Fast and Slow Integrated Problem Solving Structures
Founder and Author, IT Revolution
Dr. Steve Spear
Author, The High Velocity Edge: How Market Leaders Leverage Operational Excellence to Beat the Competition
Thank you, Julia. Okay. So one of the most impactful learning moments for me was taking a workshop at MIT in 2014, which has tremendously influenced my thinking. I went to this class because it was taught by Dr. Steven spear, who I've mentioned so many times in years past, he is famous for many things, but he's probably most famous for writing the most widely downloaded Harvard business review article of all time, uh, which he wrote in 1999 called decoding the DNA of the Toyota production system. This was based in part on his doctoral dissertation that he did at the Harvard business school. And in support of that, he worked on the manufacturing plant floor of a tier one Toyota supplier for 16 months since then he's extended his work beyond just high repetition manufacturing to engine design and patent Whitney, the building of the safety culture at Alcoa, uh, and also how we make safe healthcare systems.
And recently he was part of an initiative to build a learning dynamic across all aspects of the us Navy enterprise. So for nearly a year, we've been talking two or three times per week, try to see if we can codify what we've observed in, in our careers, because there is this magic dynamic that is being increasingly being used to unleash human creativity in so many different domains, whether it's harnessing, uh, the Adam safely to sending a man to the moon and back the Toyota production system, the Alcoa safety culture that we talk about in dev ops all the time, the story of team of teams, the gender of organizations, uh, as per Dr. Westrum resilience engineering and learning from incidents as described by Nora Jones yesterday, radical delegation as per Admiral, John Richardson also described yesterday, and we are convinced that these are all parts of a larger whole. So just as there is a cohesive set of principles and practices around Taylorism and scientific management and the a hundred year old practices of the can't charts, centralized planning, and execution and command control, there must be a set of cohesive practices that explains all the practice that we love so much here within the DevOps community. So here is Dr. Steven spear to talk about an amazing experience support of these principles on practices in a pharmaceutical development setting. Here's Steve,
Hey gene, thanks for that. Very flattering and erudite introduction. Uh, so, you know, I'll pick, pick the ball up with this. One is thinking about the situation we're trying to, um, understand, which is we, um, assemble all these experts with all this different specialization that they have, and we put them to work and we think, oh, this is fantastic. Not only do we have this distributed intelligence, but we have this collective intelligence and, and the yield out of a hole should be so much greater than the yield out of the individual parts. And then we look at this and it's like, oh my gosh, people are working so hard every day. Yeah. W where's the beef, where's the outcome of all of this. So, anyway, what I want to do is share with you a case where, um, people really tackled that problem to root cause and came up with some good explanation, which I think generalizes as to why, um, we work so hard and gets a little and how to flip that.
So we work less hard and get a lot more. So here's the situation. This is, um, a story about a, a bunch of very talented, very educated well-meaning, um, natural scientists, life scientists, biologists, chemists, computational biologists, and so forth. And what they're trying to do is develop therapeutics, uh, so that other people will feel better, live longer, do more. And, uh, just as quick background, um, developing a new therapeutic is a very, very expensive thing to do. And it takes a lot of time almost, uh, sometimes a decade and, um, upwards of a billion dollars. And if you think about the returns on this, it's, it's, uh, it's an industry which has this tremendous winnowing from the ideas that get started in the pipeline of T equals zero down to the, the one or two that squeak out at the end. Now, obviously that's a huge financial hurdle for the, um, providers of therapeutics.
Now imagine the people who go into this line of work, they go to, um, all this advanced education with the aspiration of doing something helpful that other people appreciate in terms of therapies for disease. And if they start on something, it might be, it might be a decade till they get results and they might not get results again, that winnowing thing. So anyway, this, um, team of natural scientists asked the question, why does it take us so long and why are our yields so low? And, um, they, they look upon this with a little bit of, uh, a little bit, a lot of envy because in the, um, the hardware space, right? Um, they've been bragging, you know, for, I don't know, 50, 60 years about Moore's law, which is, oh, gene, you know, every two years, just hang on, we'll give you something even faster, even better.
