A few weeks ago, I had an incredibly generative conversation with Jabe Bloom. One of the hallmarks of a good conversation is that it leaves me with a long list of threads or breadcrumbs that I can explore and dive deeper into after it’s over. 

One of those threads from my conversation with Jabe was John Shook’s work around culture change: 

How to change a culture: Lessons from NUMMI

This short article is well worth a full read buy the key idea is illustrated in the diagrams above and this salient quote: 

The typical Western approach to organizational change is to start by trying to get everyone to think the right way. This causes their values and attitudes to change, which, in turn, leads them naturally to start doing the right things. 

What my NUMMI experience taught me that was so powerful was that the way to change culture is not to first change how people think, but instead to start by changing how people behave — what they do. Those of us trying to change our organizations’ culture need to define the things we want to do, the way we want to behave and want each other to behave, to provide training and then to do what is necessary to reinforce those behaviors. The culture will change as a result. 

Shook’s insight aligns with my personal experience and aligns with other behavior change models that I’ve covered here. With an important caveat. I’d argue that behavior and mindsets (thinking) are tethered together with a rubber band. Change behavior by making changes to the external environment — and mindset changes will follow. BUT make too big of a change, too quickly, and the rubber band will snap, and the new behavior will be rejected. Another good metaphor that we can use here is that of a pressure cooker — the dish won’t cook without (external) heat, but crank up the heat/pressure too quickly, and the whole thing will blow up. Mastery of change requires the ability to sense the sustainable pace of change that won’t cause things to blow up. 

Another challenge I raised was around the “Agile Theater” phenomena — where teams are going though the motions of Agile or Scrum (stand-ups, estimations, etc.) but are not unlocking any of the value. 

Jabe reminded me of the Japanese martial arts concept of shuhari:

• shu (守) “protect”, “obey” — traditional wisdom — learning fundamentals, techniques, heuristics, proverbs.
• ha (破) “detach”, “digress” — breaking with tradition — detachment from the illusions of self.
• ri (離) “leave”, “separate” — transcendence — there are no techniques or proverbs, all moves are natural, becoming one with spirit alone without clinging to forms; transcending the physical.

Teams performing “Agile Theater” are in the “shu” step of mastery, which is a necessary step on the path towards “ha” and “ri”. 

Can we make faster progress by measuring less?

This past week, I got to do something that brought me great joy: integrating and combining several old blog posts in a novel way that led to a new insight.

In my 2020 wrap-up post, I’ve highlighted “building human connection” as an area of interest of mine for 2021, clarifying further than:

I slot some of the more interesting challenges of distributed work into this category and the maturing DEI space which is finally generating some balanced, evidence-based approaches and practices.

The second half of that sentence is the topic for today. I want to argue that if you are a small-to-midsize company (say, under 1,000 employees), and keen on advancing DEI initiatives, you are probably trying to measure too much rather than too little. This inclination to make progress through measurement is deeply encoded in the modern business ethos and further amplified by imitating larger companies where a quantitative measurement is actually a useful tool. I’ve written more about this broader pattern in ending the tyranny of the measurable — this is just a more specific application.

Allow me to illustrate it with an example: suppose a company wants to improve the diversity in its hiring pipeline and reduce bias in its hiring process. Its first inclination would be to analyze its recruiting funnel and see whether different segments of candidates are treated differently.

In theory, that makes total sense. But then reality comes in and bursts our bubble. Legally, candidates cannot be required to provide segment data (gender, race, etc.) as part of their application process, leaving us with data that’s not just partial but also biased. There’s selection bias in the people who opted to volunteer this information (there’s also selection bias in the people who opted to apply to our open role to begin with). Even if we figured out how to overcome these challenges, we would run into the “small N” challenge. Because not many people go through our recruiting process, even if our analysis yields extreme or seemingly different results, they’re not likely to be statistically significant (more on this in surveys: exploring statistical significance). Our analysis is likely to suggest bias in the process even when there isn’t or not find evidence of bias even when bias exists.

So we’re going to cut through a lot of red tape to get the data, bend over backward to somewhat credibly analyze it, only to come up with results that at best are easy to poke holes in and at worse will be misinterpreted.

There’s got to be a better way. And there is.

I started unpacking an alternative way in if we know in which direction we want do go, does it matter where we are? and a few months later in the score takes care of itself.

