Latest / Chief in Tech Deep Dive Shortcast / Chief in Tech S1E43 - Sarah Touzani : Beyond Automation – How AI Reveals Hidden Team Performance

Transcript

Chief in Tech S1E43 - Sarah Touzani : Beyond Automation – How AI Reveals Hidden Team Performance · Chief in Tech Deep Dive Shortcast

• 0:00
  Welcome to the deep dive. I mean, I really want
• 0:02
  you to think about this absolute, almost feverish
• 0:07
  obsession the business world has right now with
• 0:09
  artificial intelligence. Oh, it is everywhere.
• 0:11
  You literally can't escape it. Right. Specifically,
• 0:15
  it's this frantic race to just automate everyday
• 0:18
  tasks. Like, we are all being bombarded with
• 0:21
  the same questions. You know, how can we do this
• 0:23
  faster? How can we get a machine to write this
• 0:25
  email or summarize this 15 -page report? Or instantly
• 0:29
  generate the code for a new software feature.
• 0:32
  Exactly. Yeah, it's entirely focused on speed,
• 0:35
  efficiency, and just pushing the go button faster.
• 0:38
  But what if that entire collective obsession
• 0:40
  is actually missing the point? Like, what if
• 0:42
  the business world is sprinting in the totally
• 0:44
  wrong direction and completely ignoring AI's
• 0:47
  actual superpower? Well, the thing is, we fall
• 0:50
  into this trap with almost every single revolutionary
• 0:53
  technology. Do we? Oh, absolutely. We take something
• 0:56
  that has the power to fundamentally alter our
• 0:58
  reality, and our first instinct is just to use
• 1:01
  it to do our old inefficient habits slightly
• 1:03
  faster. We stare at the output, you know, the
• 1:06
  final product, instead of actually looking at
• 1:08
  the underlying system that produced it. And that
• 1:10
  brings us directly to the mission of our deep
• 1:12
  dive today. We are exploring some incredibly
• 1:15
  compelling excerpts from a recent session. It's
• 1:18
  titled The Waggle Method, AI and Invisible Team
• 1:22
  Flow. Right, which is just a fascinating read.
• 1:25
  It really is. The source material outlines the
• 1:27
  work of Sarah Tuzani. She's a chief operating
• 1:30
  officer who has basically pioneered this radically
• 1:32
  different approach to artificial intelligence
• 1:34
  in the workplace. And this approach has absolutely
• 1:36
  nothing to do with turning AI into like a glorified
• 1:40
  digital assistant that just does your chores.
• 1:42
  No, not at all. It requires a complete paradigm
• 1:45
  shift. I mean, the text asks us to stop looking
• 1:47
  at AI as an automated worker that just sits at
• 1:49
  a desk and churns out content. Instead, we really
• 1:52
  need to to start looking at AI as an organizational
• 1:54
  MRI machine. OK, let's unpack this. Because we
• 1:58
  desperately need to transition away from the
• 2:00
  standard AI hype, all those exhausting buzzwords
• 2:03
  that are probably flooding your LinkedIn feed
• 2:05
  right now. The endless listicles of prompts.
• 2:08
  Yes, exactly. We need to move past that and dive
• 2:11
  deeply into what this source material reveals
• 2:14
  about the hidden dynamics of how we actually
• 2:16
  work. Because doing tasks a million times faster
• 2:20
  really does not matter at all if you are driving
• 2:22
  a hundred miles an hour while completely blind
• 2:24
  to the road ahead of you. And that blind spot
• 2:26
  is the core concept of the text we're analyzing
• 2:28
  today. While everyone races to automate the creation
• 2:32
  of documents or code, they're just leaving the
• 2:35
  greatest potential of artificial intelligence
• 2:36
  on the table. Which is what exactly? Its true
• 2:39
  power is revealing how work actually flows through
• 2:42
  an organization. Yeah. you know, the complex,
• 2:45
  often really messy reality of how human beings
• 2:47
  interact to actually get something done. Think
• 2:49
  about it like this. Imagine you run a hospital
• 2:52
  and you just spent millions of dollars buying
• 2:54
  the most advanced state -of -the -art diagnostic
• 2:57
  machine ever created. It can scan the human body
• 3:00
  at a cellular level, predict illnesses, map neural
• 3:03
  pathways, all of that. But instead of using it
• 3:06
  to diagnose patients, you just wheel it into
• 3:08
  the back office and use the computer attached
• 3:10
  to it as like a fancy calculator to speed up
• 3:13
  the hospital's payroll accounting. Yeah, that
• 3:16
  is the perfect analogy for the current state
• 3:19
  of enterprise AI. The patients are still sick
• 3:22
  in the lobby, but the CFO is just thrilled because
• 3:25
  the spreadsheet loaded a few seconds faster.