And it costs less money. Um, in the pharmaceutical industry, they don't have Moore's law. They have the reverse to that. You know, they, they dubbed it Aaron's law. And the thing about Aaron's law is that, uh, it seems that for every therapeutic, it takes more and more time and more and more money to get to a result. And so th this is kind of, what's motivating this question of why are the pieces not coming together, or how do the pieces come together? How can the pieces come together together better so that we spend less money, less time and get more yield. So, anyway, for the purpose of our case, we're looking at a phase of, um, uh, drug discovery called hit to lead. Now where this fits in is that there's a group of biologists, primarily who do something called target selection. They figure out for a particular disease, what protein seems to beat to be the one that's misbehaving inside a cell that's causing them the disease.
And then that get passes up, gets passed over to another group of people to try and figure out where can we attach onto that protein, a, the molecule, which we'll get to the protein to behave differently than it otherwise is behaving and behaves nicely rather than a badly. And so that's called a hit. And if you want to think about what a hit is, what a hit is, is, um, it's kind of like a, you have a, a plot and a basic floor plan is idea. We'll put, put a building here and that'll add value to this plot of land. It's kind of what it is, where the protein in this picture is the orange and the void with trying to fill in as the white and the head. This is kind of the rough sketch, is that a little purple, um, blue thing, and the job of, uh, chemists and biologists in this phase called hit to lead us to develop that further.
So, um, they have a lead, which is actually worthy of being passed over to people who do something called lead development that, uh, it just might have a chance of going into clinical trials. So that's, um, th the, the phase of the process that we're looking at here now, um, the way the scientists do their work is they go through a cycle, which is probably familiar to everybody, then maybe different names, right. Which is they think a lot, right. And they come up with a design and then they actually have to physically synthesize that. So, you know, for the software engineer, maybe that second phase is like coding, then they have to figure out how well the, um, the thing that's been synthesized, how well it behaves, that's kind of like debugging. They go through a couple of these, not a couple, but they go through several of these thinking, making, uh, debugging testing loops to try and converge down to a compound and parts of a compound that seemed to perform better than not.
So they, um, start, you know, around this question, why does it take us so long? And why do we yield so little? They said, well, how long really does it take to go through several cycles of design make test? And they figured, you know, convergence is maybe through three cycles of this thing. So they looked at data and said, well, how much time do we spend designing, boom, how much time do we spend making boom, how much time do we spend testing? And they said, you know, probably a reasonable estimate, you know, of touch time here. Process time is about, you know, 50, 51 days, um, to, uh, get some kind of meaningful convergence. Well, when they looked at the actual data, what they found was not process time, touch time, but transit time for a compound through all of this was a double to quadruple.
And they were like, what the heck? Why, how can it possibly double do quadruple? So then the lead chemist on this program, on this pilot, she started looking at where ideas resided in this system of very smart people in chemistry labs and very smart people in biology left. Where were the ideas? Now you would like to think that someone has a good idea. Let's say gene has a good idea. He hands it to Steve. Steve works on the idea. He hands it to Aaron. Aaron works on as she entered the Anthem, Marguerite that era, right. You know, boom, boom, boom, boom. It goes around. What they found instead is gene works on something and it goes up on a shelf. And then Steve is working on something else. It goes on, on a shelf and Aaron walks over to the shelves and picks whatever the heck she and puts her stuff on a shelf.
And when they looked at the ratio of things being worked on versus ideas, which just sitting around for the next step, the ratios was like all sorts of, out of whack. And they started getting into the question then why is it, why is it that we thought we had these processes was flow right on through? And instead we have, so they take a look and they say, well, you know, what did we think we were doing? So what they thought they were doing is they thought that chemists would be, uh, you know, collaborating with other chemists on the designing and making things. And biologists would be collaborating with biologists on the development of these acids, the development of these tests and running the test, they'd be collaborating back and forth with each other and what they found instead that no one was talking to anybody else.
