Author: Christina

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Organizational Groundhog Day: Why we see the same ‘mistakes’ across our careers

If you’ve been working a while, you’ve probably had more than one corporate déjà vu moment. Maybe it’s a group dynamic gone sideways or a CEO’s self-inflicted wound. They leave you head-scratching about why, despite all the MBAs and consultants (guilty of being both!), we keep ending up in the same pickles. 

This year I decided to step back and take a broader look at organizational dynamics, to see whether their intricacies broadly weave into a few patterns. I reached out to the modal edges of my network. Ok, still mostly MBAs and consultants, but all with 20-45 years of cross-industry work experience, ranging from small to multinational and from startup to mature companies. While current roles skewed towards tech, experience spanned civil engineering and international development, venture capital and law, with titles ranging from manager to Vice President.

I asked these wise business leaders at the zeniths of their careers to look back on their collective experience and consider the same three questions: “What are mistakes you see happen in organizations over and over again? What is causing them? What can you do to change the pattern?”

What emerged isn’t just a simple list, distilled from 22 perspectives. Certainly there are meta themes. But what stood out more was how every interview felt both individual and systemic at the same time. Each deep diagnosis framed not just the endemic challenges each leader was squaring up to, but also what values they held and aimed to deliver. On the whole, when individual choices meet organizational evolution, certain ‘mistakes’ will manifest like clockwork. 

Organizational growing pains: some ‘mistakes’ are inevitable

Sitting down with a seasoned engineer who’d experienced every size and maturity of business, I received a stoical reframing of my premise: There are no mistakes. “Mistakes” we observe are not made in error. Rather, they are “structural artifacts” of an organization’s current stage of growth and maturity. If you group all companies into three simple buckets, here’s what you can expect.

  • 🐣 Small businesses: These entrepreneurs are building the plane while flying it. They make mistakes out of sheer inexperience, learning the “nuts and bolts” because there’s no one else to do the work.
  • 📈 Rapid growth companies: Welcome to the competitive wild west. You need to scale up to meet demand, but then keep outdoing yourself to stay profitable. Rapid growth, chasing the next innovation can be messy. Reaching new levels of scale that require new leadership expertise can also drive churn, contributing to the chaos.
  • 🧓 Large, stable companies: These behemoths are set in their ways, sporting policies and procedures for every scenario, having seen it all over decades. While this might feel like “boring” adulting, it’s how they operate globally at scale. But it’s also how you fall asleep at the wheel and become irrelevant.

Basically, a company making “mistakes” might just be living its truth. And that might bump up against your own goals, values, or preferences. 

Of course, these company maturity categories are artificially clean and binary. And the magnitude and frequency of “mistakes” can still vary significantly. One can certainly take the rough with the smooth, but also try to temper the rough patches. So let’s have a walk through the topics that popped across interviews. What are the most common  — let’s call them problems — and how might we remedy them?

🎶 I got 99 problems but grouped them into 4

The following short list is by no means exhaustive. Consider it the 80/20 of org “watch-outs.” 

Group 1: People problems

Most common: Bad hiring, bad management

Typical culprits: Small businesses, rapid growth companies

Typical causes: Inexperience, time pressure

“Founders are really bad at hiring. They tend to hire someone for roles they are bad at. It’s hard to hire someone for something you don’t know how to do.” – Venture Capital Investor

“I previously worked with [recruiting] during an aggressive expansion. The company was thinking about the ideal way to hire, number of interviews, what you can learn. One issue is hiring incorrectly. It has a profound and long-lasting impact. They stay long and bring on other people who aren’t right too.” – Senior Director, large tech company

The take-aways

1. Invest in hiring and onboarding 

“I valued [my former company’s] time, thought, and structure put into hiring decisions. And it’s a standardized process that helps compare candidates…The moment you let people do their own thing, it’s impossible to hire well.” – Senior Director, large tech company

“Most people don’t think about how to make the person confident and competent in the role. And then check in with that person and give feedback.” – Vice President, mid-sized tech company

2. Expect managers to enable talent at every level 

“[Direct reports] need direction, guidance, coaching, mentoring, apprenticeship. But they don’t need to be micromanaged…You should let them unleash their talent.” – Partner, top global consulting firm

“57% of people have left a job because of a manager. People should take more stock of the people issues vs. just speed.” – Career coach and Founder

“A lot of people hire someone external without creating an environment for success, and it takes too long for them to figure out how to get things done. New hires don’t have enough internal authority to push things through. A sponsor should enforce metrics around the change they want.” – Senior Strategy Manager, large tech company

Group 2: Focus problems

Most common: Unclear mandates, high reactivity 

Typical culprits: Rapid growth companies; large, stable companies

Typical causes: Market pressures, catering to leadership, high novelty or complexity

“I see a lot of reactive solutions. Something gets shrill, the geist of the moment. e.g. change of administrations, AI. There’s nothing wrong with reacting. Sometimes we move too fast to come up with something brand new, and we over-react. And [reactive solutions are] not built for longevity.” – Head of Strategy & Operations, large tech company

“R&R is a challenge [in my function]. It’s unclear who the owner and who the decision-maker is. By default you think it’s the highest ranking person, but that’s not always the case. Otherwise you just keep having working sessions. – Product Strategy and Operations Executive, large tech company