• 3:27
  I do want to push back on this premise just a
• 3:29
  little bit though, because I know what you listening
• 3:31
  might be thinking. Sure. If the goal isn't just
• 3:33
  to crank out work faster, What is this MRI machine
• 3:37
  actually revealing that we can't already see
• 3:40
  with our own eyes? I mean, companies are practically
• 3:42
  drowning in visibility right now. That's the
• 3:44
  assumption. Right. We have project managers.
• 3:46
  We use massive software suites like Jira or Asana.
• 3:49
  We hold daily stand -up meetings. We track every
• 3:52
  single hour. We know when a project is behind
• 3:54
  schedule, right? We know who is logging on and
• 3:57
  who isn't. So what exactly is this supposed hidden
• 4:00
  layer of work? What's fascinating here is Sarah
• 4:02
  Tuzani's surprising discovery regarding that
• 4:04
  exact assumption. Oh. Yeah. According to the
• 4:08
  text, she found that AI can identify behavioral
• 4:11
  patterns that reveal critical performance problems
• 4:14
  a full six months before they ever show up in
• 4:17
  any of those traditional tracking tools you just
• 4:19
  mentioned. Wait, six months? Half a year. A full
• 4:22
  half a year. I really want you, the listener,
• 4:25
  to pause and visualize your own workplace for
• 4:28
  a second. Think about the last major catastrophic
• 4:32
  project failure. Or think about the last time
• 4:35
  a crucial, high -performing team member completely
• 4:38
  burned out and abruptly quit. It happens all
• 4:41
  the time. Exactly. Or think about a major product
• 4:44
  launch that missed its deadline by a mile. How
• 4:47
  often are the metrics your company relies on
• 4:49
  just a postmortem of a disaster that actually
• 4:51
  broke months ago? ago. In traditional management,
• 4:54
  it is almost always a postmortem. We have to
• 4:56
  draw a really hard line here between two radically
• 4:59
  different ways of measuring reality, which are
• 5:02
  lagging indicators and leading indicators. Right.
• 5:04
  Break that down for us. So everything we currently
• 5:07
  rely on to run a business, like quarterly revenue,
• 5:10
  the number of support tickets closed, employee
• 5:12
  turnover rates, or even the timeline of a product
• 5:15
  launch. Those are all lagging indicators. Because
• 5:18
  they measure what already happened. Exactly.
• 5:21
  By the time that data appears on a manager's
• 5:23
  dashboard, the reality it describes is already
• 5:26
  locked in. The quarter is over. The revenue is
• 5:29
  missed. The employee has already signed an offer
• 5:32
  letter somewhere else. It's basically the ultimate
• 5:34
  rear -view mirror management style. You are driving
• 5:37
  down the highway looking only at the road you've
• 5:39
  already passed. Duzati's key insight here is
• 5:42
  that while the final outputs are lighting indicators,
• 5:45
  the behavioral patterns of the team are the ultimate
• 5:47
  leading indicators. Okay, so how people are actually
• 5:50
  interacting day -to -day. Yes. How different
• 5:52
  departments communicate. The specific points
• 5:55
  where bottlenecks form during a handoff. Even
• 5:58
  the variance in how long it takes to get a simple
• 6:00
  approval. Those are the early warning signs.
• 6:03
  If you deploy AI to read those specific behavioral
• 6:05
  patterns, you are effectively looking into a
• 6:07
  six -month crystal ball. Let's make that crystal
• 6:10
  ball tangible. Because a terrible financial quarter
• 6:13
  doesn't just spontaneously generate out of thin
• 6:15
  air on a Friday afternoon. No, it never does.
• 6:18
  Let's trace that six -month decay. It starts
• 6:21
  today with a tiny, almost imperceptible breakdown
• 6:24
  in communication rate. An engineer asked for
• 6:27
  clarity on a design feature, and instead of a
• 6:30
  quick huddle, it turns into this confusing, sprawling
• 6:33
  email thread. Right. And next week... Because
• 6:35
  of that confusion, someone has to wait three
• 6:38
  extra days for a security sign -off. Exactly.