I mean, that's an extreme, but almost nobody was talking to almost nobody else. I said, well, why is that? And, um, I think now we'll start getting into their diagnosis, which I hope rings true elsewhere is that, um, they were organized around specialty. And let me, hold on. I'm not saying that every organization should be flattened, cross this thing and cross that thing. There's a lot of reason to have silos because you get kind of critical mass of the molecular biologists. I'm sorry, the molecular chemists talking to other molecular chemists and saying, oh, what do you think? What do you think? What do you think? But when they started the fragment, it's just Steve, you know, working away, working away over here and maybe Martin area is working away, working away over there, and you got Anna working their way or no, one's talking to anybody else.
So the lead chemist, she says, wait a second. Why are we not having these collaborative conversations to tap into collective intelligence? And she realized that, and maybe it once existed that someone had actually drawn out the flow of work, who depended on whom for what that, you know, when Steve is doing his work, he is depending on gene to inform that work. When Steve is doing his work, he's doing it because Aaron depends on him to inform hard work and someone and so forth. Even if it once existed though, that shared sense of the system decade. And the only sense we have is very local what's in front of us. So the very first step for this lead chemist, what she did is she started making people aware, not only of their role, that's kind of like a title, not only of their responsibility, you're working on this compound or that part of a compound, but relationships, you know, back to this example of Steve is dependent on gene and Aaron is dependent on Steve and so forth.
Now, um, what starts happening with that is that, uh, once this lead chemist started making people more aware of these mutual interdependencies, it became both a trigger inspiration, licensed to start having conversations rather than just being stuck at the, uh, the benchtop. And I started having conversations. It became possible. Um, not only to like focus on the work directly in front of you, but to start having conversations with the person, uh, with whom you, you know, with whom you have a relationship and say, Hey, you know, Aaron, I'm working on this, what you working on? Oh, I'm working on this thing. Oh, very interesting. Can you tell me more about the thing you're working on? Oh, I didn't know about that. Let me tell you about what I'm working on. Let's compare notes. How did these things compare? Which should we do first? Which should, should we do second?
You know? Alright. And all of a sudden you started getting this critical mass, this critical mass of, um, because whatever Erin is working on on her bench top and Steve and Jean and Marguerite and Hannah and Anne, now, all of a sudden the ideas of starting to, um, collide with each other and mix with each other and synthesize with each other. So one of the things that came out of I'm starting to have these, um, much richer conversations within a silo is that, uh, you know, when I show up with my work and I say, oh, well, Aaron, let me explain my work to you. Cause I've got this, um, data behind it. She says, oh, let me take a look at that data. Can you explain it to me? I discover, wait a second. Where is that data? Why am I having this conversation that, that has been sitting around or I'm having this conversation before that arrived?
And so then just by having the conversation within the silo, it raises awareness that there was another silo with a whole bunch of other, very talented people upon whom this silo, the chemistry silo, the chemistry lab depended on. And so they started trying to synchronize their conversations within chemistry, with the timing of the arrival of information, um, from biology. And now they're strong look at the data and they're, you know, if the data shows up on a Tuesday afternoon, they're having conversation on Wednesday. And so they're starting to get more in sync one laboratory with the other, but then when they're looking at the data to start realizing, Hmm, son of a gun, I'm not exactly sure. I fully understand it. Wait, Jeff, you know, Jeff, the biologist, can you come over here? I, oh, I'd be delighted to explain the context, the nuance, the subtlety of what the data means.
And he says, and actually, while I'm here, can you explain back to me what exactly you were looking for when you wanted to test that compound? Cause maybe I can be more precise and more thoughtful about the, uh, the set of tests that I construct for that. So, um, what you end up getting with is this first conversation, which is sort of liberated by showing who's in a relationship, it becomes a more frequent, richer, wider bandwidth conversation. And then it leads to other conversations which are not only more frequent, but better synchronized, better harmonized. Um, so that the work over there is now coincide, uh, coinciding synchronized with the work going on over here now where this continues is that, um, as the chemists are having this, uh, collaborative collective intelligence conversation within their silo, and then it becomes a collaborative collective intelligence conversation across silos with biology, then they start realizing, oh, wait a second.