The take-aways

1. Ensure top-down clarity on strategic direction

“The order of decision-making should be strategy, organization, people; not people, organization, strategy…A lot of times people propose org changes that are solving for people. Or you see you’re working around the strategy.” – Vice President, large tech company

“[All companies need] a clear, well written plan. This includes strategy, mission, vision. There’s also the outcomes you want to measure to know if you’re successful. Some companies don’t have one or both. Some have both but are poorly written and executed. Lots of people don’t include what they won’t do. Companies do poorly at setting goals and [conveying] why they matter.” – Director, large tech company

2. Be targeted and deliberate in assigning shared ownership

“If everyone’s doing the same thing it’s not clear what you’re driving. There are areas that it would be really powerful to have shared goals. But in other places we can move faster as separate units. We [need to be] intentional about when we take approach A vs. B.” – Director, large tech company

“People tend to focus on their area of responsibility over how that area of responsibility affects something bigger. They focus on in-quarter metrics over long-term trends… [But big-picture thinking is] fundamental to maintaining the health of the business…In part it’s a question of empowering individuals. In part it’s signalling to people that that’s how they should be operating.” – Managing Director, large tech company

Group 3: Process problems

Most common: Slow decision-making, limited accountability 

Typical culprits: Large, stable companies

Typical causes: Org complexity

“The way our orgs are designed, with [multiple functional leads] all reporting to different people means there are too many decision-makers… The advantage is a higher degree of excellence…[but the tradeoff] is multiple layers of direction and [dispersed] accountability.” – Senior Product Leader, large tech company

“[Different divisions] have different motivations… There are too many goals to be aligned.” – Senior Software Engineer, large tech company

The take-aways

1. Match the process to the objective and risk
“We see a lot of one-way door decisions. That affects the speed we can tackle problems. Many one-way door decisions are expensive to change or impact brand reputation if you make the wrong call. We could move much faster if we identified decisions [with] two-way doors, with fewer executive reviews and alignment [needed]…You can simply identify ‘what decisions could I reverse today?’…People reviewing work set the tone, including giving coaching [on what they] don’t need to review.” – Senior Product Leader, large tech company

“Centralization vs. decentralization: When do you want to focus on consistency, speed, and execution (centralization), using top-down mandates vs. when do you want people to figure it out and do it their own way, since you don’t know the right way, and eventually make it consistent…Make that decision consciously.” – Strategy & Operations Executive, large tech company

2. Set R&Rs and accountability frameworks

“We have a very matrixed organization. It’s not clear who’s on the hook… It’s a structural challenge. You’ll get people in the room that weigh in that aren’t accountable, so shouldn’t be an extra opinion. That’s most large companies. That causes slowness. [Correcting for this] has to be intentional by top leadership. They have to regularly check in that it’s the right structure to achieve their goals.” – Director, large tech company

“In the kickoff meeting, we don’t set up a decision-maker, they just say we’re all working on it. You see a meeting with a dozen [approvals] required. We need to be more explicit. We need a leader to… assert [R&Rs and] ownership. The owner then has to tell people who’s doing what…It comes back to ownership.” – Strategy & Operations Executive, large tech company

Group 4: Culture problems 

Most common: Low psych safety, siloed communication

Typical culprits: Rapid growth companies; Large, stable companies

Typical causes: Loss aversion, deified leadership

“A mistake I see made a lot is people playing pretend. Not acknowledging reality, pretending things are good when they are not.” – Technical Professional Services Head, large tech company

“[People have a] perception of leadership that they have all the answers.” – Founding Partner, impact investing startup

“Any time the team grows, there’s a breakdown in communication. Every time you shift to no longer being in the room together, comms shift. There’s angst around hierarchy, around process and suddenly needing it.” – Career coach and Founder

The take-aways

1. Model imperfection and risk-taking 

“Everyone in the chain has to feel their leadership is supportive. If one person feels safe but the next stop up doesn’t feel safe, it won’t work. People need to see examples to believe it. Leadership has to model how they’ve made mistakes and asked for and received leadership support. There needs to be a way to reward and highlight the behavior.” – Director, large tech company

“In early stage companies they explore, but when you hit a certain size, it’s too difficult to allow freedom, because that introduces risk. And risk appetite goes down with time…Find the members of leadership that are incrementally more open than the rest, and push them to find creativity. Use that as a test case to prove. It’s an incremental approach.”  – Founding Partner, impact investing startup

2. Normalize open communication and collaborative feedback

“The Achilles heel for orgs is an inability to distinguish between accountability and blame. Accountability is an opportunity to look at something that isn’t working and try to figure out how to problem-solve together…. Vs. blame. That creates an environment where people are unwilling to identify problems.” – Director, large tech company

“People are more agreeable in group settings to give a sense of niceness and organizational cohesion. This is the root of many problems. Having backbone and being willing to disagree is important for organizational health, so people feel like they can raise important concerns… [I have seen] people disagree in backchannels or silent discontent. Then you miss the perspectives of people. [We need to] create a safe space where people feel they can vocalize opinions… You can say why you don’t agree with it, offer data, ask for more input from that person.” – Senior Product Leader, large tech company

Final notes: Expect the expected

The above org problems are less about individual human fallibility and more about the inherent design, stage of development, and maturity curve of the organization itself. Among the four problem groupings, each org type has their “favorites”. Rapid growth companies have more people problems due to hiring at a rapid clip. Large, stable companies have more process challenges due to system complexity and risk aversion. All can suffer from communication breakdowns.  