• 6:40
  And the week after that, people start scheduling
• 6:42
  massive hour -long meetings with 10 people just
• 6:46
  to untangle the original confusion. The calendar
• 6:48
  bloat is a massive indicator. It really is. And
• 6:50
  then a month later, the engineering and design
• 6:52
  departments get completely frustrated and just
• 6:54
  stop collaborating entirely. They retreat to
• 6:57
  their silos. Yeah, the classic us -versus -them
• 6:59
  mentality sets in. Three months later, the product
• 7:01
  is built, but it doesn't match the original vision
• 7:04
  at all. and then a full six months later, the
• 7:07
  product launches, the customers hate it, and
• 7:09
  the revenue drops. And traditional management
• 7:12
  only notices the problem at month six when the
• 7:15
  revenue actually drops. They completely missed
• 7:17
  half a year of behavioral degradation. It's wild
• 7:20
  when you look at it that way. It is. But to be
• 7:23
  fair, it is not necessarily the manager's fault.
• 7:26
  Human beings simply do not have the cognitive
• 7:28
  bandwidth to process the sheer volume of subtle
• 7:31
  behavioral data flowing across, say, a 300 -person
• 7:35
  organization. Right. You can't be everywhere
• 7:38
  at once. Exactly. You cannot track the time decay
• 7:40
  of every single Slack message or the metadata
• 7:43
  of how many times a single JIRA ticket bounced
• 7:46
  back and forth between two departments. But an
• 7:49
  artificial intelligence model absolutely can.
• 7:51
  Because it doesn't get tired. Right. It does
• 7:53
  not get overwhelmed by the noise. It simply maps
• 7:55
  the network, establishes a baseline of normal
• 7:58
  interaction, and immediately flags the anomalies
• 8:01
  where the workflow is suddenly degrading. But
• 8:02
  that raises a huge practical question for me.
• 8:05
  I mean, human workflows are notoriously messy.
• 8:07
  We leave these chaotic data trails, like some
• 8:10
  people prefer email, some prefer direct messaging,
• 8:13
  some people have chaotic overlapping calendars.
• 8:15
  Oh, yeah. Total chaos. How does the system make
• 8:18
  sense of that noise without just handing a manager
• 8:21
  a massive spreadsheet of false positives? Like,
• 8:24
  is this just academic theory? How do we know
• 8:27
  this six -month prediction model actually survives
• 8:29
  contact with the real, unpredictable business
• 8:33
  world? Well, we know it survives because of Sarah
• 8:36
  Tuzani's operational background. Right. She's
• 8:38
  actually done this. Exactly. The source material
• 8:40
  emphasizes that she did not develop this framework
• 8:43
  in some pristine university research lab. As
• 8:46
  a chief operating officer, she leveraged these
• 8:49
  exact principles to scale a fintech company through
• 8:52
  a period of explosive, chaotic growth. What kind
• 8:55
  of numbers are we talking about here? The metrics
• 8:57
  in the text are substantial. She helped secure
• 9:00
  a $50 million plus Series A funding round and
• 9:03
  scaled the platform to over 200 ,000 customers.
• 9:06
  Wow. Okay, that kind of hyper growth is exactly
• 9:09
  the proving ground you want. I mean, anyone who
• 9:11
  has ever been within a 10 -mile radius of a startup
• 9:13
  knows that hyper growth is usually held together
• 9:15
  by nothing more than duct tape, sheer panic,
• 9:18
  and a lot of late -night pizza. Absolutely. It's
• 9:21
  a miracle anything works at all. Right. So scaling
• 9:24
  a fintech company to 50 million dollars and hundreds
• 9:27
  of thousands of users isn't just about having
• 9:29
  a good app. It requires a flawless, incredibly
• 9:33
  resilient operational engine. A single bottleneck
• 9:37
  in compliance or customer onboarding could just
• 9:39
  tank the entire company at that speed. The stakes
• 9:42
  couldn't be higher in that environment, which
• 9:44
  is what makes her findings so critical. The text
• 9:47
  details that navigating that extreme pressure
• 9:50
  cooker actually led her to a profound realization.
• 9:54
  Here's where it gets really interesting, because
• 9:55
  the core philosophy she pulled from that hyper
• 9:57
  growth experience essentially flips the entire
• 10:00
  script on modern management. It really does.