There are other people with other specialties where, um, we're not necessarily well synchronized harmonized with what they're doing for us and what we're doing for them. And so what didn't, you know, this a very simple, simple, I say, but you know, simple act of I'm the lead chemist mapping out relationships lead to this cascade of conversations. And this increase in awareness of who's in relationship to whom, um, and who consequently, because they're in relationship has to have conversation rich conversation. And he would where's this land is, um, when, uh, these chemists and biologists started thinking, you know, what's our starting point. They, they benchmarked against the previous programs and again, to emphasize previous successful programs and this phase of hit to lead sort of a rough idea to something worthy of further development, um, that the benchmark said took over a year and of these design make tests, you know, uh, think code the bug cycles, um, up to 60 of them and the pilot as a, as a consequence of having these a better richer, more expansive collective intelligence conversations.
Um, they got done the pilot in six months and with far fewer cycles, let's think about this, think about getting a therapy to market six months earlier and the amount of, um, alleviation that would mean. Now, imagine you start picking up these six months here, six months here, and then you're getting therapies into the marketplace years earlier, the amount of suffering that alleviates, I think about the enormous financial rewards to anyone who could actually do this on a consistent basis. It's simply off the charts now just to put this in some, um, present context is, um, when, uh, COVID hit, you know, start of 2020, um, and we realized how bad it was, you know, at first, when we hit with wow, you know, be dismissive. But when, when we first realized it, we said, oh my gosh, vaccines can take, uh, you know, five, 10 years.
And we were being told by CDC and others like, oh, look, you know, we'll be lucky. It'll be a miracle if we get something within two years. And here we are vaccine developed within a year through, um, enough trials to get, uh, you know, sort of, uh, emergency certification, whatever the term is. Um, and here are about a year and a half and we're going to be vaccinated. I mean, it's freaking miraculous. Now here's my theory about that is that because this was a pandemic global level crisis that, um, the folks who developed vaccines, they discarded their old isolated, their legacy silos within silos, within silos, with ideas being worked on and on a shelf and worked on it on a shelf and they to streamline the flow of a conceptual work through the system. So anyway, that's my theory as to how we got, um, so many vaccines, like a handful of them, and so much less time than anyone predicted. Anyway, gene, uh, let, let me just finish off where I started is that, you know, we're desperate for our, um, collaborative work to tap well into our conceptual art. I'm sorry, our collaborative work to tap well into our collective intelligence. And I just want to offer that it's not just an empty hope. It's a reality if we do what these folks did. Anyway, thank you back to you.
Thank you, Steve. So Steve and I have been thinking about this problem a lot, trying to piece together, the common principles and patterns in transformations like this. And so here's something that we wrote. Sometimes it just doesn't seem fair. You've done everything right in your career. You've won all the tournaments to require required to get you where you are. You've been tasked with solving the most challenging problems in your organization so that you can win in the marketplace. Everyone in your organization is working hard to solve problems that no one could solve individually to develop design, great products and services to beat the competition. And yet competitors keep beating you arriving earlier and faster with solutions that customers love generated with seemingly less effort and they keep pulling away from the pack. And so one asks, how do they do that when you share the same starting line and allegedly a level playing field using the same science technology, the same talent pool and the same market information.
And sometimes you may even feel that you and your organization are unable to respond effectively, that you feel as though your organization's actually fighting you. And so Steve and I are now wondering, uh, that maybe it actually is because you are structured to be slow when you should actually be structured to be fast. And so I've talked to in years past about our quest to understand how organizations, uh, work the way they do both in the ideal and not ideal. And we're starting to come to believe that you can predict whether an organization is high-performing or low-performing just by looking at the communication paths within the organization and what is the frequency and intensity of those communications. And so, uh, we assert that, uh, there is this slower integrated problem solving style where that in order for, uh, two individuals from two different functional silos to work together, requiring vast escalations up and down the org chart, maybe up eight and then down eight.