So if you are experiencing corporate déjà vu, you’re not crazy. You’ve seen this movie before. It’s a classic plot, and still worth a watch. But don’t just get out your popcorn – this is a choose-your-own-adventure. Know thyself, and choose which org type you want to be in. 

And if you are in an org that you feel is missing the mark, I’ll leave you with one last inspirational quote:

“I think people believe some problems are too big for them to fix. Then they focus on the controllable. But there are instances where you’ve got to step beyond that, and take responsibility for difficult issues. It is a form of risk aversion to say that’s beyond what I can tackle. There needs to be a balance between what you can control and tackling the [big] issues.” – Managing Director, large tech company

Sometimes, it may be worth being bold and pushing for change. Either way, wherever you are, be the most adaptable, resilient ‘you’ you can be. As Justice Ketanji Brown Jackson would say, “Bloom where you’re planted.”

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The Index Card Summary of “Think Bigger: How to Innovate”

Think Bigger, by Sheena Iyengar

As a former innovation consultant, who designed initiatives, competitions, and recommended approaches for diverse clients, I was intrigued to hear that decision science scholar Sheena Iyengar, famous for her “jam study”, was now teaching, writing, and advising on innovation. I, of course, wanted to compare notes. Iyengar asserts that there are no new ideas, only new combinations of old ideas. True to her word, her methodology combines tried and true methods.

The Think Bigger Methodology

In Think Bigger: How to Innovate, Iyengar proposes a practical, six-step method to generate innovative solutions:

  1. Choose the [right] problem. Start by focusing on the problem, not the solution. Defining the right problem is a goldilocks challenge: to yield impactful, innovative results, you need a problem that balances specificity with broad enough relevance.
  2. Break down the problem. Address each component of the problem separately; this will enable combinatory options in Step 5.
Iyengar uses Henry Ford’s auto manufacturing innovation as a case study for the Think Bigger methodology.

3. Consider desires. Identify the motivations and desires of those impacted by the problem — including you, the innovative problem-solver; the target beneficiary; and third party stakeholders.

4. Search existing solutions. Learn from past attempts to address the problem; identify both failures to avoid and insights to use.

5. Create a “Choice Map”. Consider different combinations of wide-ranging solutions to the sub-problems. Select several full solutions to investigate further.

A Choice Map is a mix-and-match menu of sub-solutions, to help identify and test different combinations.

6. Seek validation: Get feedback on targeted aspects of your candidate solutions from others.

Iyengar’s key advice

Source: Nordic Business Forum

In addition to simplifying the innovation process, Iyengar also strives to debunk common misconceptions. To increase efficacy, she advises targeting your efforts as follows:

  1. Get over shiny new object syndrome. The most unusual ideas are rarely perceived as more innovative. Instead, new applications and combinations of old ideas often get the most traction.
  2. Listen to your gut before you listen to the data. Ideas are a dime a dozen. But motivation is finite. Notice the direction of travel of your enthusiasm. Is it trending up, down, or flat as you refine your idea? This “real talk” will help eliminate magical thinking about your level of commitment. You need to feel passion to power through the process successfully.
  3. Spend more time researching solution options than you think you should. In contrast to the Lean Startup paradigm, which now dominates Silicon Valley and recommends racing to a minimum viable product, Iyengar recommends spending more time researching many possible MVP options.
  4. Don’t ask for feedback on the full solution. People are likely to judge your idea before its fully-baked. To effectively refine your concept, ask for selective, narrow feedback on specific aspects you are testing.

Nothing new under the sun

Iyengar arrives at core principles consistent with what I’ve seen work. Essentially, she is an advocate of open innovation and lateral thinking — problem-solving by looking beyond traditional organizational or social boundaries and connecting seemingly outside ideas. Given how tried and true her methodology is, I wondered why she chose to write the book. Iyengar herself highlights that even her Choice Map is largely derived from the GE Trotter Matrix.

I believe Iyengar wrote Think Bigger for three reasons: 1) she believes everyone can be innovative and, thus, sought to create an empowering “user manual” of sorts, 2) she observed common pitfalls among her students that she wants to help others avoid, and 3) she’s an academic — she must publish or perish.

To the first motivation, I agree that anyone can be innovative if they look outside their disciplinary or professional silo. This is part of what drew me to study Public Policy as an undergraduate student — it combines a basket of disciplines oriented around solving a challenging problem: designing rules for society. Political Science, Philosophy, Economics and other disciplines can not take on this challenge single-handedly. But while I agree that solutions to complex problems need to come from outside a single silo, I do not think a solopreneur or small team can crack the code as easily as Iyengar implies.

By optimizing her solution identification process for a single person, Iyengar’s methodology is both empowering and overpromising. Think Bigger reads as if one person can transform a product category, industry — nay, the world! — with little more than a worksheet. Yes, it’s a handy worksheet, and with time a single person can run the research process of identifying promising solutions to the target problem. But this individualistic approach limits output to that of one person / team vs. incentivizing many solvers to tackle the challenge — which is the core of a true open innovation approach.

