Bad outcomes often come from solving the wrong problem well

We often say things like:

• Let’s optimise conversion rate
→ we’ll get more sales

• Let’s increase email CTR
→ it will prove our CRM strategy works

• Let’s expand to new markets
→ it will make us look good externally

On the surface, these sound sensible.
They’re measurable.
Actionable.
Impressive.

But this is often the same kind of thinking as saying:

• Let me drastically cut calories
→ I’ll lose weight

• Let me double my exercise volume
→ I’ll see faster gains

• Let me read X books per month
→ I’ll become smarter

Will you see results quickly?
Probably yes.

But will it be:

• Sustainable? → probably not
• Aligned with what you actually need? → often no
• Healthy, ethical, or grounded long-term? → unlikely

Because optimisation is easy when you don’t question the goal.

As AI makes optimisation dangerously easy, the real skill (and responsibility) is knowing when to STOP, reframe, or say no.

And this is where things usually go wrong.

Not in execution.
Not in intelligence.
But in what we choose to optimise for.

We tend to prioritise:

• Short-term wins
• Metrics that signal competence
• Ego-driven incentives (“this will show progress”)

And we quietly ignore:

• Second- and third-order effects
• Lagging indicators (trust, loyalty, resilience)
• Team fatigue when work feels performative, not meaningful
• Whether this effort actually moves us closer to the right outcome

So what should you do instead?

Slow down — before you speed up.

• Interrogate the problem framing, not just the solution
• Ask what success looks like after the metric moves
• Separate “this is measurable” from “this actually matters”
• Create space for uncomfortable questions — especially the ones that threaten the plan
• Stop trying to rationalise every decision
→ you often know more than you can tell or prove (a lot of your knowledge is experience-based, difficult to articulate or codify)
→ trust that knowledge
• And yes — have some fun with it

Good decision-making doesn’t start with optimisation. It starts with sense-making.

With acting —> noticing —> adjusting.
With allowing understanding to emerge while you’re engaged — not waiting for certainty that never comes.

And that’s the scary part:

You can be very good at solving the wrong problem — and still fail.

Speed doesn’t replace clarity.
More data doesn’t fix a flawed frame.

Better outcomes usually don’t come from doing more…

They come from choosing the right problem to solve in the first place.

Agnes Trocinska