And so this has a couple of problems, right? Is that, uh, when things escalate leaders are getting incomplete information, uh, often too late. Um, and the result is that teams don't have access to the expertise they need deprived of the full creative problem solving potential. And the reason is that everyone's trapped in these functional silos, these cones, where they're not allowed to talk to people in other silos, because that was just not allowed. And so what Steve described in the pharmaceutical development example is that they change the structure. That's the majority of communications. Uh, the majority of integrated problem solving is happening at the edges. And when things escalate as slate up eight levels, instead the escalate up one level, Steve described how by making these very explicit value streams, uh, where, uh, the relationships are enabled by sanctioned interfaces where integrated problem solving, uh, can happen, uh, at the edges, magical things happen.
And, uh, as he mentioned, uh, also it's actually easier to change the system in this mode because the organization can dynamically change itself because the structure, uh, is simpler. Uh, and so, uh, if you haven't picked this up, uh, we're obviously borrowing the language from the amazing book thinking fast and slow by work, um, based on the work by deco Dr. Daniel Kahneman and, uh, uh, Dr. Amos Tversky. And so, uh, for tutoring for fast integrated problem solving and slow integrate problem solving is really a proxy for four measures. And so in control theory, there are really four axes there's frequency there's latency. Uh, so in other words, are we reacting to old information or are we reacting to near, uh, real-time present conditions, uh, and then the granularity or detailed information, as well as the accuracy and fidelity. And so in operations, we very much favor the second to frequency and latency.
Uh, whereas for planning, we very much favor the first two. In other words, we don't want to make plans, uh, based on categorically false information. And so, uh, what I find so amazing about that slide that Steve showed about Aaron's law is that as we increase the number of functional specialties, that must work together to solve tough problems, uh, we feel the effects of that in, uh, the amount of difficulty and expense of creating solutions. So I love this graph that shows for every given billion dollars, the number of pharmaceutical therapeutics, uh, being able to be generated, uh, is actually going down, uh, logarithmically, uh, the exact opposite of Moore's law. And so we see the limitations of the slower integrated problem solving style in so many domains. This is the story of why in team of teams, they weren't able to successfully dismantle the kata interact terrorist network because despite being larger and having better technology, Steve described what's happening in pharmaceutical development.
Uh, this is what led to the creation of agile and DevOps within software development and delivery. Um, and as often hampering vaccine rollouts, and is exactly the symptomology, in my opinion of why so many organizations are having difficulties in deploying OKR is objectives and key results as described by, uh, John smart and, uh, Dr. Mccarsten. And in other words, we are clinging firmly to the slower integrated problem solving style. And so really if we take a look at the domains of planning, operation improvements, uh, we can see, we can start to bifurcate which activities should be in the slower mode and which ones should be in the fast modes in planning. Uh, so these are the slower cognitive activities of setting system level goals, designing the organization, uh, how we organize teams, uh, the relationships between them, uh, also, uh, defining and deploying objectives and key results.
Um, and then leadership should stay out of operations, uh, because that's mostly in the business of expediting. Uh, instead they, we come back to the slower problem solving style in improvement as a system where we ask is a system achieving the system level goals. So this in the book team of teams, this is when general Stanley McChrystal asks, are we, despite all the tactical wins? Are we achieving our strategic objectives? And his answer was, uh, you know, absolutely not. And that which led to, uh, the breakthroughs described in the book, this is what we do in agile retrospectives. This is what we do, uh, in halftime and in an American football game. And so, uh, the domain of operations, uh, is where we are using the faster integrated problem solving style. This is where the majority of communications that interactions are happening within the teams or between teams using sanctioned interfaces.
So, uh, just as Steve described how they changed the relationship of how biologists and chemists work together, they've still remained one of the coauthors of, uh, the book team of teams as he presented last year. Yeah. There's wonderful language for it is that when, uh, leaders can radically delegate leadership, what does that do for those teams? It says it is this amazing, magical feeling of when you, when their leadership is eyes on. Uh, but hands-off, um, and the inverse is that when leadership reaches down into daily operations too much per he had a term for that too, it is when your leaders have pulled, uh, your decision space up. In other words, there are decisions that you were making that are now alone, longer, no longer yours to make, uh, which is actually a terrible feeling, because that is now depriving you of your full creative problem solving capabilities.