To the second motivation, Iyengar’s advice certainly can save time and help individuals learn from others’ mistakes. As a decision-psychologist, she is versed in the mental tricks our own minds play on us. However, Iyengar’s own biases color her advice. As a full-time academic, she over-indexes on research, suggesting conducting enough research to produce thousands of potential solution combinations. Acknowledging this overwhelming option set, Iyengar then suggests selecting test solutions using a random number generator. How odd to recommend a big upfront investment, followed by an arbitrary narrowing-down process. Perhaps this is where the third motivation — to publish due to peer-pressure — may be tainting her recommendations as well.

Iyengar closes by reasserting, there are no new ideas, just different combinations of old ideas. This claim sums her book up well. She steels with pride and references, offering handy tools that can spur focused creativity. But they are not quite the cure-all implied.

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How bad is it? A methodology for managing mistakes

The cost of perfection is infinite. So how do you right-size your efforts when you have a high quality bar but limited resources?

We’ve all had the impulse to work on something for just a little bit longer. Perhaps late at night or after some rapid edits, you have the nagging pull to check your work for the fifth or sixth time. Perfectionist polishing has its own magnetic field. Whether its fear of finishing or fear of failure, we can feel foibled by our own human fallibility. I have been known to struggle against my physical limitations — eyes bleary, brain no longer absorbing all the details. My body tightening at the thought that after all this effort, I still might — *gasp* — make a public mistake!

The burn and burden of perfectionism can arise in any org, leaving many asking, “At what cost?” Besides weighing you down with excess mental baggage, these moments consume focus, crowding out space for broader impact beyond the one deliverable. Is it worth your health, your sanity, to pursue the impossible goal of perfection? Eventually I realized that my answer is no. I committed to rationalizing my efforts. Afterall, not all errors are created equal. Mistakes are inevitable. It was up to me to learn how to deal with them. So I came up with some heuristics. It began with learning to assess the impact of a mistake. And it ended with learning to calibrate to my context. I dig into both steps below.

Step 1: Understand the impact of your mistake

Early in my career, I attacked every error with the same level of ferocity, whether it was a formatting error or a forecasting error. When I finally realized I need to draw boundaries, CGP Grey ‘s taxonomy of mistakes provided a starting point. Grey takes a structured look at mistakes, defining types and matching the best responses to each:

CGP Grey’s “Menagerie of Mistakes” (from timestamp 14:15) categorizes the big and the small, starting with glitches — mistakes that are good at blending into the background. Source: CGP Grey was WRONG

Glitches and Blunders

Glitches are mistakes that are good at hiding in plain sight. Blunders are embarrassing, visible, but still harmless mistakes (think Bushisms). Glitches and blunders become more common the more you have to do — putting on a sweater backwards before you rush to work, calling someone by the wrong name after you’ve just met a dozen new team members. These are worthy of grace, of laughing it off.

Errors of all varieties

Errors range from the trivial to the significant, and are more varied in how people perceive size, importance, or even if there’s an error at all. Grey playfully names their species, including:

  • Error Trivialis
  • Bad Takeus
  • Technically Correctus
  • Error Factualus
CGP Grey’s “Menagerie of Mistakes” (from timestamp 14:15) describes different error types. Source: CGP Grey was WRONG

Notably, every error requires a judgment call, weighing the cost of fixing the error vs. the benefit of moving on. Unless, that is, you make a catastrophic error — one that fundamentally breaks what you’ve made. They demand a do-over, a recall, or some equivalent.

Step 2: Calibrate to your context

Grey’s mistake taxonomy leaves the critical last mile to the audience, using one keyword to back out of any specific guidance: “perceived.” As political strategist Lee Atwater famously stated, “Perception is reality.” As such, I’ve learned to consider what I perceive, what my stakeholders perceive, and how to mediate between the two. Said another way, three pieces of context guide my error response: my objective, the work’s development stage, and organizational expectations.

Know your objective

Your primary goal should inform what kinds of mistakes are acceptable. If you’re a history teacher, for example, factual errors would undermine the entire endeavor, whereas technically correct errors (e.g. omissions) are necessary to manage class time constraints.

My objective in this blog post is to help ease the mental burden of navigating uncertain expectations in one’s career. Thus, while I can’t afford a bad take, I may well make some trivial errors without impacting this post’s clarity, accessibility, or truthiness. In setting your own boundaries and standards, you tap into your own intrinsic motivation — which leads to better work.

Adjust to the stage of development

As work goes from concept to finished product, it’s rarely worth aiming for perfection until the clear shape of what you are driving towards emerges. In copy editing, for example, editors target attention towards one focal problem to solve per editing round — nailing the story, then nailing the flow, then word choice. Trying to do all three at once tends to fragment attention and drive anxiety born of ineffective strain. Rather than wasting attention, I’ve learned to focus it and let go of certain errors before their time has come to be addressed.

Calibrate to your organization’s expectations

Almost more important than whether you correct a mistake is agreeing with those impacted about how to manage mistakes. As we noted above, mistake tolerance is highly subjective and varies by industry, organization, and team. Some optimize for speed and fast feedback, allowing mistakes to abound in support of rapid iteration. Some are highly image-conscious and demand perfection as a proxy signal for competence and trustworthiness. Notably, the latter is not a sustainable strategy; such organizations are often marked by high turnover (e.g. management consulting).