• 10:02
  We are so conditioned to view management as this
• 10:05
  forceful act, you know, cracking the whip, setting
• 10:07
  artificial deadlines, pushing the boulder up
• 10:09
  a steep hill through sheer willpower. But her
• 10:12
  premise is that high performance is actually
• 10:14
  a team's natural state, provided you actively
• 10:17
  remove the elements that are blocking the current.
• 10:19
  When work flows naturally, performance improves
• 10:22
  automatically. And the text gives a specific
• 10:25
  diagnostic name to those hidden blockages. Calls
• 10:28
  them invisible friction. Invisible friction?
• 10:31
  I like that. Her entire methodology is built
• 10:33
  around helping organizations identify and eliminate
• 10:37
  this invisible friction so they can stop fighting
• 10:40
  against their own internal architecture. Let's
• 10:43
  ground this concept of invisible friction for
• 10:45
  you listening. Think about the plumbing running
• 10:48
  through the walls of your house. Invisible friction
• 10:50
  is like a microleak hidden deep behind your drywall.
• 10:53
  You can't see it. Right. You literally cannot
• 10:55
  see it. Your water bill, which represents your
• 10:58
  traditional lagging metric, looks completely
• 11:00
  normal for months. So you assume everything in
• 11:03
  the house is structurally sound. But meanwhile,
• 11:06
  water is slowly, persistently dripping. Ruining
• 11:10
  everything behind the scenes. Exactly. The insulation
• 11:12
  is soaking it up, the wooden studs are starting
• 11:14
  to rot, and the foundation is slowly being compromised.
• 11:18
  By the time you finally see a dark wet spot forming
• 11:20
  on the living room paint, calling a plumber is
• 11:23
  no longer enough. You need a structural engineer
• 11:25
  to rebuild the wall. That structural rot is exactly
• 11:28
  how invisible friction operates within a corporate
• 11:31
  team. It is almost never one massive obvious
• 11:35
  failure. It is the micro delays. The little things
• 11:38
  that add up. Yes. It is a critical handoff between
• 11:41
  sales and legal that has no clear owner. It's
• 11:45
  an ambiguous approval process, where three different
• 11:47
  managers think they have veto power. These small
• 11:51
  points of friction compound daily, and they slowly
• 11:54
  erode the foundation of a team's productivity
• 11:57
  and morale. But I have to play the skeptic again
• 11:59
  here. Because if a process is truly that broken,
• 12:01
  like if an engineer is waiting four days for
• 12:03
  a routine compliance check, shouldn't a competent
• 12:06
  manager know about that? You would think so,
• 12:08
  right? Yeah. Why does the source material place
• 12:10
  such heavy emphasis on the fact that most companies
• 12:13
  are completely blind to where their flow is blocked?
• 12:16
  Like, why is the friction invisible to the people
• 12:18
  whose entire job is to oversee the work? If we
• 12:21
  connect this to the bigger picture, the blindness
• 12:24
  isn't actually a failure of individual managers.
• 12:27
  It is a fundamental flaw in how modern organizations
• 12:30
  are architected to view themselves. What do you
• 12:32
  mean by that? Well, almost every company on earth
• 12:34
  is structured exclusively around outputs and
• 12:37
  nodes. They look at the individual employees,
• 12:40
  you know, the nodes in the network, or they look
• 12:42
  at the final polished product. The questions
• 12:44
  they ask are always binary, like, did John finish
• 12:47
  drafting his report? Did the marketing campaign
• 12:50
  go live on Tuesday? Exactly. It is entirely binary.
• 12:53
  The checkbox is either ticked or it isn't. The
• 12:55
  node is either functioning or it is broken. But
• 12:58
  here is the critical blind spot. They never look
• 13:01
  at the space between the notes. They do not measure
• 13:04
  the behavioral flow of how that specific piece
• 13:06
  of work moved from Jane over to John through
• 13:09
  the legal department and finally to the launch
• 13:11
  pad. Management is blind to the space between
• 13:14
  people because historically that space has been
• 13:17
  impossible to quantify. Right, you can't put
• 13:18
  that in the spreadsheet. You cannot drop a column
• 13:21
  for natural team flow into a standard financial
• 13:23
  document because it couldn't be measured, it
• 13:26
  simply wasn't managed. Therefore, the agonizing
• 13:30
  friction occurring in those transitional spaces
• 13:32
  remains entirely invisible to the traditional
• 13:35
  managerial eye. OK. That makes perfect sense.