In Steve story, he described how, uh, Beth, the head of a chemistry changed, how chemistry, uh, chemists and biologists work. Uh, they enabled chemists and biologists to change the way they work so they can better jointly co-create a new knowledge. So there's one other, uh, case study that I'd like to bring up just because in my mind, it demonstrates so vividly that these two different types of problem solving styles, and that is United airlines flight 2 32. And so this was a regular lease scheduled, a United airlines flight, uh, in around 1989. And it was a DC 10 that suffered the loss of all three hydraulic systems. That was the first time that that had happened. And, uh, that was a condition that, uh, was arguably one that should never happened. The captain of the flight, uh, gave a lecture at NASA Ames instantly. This is a, it's a famous store for many reasons.
One of which it was that it was the first, um, disaster that fully utilize what they called crew resource management. Uh, they changed the dynamics of how people acted and reacted within the airplane cockpit. So I remember reading this in 1995 and he is describing, uh, what happened. And so I'm reading from the transcript. The link is in the slides. Uh, he said, I'm going to dramatically, re-enact his talk. He said on July 19th, Murphy's law caught up with us and we'd lost all three hydraulic systems. As a result. We had no ailerons to bank the plane. We had no rudder to turn it, no elevators to control the pitch and the leading edges at the land, no slats, this'll slow the plane down, no trailing edge flaps for landing. And, uh, we had no spoilers on the wing to help us get us down. Once we were on the ground, we had no steering, no nose wheel or tail and no brakes.
And so, uh, apparently they had a simulation. I think that, that they ran, uh, before his talk. And so he said, as you saw, uh, the number one and number three engine controls were frozen. And that was the only means of controlling the plane. Another really unusual aspect of a United flight. 2 32 was the fact that there just happened to be in the passenger cabin of the plane, uh, captain Fitch. Here's how captain Haynes talked about that. Um, we learned that, uh, there was a DC 10 captain in the back. He was an instructor, and we'd like to think that instructors know more than we do. So I figured that, uh, captain Denny Fitch, uh, might know something that we didn't. So we asked him to come up and he, uh, came into the cockpit to one look around and, uh, that's his knowledge. And it is sort of funny listening to the transcripts because he is 15 minutes behind us.
Now he's trying to catch up with where we are and everything he says to do. We've already done. And after about five minutes, and that's now 20 minutes into this emergency, he says, uh, we're in trouble. So, uh, we thought that's an amazing observation, Denny, uh, there's laughter in the audience. Uh, and we kid him about it now, but he's just trying to catch up with our thinking. We're 15 minutes ahead of him. After that he asks, what can I do now? So I said, you can take these throttles and help us steer. He took one throttle and one hand another and the other, and, uh, the number two throttle frozen, uh, this is something that the pilot and the copilot could not do themselves. So we said, give us a right bank, bring the wing up. That's too much spank tried to stop the altitude.
He tried to respond after a few minutes of doing this, everything we had, uh, everything we do with the yoke, uh, he would correspond with the throttle. So it was a synchronized thing between the three of us with Dudley. I think that's the flight engineer, still being able to do all his communications with the air traffic control. And so that is how we operate the airplane. And that is how we got it on the ground. So there was another interesting thing that happened. Uh, he describes his communications with the San Francisco area maintenance group. Uh, he said, uh, the area maintenance crew are those experts, uh, sitting in San Francisco for each type of equipment, uh, that United flies all the history of the aircraft, all the information that they could draw upon help a crew that is having a problem. But unfortunately, in our case, there was nothing that they could help us with.
Every time they try to find something that we could do that we had there already done it, or we couldn't do it because we had no hydraulics. In other words, there was nothing in their procedures to handle the contingency where they had lost all three hydraulic systems. So, uh, going to this model, um, the planning parts, the slower cognitive problem solving included, setting up the CRM protocols, establishing training of CRM across all us airlines, setting up those area maintenance crews, uh, in San Francisco. Um, so the faster, uh, integrate problem solving was happening in that cockpit, uh, trying to figure out how to fly the plane with the loss of all three hydraulic systems assessing whether, uh, the maintenance area team, uh, in San Francisco could actually help. And the answer was no assessing whether captain Fitch, uh, had some sort of regulatory knowledge that could help save the airplane.