All recruiting processes have some bias in them by the sheer fact that humans are involved in them. And there’s plenty of evidence that humans are biased. If our company thinks that we’re all some special bias-resistant snowflakes, no amount of data and analysis will save us.

The gist of “the score takes care of itself” approach is to hold people accountable for the behaviors that eventually lead to long-term success, rather than to a defined outcome with a fixed time horizon (like a diversity metric, for example). And as outlined in inclusive organizations change their systems, not just train their people, organizations are better off changing their systems to be more bias-resistant. It’s a lot easier to do than make people more bias-resistant.

Fortunately, again, there’s a substantial body of evidence available to us on how to build bias-resistant processes. The “bias interrupters” model is outlined in the link above, and specifically in the recruiting context, I’ve aggregated additional pieces in inclusive hiring: a short primer.

Back to our example. An alternative approach will be to audit our existing recruiting process against one, or both, of these benchmarks and start implementing a plan to close the gaps. We can be confident that we’re moving in the right direction since we’re basing our changes on battle-tested interventions.

To wrap up, a few words of caution and important caveats:

First, when picking evidence-based interventions, know how to tell the difference between “popular” and “effective”. There are a lot of popular but ineffective practices out there. “Because Google is doing it” is not a good enough reason to adopt a practice. Do your due diligence.

Second, measurement can be useful when used in the right context and in the right way. I’m not saying “measurement is evil, don’t ever do it”. I’m just inviting you when you’re quoting Drucker (“you can’t manage what you can’t measure”) also to keep Goodhart in mind (“when a measure becomes a target, it stops being a good measure”).

A recent conversation with a colleague about a company’s 401k contribution matching strategy triggered an interesting reflection on how equity (fairness) is handled in different company benefits. 

Many companies, caring for their employees’ long-term financial well-being, incentivize employee contributions to their 401k plan by offering to match their contributions up to a certain percentage of their salary (3% and 6% seem to be the most common ones). 

That incentive can lead to some interesting outcomes. Consider 3 employees of AcmeCorp, making $75K, $150K and $300K respectively. AcmeCorp matches its employees 401k contributions up to 6% of their salary. All three are hard-working and care deeply about their long-term financial well-being so they contribute the maximum amount they are allowed to contribute to their 401k each year — currently $19,500. 

• Employee A ($75k) will receive a $4,500 match from AcmeCorp.
• Employee B ($150K) will receive a $9,000 match from AcmeCorp. 
• Employee C ($300K) will receive a $18,000 match from AcmeCorp.  

Same contributions. Very different matches. A cynic may say that this incentive scheme acts as a regressive tax — the more you earn, the more you benefit from it. The less you earn, the less you benefit from it. 

Ironically, the reason many companies opt for this matching scheme is because of regulations by the IRS that require companies to perform annual tests to ensure that the 401k plan does not discriminate in favor of high earners in the company. Failing the test has some pretty painful implications. However, companies that opt to use one of three Safe Harbor plan designs are not required to perform the non-discrimination test. The full match up to x% of salary is one of those three designs…

 Side note: the combined contribution limit (employee + employer) during the writing of those lines is $57,000/year. So assuming an employee maxes out their portion, the employer can contribute up to $37.5k/year extra. Imagine the impact that this can have on long-term financial well-being. 

Zooming out

What’s even more interesting is that when we zoom out, benefits are not handled according to a consistent equity standard. 

Some benefits are regressive. Like the 401k example above.  

Some benefits are progressive/needs-based. Like paid family leave beyond what’s legally required by law. 

Some benefits are neutral. Everyone is getting exactly the same thing. Healthcare premium participation usually falls into this category. Defined PTO plans to some extend (the $ value of a day differs). 

That insight brought me back to revisiting an idea I explored a year and a half ago — the Service-as-a-Benefit (SaaB) platform. 

In addition to the advantages I outlined in the original post, it can also enable companies to create a more coherent benefits strategy from an equity perspective, whichever way they choose to define it. Companies can set a consistent equitable allocation of funds at the overall budget level, instead of at the individual benefit level like they do today. That budget can be set as a % of salary, a fixed sum per employee, or a more complex formula taking individual needs into account. For there, each employee can allocate those funds to the benefits that matter to them the most: a higher 401k match, a lower health insurance premium, a longer family leave, or a sitter for your cat.

Having realized that, I’m even keener about a SaaB platform today, than I was more than a year ago.