Regardless of your org culture, set expectations. Have discussions early and often about what your colleagues need and expect from you. Understand how much they want to co-create versus review polished work. Convey where you are in the development process. Gauge whether your teammates can engage with ideas before they are fully fleshed out. Whether you agree or not with your colleagues preferred approach, knowing is half the battle.

Mistakes will be made

Cringy as mistakes may feel, Grey gives us a reality check: “If you make things, there will be errors.” Unless lives depend on it, there is no truly “right” or “wrong” approach to managing mistakes. What matters is that you have one.

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Is empathy scaleable? A question for large anything, from orgs to LLMs

I stand as a manager where smart colleagues in a slowing industry want to know how they can do well while doing good, amidst the techlash. I am also a few degrees of separation from many important decision-makers in the tech industry. From my Stanford class (2007), that’s not hard to do — an Instagram founder and the Open AI CEO both roamed my dorm hallways. Nice, thoughtful people, creating what some now consider weapons of mass destruction.

I see constant battles of competing frames, all with elements of truth. ChatGPT the assistant vs. ChatGPT the job thief. Downsizing to sustain a company versus downsizing to boost investor profits. As I look around at tech industry leaders, and see their lofty visions and decisions met with backlash and criticism, I’m left wondering what good stewardship looks like. It is definitely not the summation of responding to all individual pushes and pulls; that is a recipe for wholesale discontent.

And us humans, with all our complexity, can hold competing ideas in our head at the same time. We might recite one belief on a Tuesday and a clashing belief on a Thursday, depending on mood and context. So is there any way to get it “right” — get to holistic “right” answers that will displease many, while also treating everyone “right” in the process? Put another way, can empathy scale at the same time as organizations and systems do?

Meeting the needs of many

As I considered how scaled decision-making can balance logic and empathy, I reflected on a model of corporate culture developed at Harvard Business School and Spencer Stuart. It describes eight types of business cultures. Results cultures, which value achievement and winning, are in conflict with Caring cultures, which focus on relationship and trust. This seems a close proxy to my logic + empathy values and left me wondering: what is my ideal culture, and can it even exist? Is there a way to minimize or manage the Results/Caring “culture gap” (i.e., the distance between two conflicting cultures)?  Can I get to the best answer for everyone, even when there is implied tension between optimizing for outcomes and optimizing for people?

Source: Harvard Business Review analysis developed at HBS and Spencer Stuart; MBA in the City analysis

Below I explore the question of empathy at scale, drawing on insights from philosophy, psychology, and technology. The TL;DR is 1) it depends on what you mean by empathy; 2) by commonly used philosopher definitions, empathy is not scalable, but 3) perhaps that’s for the best.

The debate and definitions

It turns out social psychologists and philosophers have already sparred on this question for a while. Professor Paul Bloom, psychologist and author of Against Empathy: The Case for Rational Compassion, framed the question as, “What kind of intellectual and sentimental attitudes should we have towards people?” He opens his argument with an important distinction: empathy is the ability to understand and share the feelings of another person — not to be confused with compassion, the desire to alleviate their suffering. Buddhist monk Matthieu Ricard similarly flags that empathy caries a sentimentality, whereas compassion allows for some distanced. (I guess we know how Matthieu would answer the trolley problem.)

But does emotional distance serve the greater good or just ourselves, protecting us from the emotional strain? There’s evidence on both sides. Professor Paul Slovic compared how much people cared about one suffering person versus that same person in a group and found that people cared less about the original person as the group grew. Our brains seem to be managing our capacity, so that we aren’t completely drained by the sheer volume of people to empathize with. Our psychological immune systems seem to prevent us from caring too deeply about each individual in a large group.

But perhaps this brain protective mechanism is, in fact, for the greater good. Bloom argues that limiting empathy can actually protect more vulnerable populations, because empathy is not immune to bias. We tend to empathize more with people who are similar to us, and less with people who are different. Imagine how this could shape decisions that affect a large group of people — problematic to say the least.

So, it seems our greatest moral teachers are warning us away from empathy; its short-sighted nature can misdirects us to focus on the suffering of one rather than the many. It’s why we care more about a mass shooting than we care about the other 99.9% of other gun homicides. When it comes to designing rules and legislation, we want something better than empathy. As philosopher Sam Harris puts it, “we want laws that are wiser than we are; a system that corrects for our suite of moral illusions or biases that lead us to misallocate real resources.”

Star Trek’s Spock is famed for considering “the needs of the many” with emotionless logic. Spock’s species achieves this by suppressing emotion, because theirs are much stronger than human emotions.

The challenge is, good rules are very, very hard to make. So how do we take on this challenge at scale? This is the core question for organizations, as well as for the burgeoning AI industry.

Empathy in organizations and Artificial Intelligence

In the end, I do think logic and empathy can be married; the Results/Caring “culture gap” can be bridged by leaning into cultural moments of Purpose or Learning. For example, if you are pursuing a business reorganization, while it may be the logical move, the transition will be most successful if you include a Learning approach, testing elements of the reorg, and reaffirm shared Purpose — both of which can help demonstrate Caring by valuing feedback and fostering solidarity. This flexible approach could also enable bringing as many sub-cultures along as possible as well.

