What a £75m Incentive Target Taught Me About Decision-Making Under Uncertainty
The number that framed my entire internship wasn't a KPI I chose. It was £75m, the Outcome Delivery Incentive target Severn Trent was working against for AMP8, tied to outcomes like customer experience, environmental performance, and service reliability that get independently assessed against a regulator's methodology. I didn't set that target and I couldn't move it. What I could do was help assess which of the high-impact ODIs (CRI, C-MeX, GHG Emissions) were actually feasible to hit, and how risky each path to hitting them was. That's a different skill than the decision-making most people get taught, and it's the one I actually walked away with.
Most decision-making advice is built for situations where you can gather more information cheaply and the cost of being wrong is contained. Under a fixed external target with a fixed timeline and a very public downside, that assumption doesn't hold. You can't wait for certainty, because the target doesn't move while you wait, and the cost of guessing wrong isn't abstract: it shows up as a number in a report that people outside the building will scrutinise.
Ranking risk by consequence, not comfort
The instinct I had to unlearn fastest was ranking uncertainty by how uncomfortable it felt rather than by what it would actually cost if I was wrong. Some of the ODIs we assessed had highly uncertain delivery paths but low consequence if the estimate was off: you could recalibrate mid-year without real damage. Others had much more contained uncertainty but a huge consequence if the delivery model was wrong, because they fed directly into commitments already made to the regulator. My first instinct was to spend the most time on the ODI that felt the most uncertain, because uncertainty is uncomfortable and it pulls your attention. The better use of time was spending it on the ODI where being wrong was expensive, regardless of how confident I already felt about it.
Uncertainty and consequence are not the same axis, and most bad decisions come from treating them like they are.
That distinction sounds obvious written down. It wasn't obvious in the moment, sitting across from senior stakeholders trying to figure out where twelve weeks of analysis should actually go. The pull toward the more uncertain-feeling problem is strong, because resolving discomfort feels like progress. Resolving consequence is the thing that actually protects £75m, and those two feelings point in different directions more often than intuition wants to admit.
In practice, that meant building a simple two-axis triage before committing real analysis time to anything: how confident are we in the current estimate, and how much does the outcome move the £75m number if that estimate turns out to be wrong. An ODI could sit anywhere on that grid. The one that ate the most of my attention wasn't the one with the scariest-looking uncertainty range; it was the one sitting in the quadrant where our confidence was moderate but the financial exposure if we were wrong was largest, because that's the quadrant where an extra week of work has the best odds of actually changing the outcome. Ranking problems that way instead of by how unsettled they felt sounds mechanical written down like this. It's genuinely difficult to do under time pressure, because the emotionally loudest problem in the room is rarely the same one as the financially loudest.
The other thing a fixed external target forces on you is intellectual honesty about your own confidence. When you're building a case that will be read by senior leadership and eventually assessed against outcomes you don't control, you can't hedge everything into vagueness and you can't oversell certainty you don't have. I had to get comfortable writing “this is likely but not confirmed, here's what would change my assessment” into material going to people far more senior than me, instead of either burying the uncertainty or inflating my confidence to sound more decisive. Both instincts are more common than genuine calibration, and both get punished eventually: vagueness because it's useless to a decision-maker, false confidence because it's eventually wrong in public.
The version of this I see most often now, outside a utilities context entirely, is people treating “I need more data” as a universal justification for delay. Sometimes it is. Often, the honest answer is that more data wouldn't actually change the decision; it would just make the person feel better about a call they've already effectively made. Twelve weeks against a fixed target taught me to ask a sharper question before requesting more analysis: what specific finding would change what we recommend, and how likely is that finding, actually, given what we already know. If the answer is “nothing would change our recommendation,” the additional analysis is comfort, not decision-making, and comfort is a luxury a fixed deadline doesn't let you afford.
What stuck with me isn't a Severn Trent-specific lesson, it's a general one: real decision-making under uncertainty isn't about eliminating the uncertainty, because you usually can't, especially against a deadline you don't control. It's about triaging which uncertainties are load-bearing and putting your limited analysis time against those, while being explicit (to yourself and to the people relying on your assessment) about which parts of your answer are confirmed and which are your best current read. I use that distinction constantly now, well outside anything to do with water utilities or ODI frameworks, because most high-stakes decisions I make don't come with the luxury of waiting for more certainty either.