• 13:39
  If you only measure the final output, you are
• 13:42
  willfully blinding yourself to the grueling,
• 13:45
  friction -filled journey your team actually took
• 13:47
  to produce that output. And this happens all
• 13:50
  the time. Constantly. A team might hit their
• 13:52
  massive Q3 deadline, and the traditional metric
• 13:55
  dashboard flashes green. It screams success.
• 13:58
  Everyone celebrates. Right. But to get that green
• 14:01
  light, the team had to work three weekends in
• 14:03
  a row, navigate six incompatible software platforms,
• 14:07
  bypass a toxic middle manager, and manually enter
• 14:10
  data because the API was broken. The output looks
• 14:13
  like a victory, but the behavioral pattern underlying
• 14:16
  it is screaming imminent burnout and systemic
• 14:18
  failure. And that failure will eventually arrive.
• 14:21
  The green light on the dashboard is really just
• 14:23
  masking the structural rot behind the drywall.
• 14:25
  And this gap, the inability to measure the space
• 14:28
  between the nodes, is exactly the problem Tuzani
• 14:30
  is tackling with her new framework. So if the
• 14:33
  fundamental problem is that companies cannot
• 14:35
  see where their internal plumbing is blocked,
• 14:37
  and traditional metrics are essentially just
• 14:39
  an autopsy report of a failure that began half
• 14:42
  a year ago, what is the actual practical solution
• 14:45
  being proposed here? Well, according to our source
• 14:48
  material, the solution is the platform she is
• 14:50
  building right now. She is actively pioneering
• 14:53
  a new category of organizational intelligence
• 14:56
  through her current venture, which is called
• 14:58
  waggle AI. OK, waggle AI. What exactly is the
• 15:03
  waggle method doing to find these micro leaks?
• 15:06
  Because we talked earlier about not spying on
• 15:08
  people. How does it map the flow without becoming
• 15:10
  like this creepy surveillance tool? The text
• 15:13
  highlights that waggle AI functions by measuring
• 15:16
  and improving how work actually flows purely
• 15:19
  through operational metadata. Metadata, OK. Right.
• 15:21
  It is not reading the content of your private
• 15:23
  messages or tracking your keystrokes to ensure
• 15:25
  you are glued to your desk. It is analyzing the
• 15:28
  velocity and structure of the work itself. Oh,
• 15:30
  that's a big difference. It's a huge difference.
• 15:33
  It looks at the metadata of a project life cycle.
• 15:36
  So if an asset typically takes two days to move
• 15:39
  from the engineering node to the quality assurance
• 15:41
  node, and suddenly takes seven days, and it spawns
• 15:44
  three unexpected calendar invites with 10 attendees
• 15:46
  each. The AI flags that variance. Exactly. It
• 15:50
  flags it instantly. It is actively helping leaders
• 15:53
  spot those specific structural blockages months
• 15:56
  before a missed deadline ever materializes. It
• 15:58
  is finally putting a highly sensitive diagnostic
• 16:01
  sensor on that unmeasurable space between the
• 16:04
  nodes. The invisible friction finally becomes
• 16:07
  visible data. This raises an important question,
• 16:10
  however, about the human element of leadership.
• 16:12
  Oh. If you deploy an AI capable of continuously
• 16:16
  mapping your team's behavioral patterns and highlighting
• 16:19
  exactly where the current is blocked, it forces
• 16:21
  a massive, really uncomfortable mindset shift
• 16:23
  for the people in charge. Uncomfortable in what
• 16:26
  way? What do leaders have to give up? They have
• 16:28
  to give up being task managers. If the artificial
• 16:31
  intelligence is giving you a real -time topographical
• 16:33
  map of the workflow, your role fundamentally
• 16:37
  changes. You must transition from a task manager
• 16:40
  into a flow facilitator. A flow facilitator?
• 16:43
  Right. A traditional manager looks at a dashboard,
• 16:46
  sees a red light, and asks, who didn't do their
• 16:48
  task? Why isn't this done? The blame game. Exactly.
• 16:51
  But a flow facilitator looks at the AI insights
• 16:54
  and asks a completely different set of questions.