Source: Harvard Business Review, analysis developed at HBS and Spencer Stuart; MBA in the City analysis

While operationalizing empathy in a practical, scaled way is a constrained process in organizations, the question becomes very different when we start outsourcing decisions to Artificial intelligence (AI) systems. AI is clearly capable of overcoming some human constraints. But is empathy one of them? Becoming emotionally drained is a unique human constraint; we see this in personalized learning, where AI tutors express infinite patience with pupils. AI could be trained to understand and respond to a wide range of emotions; perhaps AI can learn to scale beyond the personal assistant use-case to organizational assistance, and digest large volumes of feedback in an empathetic but balanced way.

The main dampener to this vision is that AI systems have not overcome every human limitation; they can still encode bias. And worse than humans, they generally lack emotional intelligence and can be easily manipulated. So at this point, it would be extremely risky with unquantifiable downside to trust decisions that require empathy or compassion to AI. As many an edtech expert has postulated, we need humans in the loop for all AI systems to evaluate their decisions and recommendations. The Partnership on Responsible AI recommends integrating inclusive participation across all stages of a development lifecycle (vs. participation only at the end, the more common practice).

Does empathy scale?

“I’ve learned that you can have it all, but not all at the same time.”

– Michelle Obama

FLOTUS’ words have stayed with me since I first read them — she warned that Millennials and younger generations often wants everything all at once. While I want both logic and empathy at all times, I accept that their tension will sometimes force trade-offs.

There may not be a single “right” answer for all people, organizations and stakeholders, but we can manage a balance of logic and empathy by keeping humans in the loop, especially in the tech industry. Identifying integration points ahead of time and what targeted input you need can improve both organizational and AI decision-making.

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The Index Card Summary of “The Five Dysfunctions of a Team”

The Five Dysfunctions of a Team is Patrick Lencioni‘s New York Times Best Seller for a reason: any team member can see themselves in this list of foibles.

Each of the five dysfunctions require leadership interventions to solve. Below is a brief summary of what that entails.

The Index Card Summary

1. Building trust

What. The foundation of a functional team is trust: confidence that peer intentions are good, that there is no reason to be protective around the group. Trust minimizes second-guessing and makes it easier to ask for help.

How. Create shared experiences over time, and understand unique attributes of team members. Team-building exercises, discussing team effectiveness, 360 feedback, and team leader role modeling can all drive the psychological safety and honesty needed to engender trust.

2. Addressing conflict

What. All relationships require productive conflict to grow. Without direct, content-focused conflict, teams can be handicapped by back-channeling and personal attacks that doom people to revisiting issues endlessly without resolution.

How. Acknowledge that conflict is productive; agree that impassioned debate is welcome. Ask for permission to address conflicts for the good of the team.

3. Driving commitment

What. Commitment requires both clarity and buy-in. Desire for consensus and/or certainty can weaken commitment and delay decision-making which, in turn, can paralyze teams and weaken their confidence.

How. Drive alignment through tight information cascades, close to the time decisions are made. Reduce ambiguity by setting intermediate deadlines. Address fears with contingency plans or worst-case-scenario analysis. Normalize decisiveness starting in low-risk environments. Leaders must also role model commitment by being ok with wrong decisions, asking for commitment, and reducing emphasis on certainty or consensus.

4. Welcome accountability

What. Peers need to welcome peer call-outs on actions that might negatively impact the team or actions others should model and amplify.

How. Leaders can enable and normalize peer accountability through setting agreed upon standards and goals, encouraging peer feedback, and giving collective team rewards for collectively modeling the right behaviors.

5. Focus on results

What. Time-bound, outcome-based performance drives business success. Yet it can often be crowded out by team or individual status or focus on survival.

How. Define specific target results that you publicly commit to, and reward only supporting actions; tie compensation to outcomes. Leaders must also reinforce a focus on results; if the team leader shows they value and reward other things, teams will react accordingly.

Disconnects and Through-lines

Unpacking the “How”

While Lencioni provides excellent tactical suggestions for tackling the numerous team dysfunctions, he spends too little time exploring where to start. While ‘absence of trust’ is named as the foundational problem, I think the easiest place to start is at the top, with the most visible problem: ‘inattention to results.’ Once you have specific results targets, you can drive accountability, which then motivates commitment, and so on down the pyramid.

Addressing the foundational dysfunctions, ‘fear of conflict’ and ‘absense of trust,’ is admittedly more challenging. In large organizations especially, cross-functional teams may not even conceive of themselves as teams and, thus, may see little benefit to engaging in conflict and little need to build trust. In those instances leaders play an even more critical role. Leaders with a longer-range view will be mindful of the personal costs of these more insidious dysfunctions: energy poorly spent, low morale, and high unwanted turnover.

Across the five dysfunctions, communication and leadership sit at the center of many proposed solutions. Spending more team time together drives trust, can root out conflict and reduce confusion. Documenting decisions and desired results and rapidly sharing and reinforcing them keeps teams in sync. And leadership modeling the right behavior and taking challenges head-on can inspire healthy team culture. These are all reasonable tactics to pursue, but side-step the issue that so much rests on leadership, especially the larger an organization gets.

