I'm still using my AI agents. I wouldn't be investing this much time in them if I didn't believe they could be useful.
But I wonder whether we're becoming so good at finding solutions that we're spending less time understanding the problems, the pain behind them and where that pain might end up.
What Jane Austen understood about good intentions
Which brings me to Jane Austen.
Austen's novels are full of people making decisions based on what they believe others need, want or ought to do.
Her characters are driven by pride, ambition and fear of disgrace. Often, they're convinced they know what's best for everyone else.
They may mean well. It doesn't make them right.
The consequences frequently create far more pain than the original problem.
I suspect she'd have been rather less interested in AI than in the people confidently deploying it to improve everybody else's lives.
We now have the opportunity to make the same mistakes at scale.
About the author
Rebecca Hastings is the founder of Lucent Search and a partner at Owendale Advisory. Her work spans executive search, AI strategy and the organisational decisions that determine whether technology investment delivers business value.
She writes What Strategy Becomes, a newsletter that explores how leadership turns strategy into execution, with insights on technology, transformation, and people.
Originally published in What Strategy Becomes on LinkedIn, 27 October 2026.
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