• 16:58
  They ask, Why is there sudden friction between
• 17:00
  the engineering pod and the design team? Is the
• 17:03
  new intake form we introduced last week causing
• 17:05
  a bottleneck? How do I redesign this environment
• 17:08
  so the work can flow naturally again? Wow. That
• 17:10
  really requires stepping back from the micromanagement
• 17:13
  of individuals and stepping up to become an architect
• 17:16
  of the environment itself. I want you, the listener,
• 17:19
  to run a mental diagnostic on your own daily
• 17:21
  workflows right now. Think about a typical Tuesday
• 17:24
  morning. Everyone loves a Tuesday. Right. Visualize
• 17:27
  the endless email chains, the hour -long sync
• 17:30
  meetings that could have easily been a paragraph
• 17:33
  on Slack, the constant waiting for access permissions
• 17:36
  to a single document. Where is the invisible
• 17:40
  friction hiding in your Tuesday? It's everywhere.
• 17:43
  How much of your finite time and mental energy
• 17:46
  is completely wasted just fighting against bureaucratic
• 17:48
  blockages instead of actually riding the natural
• 17:51
  flow of your expertise? We have normalized a
• 17:54
  culture where people spend vastly more energy
• 17:57
  navigating the friction of their workplace than
• 18:00
  executing the actual work they were hired to
• 18:02
  do. It's so true. Imagine the exponential leap
• 18:04
  in a company's performance when that wasted energy
• 18:07
  is suddenly reclaimed. as Tuzani's Fintech scaling
• 18:10
  experience proved. When the friction is removed
• 18:12
  and work flows naturally, performance is not
• 18:14
  just inch upward. It improves automatically.
• 18:17
  You do not have to force it. It is an incredibly
• 18:19
  liberating concept. You do not have to squeeze
• 18:21
  more blood from the stone. You do not have to
• 18:23
  demand 80 -hour work weeks. You just have to
• 18:25
  clear the debris out of the river and let the
• 18:27
  water run. That's beautifully put. Okay, let's
• 18:30
  unpack all the layers we have covered today.
• 18:33
  So what does this all mean? We started this deep
• 18:36
  dive looking at the massive global distraction
• 18:39
  of basic AI automation, you know, the relentless
• 18:42
  push to make a machine write emails faster while
• 18:45
  completely ignoring the system generating those
• 18:48
  email in the first place. The blind spot. Right.
• 18:50
  And we discovered through the source material
• 18:52
  on Sarah Tuzani and the Ragle method that the
• 18:55
  true revolutionary power of artificial intelligence
• 18:58
  lies in its ability to see the invisible. It
• 19:01
  is the unprecedented ability to map the behavioral
• 19:04
  flow between human beings, analyzing operational
• 19:07
  metadata to predict systemic failures a full
• 19:09
  six months before traditional lagging metrics
• 19:12
  even register a problem. A six -month crystal
• 19:14
  ball. Exactly. And we learned from her proven
• 19:17
  success in hypergrowth environments that the
• 19:19
  ultimate secret to high performance is surprisingly
• 19:21
  simple. remove the invisible friction, design
• 19:24
  an environment where work flows naturally, and
• 19:27
  the metrics will take care of themselves. It
• 19:28
  is a brilliant necessary paradigm shift for the
• 19:32
  modern workplace. It fundamentally changes how
• 19:35
  we view efficiency. However, it does leave us
• 19:38
  with one final lingering thought to consider.
• 19:41
  What's that? Well, If artificial intelligence
• 19:44
  eventually becomes flawless at mapping our behavioral
• 19:46
  patterns, if we successfully utilize these tools
• 19:50
  to eliminate every single piece of invisible
• 19:53
  friction, every bottleneck, and every miscommunication
• 19:56
  in our daily workflows, at what point does optimizing
• 19:59
  for perfect frictionless flow start to eliminate
• 20:02
  the chaotic, unpredictable human friction that
• 20:05
  often sparks our most brilliant out -of -the
• 20:07
  -box innovations? Wow. That is a phenomenal point
• 20:10
  to end on. Does perfect efficiency ultimately
• 20:13
  kill the happy accident? Does a perfectly smooth
• 20:15
  river ever carve a new canyon? That is definitely
• 20:18
  something for you to chew on this week as you
• 20:20
  navigate the invisible currents of your own workplace.
• 20:23
  Thank you for joining us on this deep dive. Keep
• 20:25
  questioning the standard metrics, keep an eye
• 20:27
  out for those micro leaks behind the drywall,
• 20:29
  and we will catch you next time.