Addressing the “Why”

Lencioni claims that “teams succeed because they are exceedingly human.” But realistically, it is equally why they fail. Humans are full of bias and are often more focused on their individual experience over organizational goals. Thus, even if an organization starts with a high-functioning team culture, it’s hard to scale; heterogeneity via sub-cultures is normal the larger an organization gets, and cultural drift is equally normal through turnover, changing external contexts, and organizational evolution. With this in mind, it makes more sense to think of high functioning teams as a practice to commit to rather than an end state. Like so much of life, it’s a journey, not a destination.

Source: aspitzer.com
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Harvard trained beggar takeaways

Source: Twitter

The “Harvard trained beggar” snapshot has made the social media rounds, and I had so many reactions. My first thought was: brilliant execution of a social experiment. He earned his pocket change that day. Then my second order thinking kicked in with three more layers:

1. That totally worked! Why?

This clever beggar is heightening group and individual identity, forcing people to compare their behaviors to both each other and their own self images. The threat of cognitive dissonance drives passers-by to literally put their money where there mouth is, especially if a competing group is in the lead. In addition to spurring competition with in group / out group dynamics, our beggar is leveraging social proof. It’s like how power companies now might send you fliers about how much more energy you’re wasting vs. neighbors from peek hour usage. If one religious person gives, surely you (another religious person) should, too.

2. Not sure that’s scientifically accurate…

If we’re taking seriously the idea that the contributions really answer the question of which religion cares most about the homeless, then we need to weight donations by religious demographics of the area. Christianity is clearly the front-runner in gross terms in the snapshot, but there seems to be some selection bias in this sample. Christianity being in the lead and atheism as runner up tracks with national data on religious affiliations.

3. There are ways to improve the outcome

Our beggar could be gaming the outcome even more. If he knows the demographics of his corner, he could heighten competition against the out groups by adding money to the rival bowls. Fill a few competing bowls and hangout in front of a church, for example.

I wish him luck in his endeavors. I look forward to reading the sociology paper he publishes following this experiment.

P.S. What does the alternate sign say in the back left? “Help me find a Job”? Guess that one did not work out as well in his A/B testing.

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Top five small apartment survival tools

It’s no small feat to live your best life in a big city and a small apartment. Below are a few tools for your urban jungle survival kit, indoor edition.

1. Swap your TV for AR glasses

I was so glad my husband did not subject me to She Hulk this fall. Instead, he lay in bed with his Nreal Air AR glasses, watching TV on the ceiling. Consider it a household harmony investment.

2. Get the right cooking equipment

Something I’ve learned the hard way: your smoke detector is not a done timer. In a standard-sized NYC kitchen…

…the lack of ventilation means you’re not going to be searing steaks on a blazing hot skillet. Not unless you want a visit from the FDNY. You might think this is what’s going to show up at your house, but it’s actually probably more like this. Slow and low is the rule for flames. Have a Dyson dual fan/air filter at the ready. And a glass of scotch to tide you through any mishaps.

3. Decorate with dual purpose furniture

Milk every inch of space with multi-purpose furniture. Seats with storage. Beds with drawers. Tables with adjustable height and size. These transformers were made for the studio apartment.

4. Treat your furry friend with a Furbo

Maybe you bought a pandemic puppy or have a longer-term furry friend. If your neighbors are telling your dog to sit through your thin apartment walls, it’s probably time to invest in a Furbo. Listen, your land lord has bills to pay and can’t exactly invest in sound proofing apartments all the way in NYC when he lives in the Hamptons. You’re just going to have to take responsibility for your noise since you chose that spacious $5,000/ month studio.

The Furbo is a dog cam / treat dispenser / walkie-talkie all rolled into one. It alerts you to loud noises and lets you train your doggo from afar.

5. Create personal space with excellent wireless headphones

If you’re a typical New Yorker living with five roommates, and you don’t want to wake up your brother on the couch, a good set of headphones is critical. An Apple TV / AirPod pro combo is great for personal and shared audio. I especially love headphones for action movies, which should all be titled: “EXPLOSIONS!! and whispers….” With headphones, all the sound gets leveled out. The one thing headphones can’t help me with is comedies. For some reason when I laugh, the Furbo thinks my dog is barking. Speciesist.


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Has the metaverse missed its moment? Three recent misses explored

If you were looking at “metaverse” mentions alone in 2022 earnings calls, you might be fooled into thinking massive adoption is underway, a-la Facebook in 2007.

Mentions of “metaverse” in earnings calls (Q1 2016-Q1 2022)

Source: Axios

And in the last three years, three concurrent forces emerged that could have catapulted the metaverse into the next ubiquitous computing platform, similar to how the iPhone turned cell phones into pocket computers in 2008. These cultural moments have been catalyzed or amplified by the forced isolation of the a pandemic. They are:

  1. The loneliness epidemic
  2. Remote school and work
  3. Diversity, equity, and inclusion

Below we look at these three cultural moments and consider what the missed opportunities with each mean for the future of the metaverse.

1. The loneliness epidemic

As Luminary Labs summarized it, “before the coronavirus pandemic, there was the loneliness pandemic. Three in five Americans say they are lonely.” Forced isolation and social distancing during the pandemic exacerbated loneliness, but also jolted us into trying new ways of interacting remotely. Zoom Christmas became a thing that even grandma attempted.

