Are OKRs improving or inhibiting decision making?
We’ve broken this topic out into a multi-part series:
- Part 1: Setting the stage - surfacing problems with OKRs and decision making (👈 You are here)
- Part 2: Do OKRs hinder decision making in radically uncertain environments?
- Part 3: Exploring complementary techniques for uncertainty and complexity
To start, I figured I’d share my background with OKRs and some ways I’ve personally found them valuable. (feel free to skip!)
My personal experience with OKRs
I’ve been around OKRs (a goal-setting framework developed by Andy Grove at Intel and made popular by John Doerr at Google) since before the publishing of Measure What Matters in 2018 - both in using them with my own teams and designing OKR tooling for enterprise customers at Atlassian.
I remember being introduced to OKRs sometime in late 2017 or early 2018 through working with teams at Tableau and Eventbrite - they were anchoring their strategic planning meetings around this framework and it’s the first time I saw it in action. There were two things that stood out to me (and I think many have had the same experience):
- Teams were having ‘different’ outcome-driven conversations. At the time, everyone was talking about teams ‘knowing the why’ behind work, but the conversations I saw happen around OKRs were the first experiences where I actually saw that happen.
- Decision making and ‘Solutioning’ was decentralized. OKRs created a new type of language for progress - we saw cascading trees of epic/feature/story progress bars traded out for outcome-oriented dashboards. They quickly created an environment of trust just by agreeing on what success looked like.
Looking back it was a watershed moment - a framework that actually produced real results (and quite quickly). It’s accessible, powerful, and like any other framework, it can be implemented ineffectively, but its simplicity is one of its best features. It’s a way to incorporate outcome-focused principles right out of the box.
Since starting the Uncertainty Project, we haven’t talked about goal-setting a OKRs a whole lot, but we have had multiple requests to cover the topic. A few months ago, we wrote a post on exploring goal setting and OKRs through the lens of decision making, but this time, we’re digging into some of the potential pitfalls
And though I am a big fan of OKRs, I’ll try to take a more critical/skeptical perspective for the sake of this post 😈
Note: For what it’s worth, though I’ve worked alongside them (extensively), I’ve never been a coach or consultant implementing OKRs in an organization. My perspective is both through observer (learning for the purpose of building experiences) and practitioner (using OKRs across the company at Atlassian).
The surface problems
When we look at OKRs through the lens of decision making, I think there are some clear problems (already known and felt by most) that can inhibit decision making. They surface situations that make decision making murky and difficult.
Specifically, these include:
- Correlation and Causality: How do we know that what we’re doing is actually the thing that’s moving the needle?
- Leading vs Lagging Indicators: What if the initiatives that look like they aren’t working just aren’t working yet?
- Escalation of Commitment: What if we find out that what we’re chasing is wrong in the first place? We tend to hold on to losing bets.
There are behavioral challenges as well - like ‘set & forget’, poorly defined objectives, and structuring KRs as outputs - but I would argue these problems still plague OKRs even if teams are doing everything ‘right’.
The difficulty with these problems is that they are rooted in human behavior - and in my opinion, tools aren’t all that great at improving human behavior, only augmenting and improving the effectiveness of existing behavior (or forcing behavior - which isn’t improving; it’s introducing friction and stress until something breaks).
If we’re being honest, in most cases, we’re relying on human judgment to correct these issues and each one comes with behavioral traps that we are individually blind to:
- The trap of causality illusions, illusory correlation, and the representativeness heuristic - we tend to see correlation, causation, and ‘meaning’ in data that isn’t there.
- The trap of optimism bias, pessimism bias, and strategic misrepresentation - we tend to misinterpret or overexaggerate signals of success/failure and manipulate information in response to incentives.
- The trap of status quo bias, sunk cost fallacy, and commitment bias - we tend to entrench ourselves in existing pursuits/investments even in the face of overwhelming contradictory evidence.
These traps have little impact on how effective or well-crafted our goals and measures are, but they do impact our ability to achieve those goals. OKRs, in isolation, do not improve our ability to make better assumptions or improve/check human judgment - they provide helpful constraints for decision making (e.g. it’s clear we are trying to achieve x, not y), but they don’t have any effect on our ability to achieve the goal.
OKRs are a tremendous improvement on traditional output-driven project planning, but they weren’t designed as a substitute for strategy - our current situation is the result of the choices we make in our competitive environment, not based on whether or not we crafted the right goals (or even achieved them). The goals themselves are a choice.
This is an intuitive conclusion - two different teams/companies can have the same goal with different capacities to achieve that goal. There are limits to the competitive advantage of ‘crafting better goals’ - and competition in this environment is relative, not absolute. It’s an infinite game.
“Desire (as with hope) is simply not a strategy. The desire to achieve the named key results won’t cause those key results to happen. You may desire the substantial rise in your NPS, but if you are serving customers that your key competitor serves better than you do, your NPS is unlikely to rise — even though you really want it to.” - Roger Martin, Stop Letting OKRs Masquerade as Strategy
Do OKRs hinder decision making in radically uncertain environments?
There are two problems that have interested me recently that were not initially obvious to me - and I think it explains why OKRs, alongside all of their positive features, don’t feel as effective in low-validity, complex domains.
- In navigating uncertainty, particularly in ways that produce asymmetric results, we typically do not start with a ‘known end’ - which is why asymmetric results are obvious in hindsight, but ‘unimaginable’ at the decision point.
- We know that one of OKR’s most powerful features, focus, may have unintended consequences on decision making in these environments.
Next week we’ll walk through a few perspectives on these problems:
- Known/Unknown End and Known/Unknown Means: OKRs articulate a target (or known end) and encourage teams to decide what means are optimal to reach that end. Is this an inherently convergent constraint?
- Causal vs Effectual Reasoning: What do studies on entrepreneurial thinking and explore/exploit functions tell us about goal setting?
- Goal-induced Blindness: We’ll cover some of the major criticisms and counterpoints to goal-setting frameworks
- Techniques to complement OKRs: We’ll tie it all together with potential techniques that may address some of the potential downsides we see with strategic decision making. These include estuarine mapping (Dave Snowden), the concept of Obliquity (John Kay), Design Futures, and others!
If you use OKRs in your own organization, we’d love to hear how you think they help/inhibit decision making - feel free to drop a comment at the bottom of this post or reply to this email!
My intention with this series is to understand the impact OKRs have on strategic decision making - and maybe land on a playbook for dealing with these problems.