And the answer is no, but they did assess that he actually had skills that could help them land the plane, uh, and establishing a new cockpit roles and responsibilities within the cockpit. You know, all of that happened within the fast integrated problem solving mode in this lecture, captain Al Haynes talks about how much of this success was due to crew resource management. One of my favorite lines in his talk was this, he said in that cockpit, we had over a hundred years of flying experience, none of which included flying with all hydraulic systems down. So he said, why would I know any more than my colleagues about how to land that plane? It required tapping the collective problem, solving capabilities of everyone, uh, in order to get that plane on the ground. To me, it's so interesting that the only experts that had the expertise relevant to landing that plane were in that cockpit.
Uh, there's another example that I really love where it was the opposite, and that's one of my favorite scenes in Apollo 13. And that is when they are trying to figure out how to change the carbon dioxide filters, uh, to save the astronauts. And the problem is, is that, uh, this requires fitting a square peg into a round hole, uh, which then leads to this scene, which is where the engineers all get together and try to figure out how to fit this square peg into a round hole using only these parts, which appear to be a space suit, uh, duct tape, and a bunch of tubing. And so in this case, they were the astronauts who were much able to tap the collective experience and integrative problem solving of hundreds of engineers, uh, that were able to describe how to change the CO2 filters. So just as Steve mentioned, I think the COVID global pandemic is showing, uh, how much potential there is to incredibly increase our cognitive creative problem solving skills.
Steve mentioned how, uh, we have not one vaccine within a year of first being identified, but, you know, five that had been approved for emergency use. Um, Steve and I had the privilege of talking with the chief operating officer of one of the healthcare systems here in Portland, Oregon, where they increase the number of vaccinations per day from 2000 to 8,010. So here's a picture of Dr. , Trent and me, uh, here at the convention center in Portland. And so that is what Steve and I have been working on. Steve, I sure hope this resonates with you and that, uh, this represents our growing understanding of why organizations work the way they do both in the ideal and not ideal.
Yeah, absolutely. Jean look, I think we've made, um, a couple of strong assertions and I'd be delighted if we could get feedback from the people watching, listening as to whether, uh, what we're saying resonates or doesn't at all. And those assertions are one week we have, uh, uh, cause and consequence that this loss of critical mass by people being disconnected across boundaries and consequently isolated in silos and an isolated not only isolated silo to silo, but within silos, that's a common experience. I think it is. That's why we offered it, but, you know, is it or not? And, uh, that being a cause and the consequence being that, um, uh, people working very, very hard, but not, not necessarily in a way that's harmonized synchronized synthesized so that they're working very hard, but they're getting, um, I'll put less than they hope and want. So anyway, true or false.
That's our question. And then, and then, then the other kind of, uh, validation reputation we want is we offer a corrective action, which is, um, making clear, not only roles and responsibilities, which is a very, very egocentric view of the world. This is my title, and this is what I do, but relationships, which is now a very expansive view that, um, our recommendation or our suggestion is that that is a corrective action will, um, liberate the opportunity for collaborative conversation and, and expansive collaborative conversation, which will help tap into collective intelligence. And again, you know, the, the question back to, uh, viewers and listeners is true or false makes sense, or doesn't make sense. Those are all right. Yeah.
And by the way, I'm reminded of this amazing part of the H and M presentation this morning, where, uh, Daniel Clawson said, when he saw what happens when you have a merchandiser sitting next to the developer, that magic happens as opposed to, you know, stuck in their respective silos as so much of a, this is being pieced together in this podcast, uh, that I've been doing called the ideal cast of which Steve has been involved in so much, uh, as well. In fact, Steve and I, uh, in two weeks, we'll be releasing an episode where we actually interview Trent green, the chief operating officer, who is responsible for the mass vaccination clinic in Portland convention center. So, uh, so great. And Steve, any help you're looking for?
Yeah, no, the feedback on this stuff. And we've created some tools to make that mapping of relationships easier. If you're interested, you know, you can find us,
Thank you so much
Over and out.
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Dominica DeGrandis' Essential Data To Enable Systemic Change Playlist