Source: Business Insider

But the change in circumstance didn’t change the underlying mental health crisis. Loneliness leads to depression, and depression saps motivation, especially motivation to try new things. Ironically, virtual reality (VR) — the metaverse’s cornerstone technology — can effectively treat depression and anxiety, among other psychological and physical disorders. But outside of a few medical vanguards, no metaverse investor made significant strides to bridge the user motivation gap.

2. Remote school and work

Remote school and remote work are both here to stay. At a minimum, remote school will be a supplement to in-person schooling. (Goodbye snow days!) At a maximum, remote school will continue to unlock access, including for disabled, rural, and other student populations. Similarly, remote work will continue, as demand from workers is clear and employers are trending towards offering it in a competitive labor market. Metaverse solutions could arguably be the highest quality and lowest cost tech for both. An Oculus Quest 2 costs less than many Chromebooks, and offers many more capabilities. Further, two of VR’s biggest applications to date are in education and professional training, from surgeons to customer service reps.

Despite the high potential use cases and cost efficacy, no big employers or schools have announced the launch of internal metaspheres. This is likely for the same reason accounting firms issue buggy five-year-old laptops to employees making six figures: internal infrastructure is viewed as a cost center, not a value-generator. Why give employees infinite screens on their Oculus when they can still eke out the “same” work on their tiny laptop screens? Metaverse investors have done little to reverse this myth. So educators are likely to continue cobbling together free and lower cost resources, and employers are unlikely to significantly revise their budgets for remote work systems development.

3. Diversity, equity, and inclusion (DEI)

For as little as $15, anyone can begin to experiment with VR and, thus, the metaverse, via Google Cardboard. With one download, you can fly around the globe in Google Earth, visiting the Coliseum or the Pyramids. And on Oculus, popular games like Beat Saber cost half as much as Nintendo Switch games. This affordability presents an epic inclusion opportunity. Oculus Quest 2 sales have already topped 15 million, making the barriers to participation pretty low. In addition to being relatively affordable, the diversity of subcultures and self-expression possibilities are endless. Inclusion could be easier with the freedom to choose avatars that reflect your identity. A woman can choose a male avatar, a man could choose a wolf avatar, and perhaps in the future, a gender fluid person could change their avatar daily if they so chose.

Despite the metaverse’s economic accessibility, and its potential to welcome diverse populations equitably, most people don’t seem to see themselves participating. Critics hesitate to become legless floating bodies, and some women feel awkward in the currently male-dominated spaces. As many companies have learned in the great return-to-office debate, cultivating a sense of belonging is not as simple as just creating the persistent space. And its not clear that metaverse investors are creating welcoming on-ramps to expand inclusion.

Did metaverse miss its moment?

Why did the metaverse’s biggest advocates, from Microsoft to Facebook, not double down on pushing products like Mesh, the holographic collaboration tool, or experiences like Meta Quest meet-ups (surely a welcome alternative at the height of Zoom fatigue)? The problem seems to be two fold: lack of focus and premature hype.

The metaverse remains so loose a concept that even tech evangelists are confused about what it is. In theory it’s such a flexible concept — inclusive of most shared, persistent digital spaces — that, with some interest and ingenuity, early investors should be able to find pockets of growth. But instead that flexibility has cultivated a lack of focus. A very expensive lack of focus at that — Meta’s investors hammered the stock for having too little to show for its $10 billion of losses per year. While Oculus hardware is making great strides, the virtual experiences themselves haven’t reached the quality level that can attract everyday use. And with very public tech flubs like Facebook’s virtual Foo Fighter’s concert mishap, the technology clearly isn’t ready to support mass adoption.

The metaverse today is like QR codes in the 2000s — useful, but not convenient, intuitive, or ubiquitous enough to see mass adoption. That took a pandemic to change. And the metaverse has (hopefully) mostly missed this one. But that doesn’t mean it won’t catch the next growth opportunity. And we certainly can’t call the three cultural moments discussed above — loneliness, remote school and work, and DEI — solved problems. The metaverse is on its way. Just more slowly than its proponents would have you think.

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The nudge report: A new marketing exploit being tested in retail

On a stroll through Soho, I noticed an unusual sale sign. It wasn’t your typical “40% off!” or “End of year sale!” promising deep discounts on already marked-up products. It was actually the opposite. Yes, it was price anchoring high, but in the most direct way possible:

$75 and under. Not $75 and over. The sign lists the highest price you’ll pay. By anchoring high, the sign is nudging you to spend at least $75 per item. Versus a sign listing the lowest sale price e.g. “99¢ and up”, which nudges you to expect to only pay that bottom price. This bifurcation in price anchoring indicates target market segmentation. The low anchor marketing is for low price, high volume businesses, whereas the high price anchor is likely for high price, low volume businesses. This sign was in front of Athleta, so targeting somewhere between the Under Armor and Lululemon athleisure segments.

It’s a creative new tact, but it utterly failed to entice me. I suppose I’ve been exposed to 40% of Banana Republic signs for all of my independent shopping life, so my brain is primed towards that particular bug. I give this social nudge 2 out of 5 stars. High marks on creative experimentation, low marks on efficacy on an audience of one.