Continuous Learning Culture A continuous learning culture can positively impact an organization’s performance, innovation, competency, and competitive advantage. But what exactly is it and how do you create one in your organization for successful SAFe business agility? In this episode, Jeff Shupack, SAFe Fellow and SPCT at Product and Team, join us to define continuous learning culture, explain why it’s important, and describe what it looks like in the real world. Click the “Subscribe” button to subscribe to the SAFe Business Agility podcast on Apple Podcasts Subscribe Share: A continuous learning culture can positively impact an organization’s performance, innovation, competency, and competitive advantage. But what exactly is it and how do you create one in your organization? In this episode, Jeff Shupack, SAFe Fellow and SPCT at Product and Team, joins us to define continuous learning culture, explain why it’s important, and describe what it looks like in the real world. Melissa and Jeff discuss: What happens when an organization doesn’t have a continuous learning culture?The shift in thinking from the birth of Lean to the present day.Organizations that are successfully exemplifying a continuous learning culture.Pitfalls to avoid when trying to roll out a continuous learning culture. Follow these links to learn more about these topics mentioned in the podcast: Carlota PerezDaniel PinkElon Musk’s tweetMik KerstenPeter DruckerSimon Wardley Hosted by: Melissa Reeve Melissa Reeve is the Vice President of Marketing at Scaled Agile, Inc. In this role, Melissa guides the marketing team, helping people better understand Scaled Agile, the Scaled Agile Framework (SAFe), and its mission. Find Melissa on LinkedIn. Guest: Jeff Shupack Jeff is President of Advisory Practice at Project & Team, specializing in digital transformations within highly regulated and complex environments for the Fortune 100 and government agencies. A SAFe Fellow and SPCT, he is a sought-after transformation expert focusing on digital enterprise, cyber-physical delivery, and change leadership. Connect with Jeff on LinkedIn. Transcript Speaker 1: Looking for the latest news experiences and answers to questions about safe. You’ve come to the right place. This podcast is for you, the SAFe community of practitioners, trainers, users, and everyone who engages SAFe on a daily basis. Melissa Reeve: Welcome to the SAFe Business Agility Podcast recorded from our homes around the world. I’m Melissa Reeve, your host for today’s episode. And joining me today is Jeff Shupack, SAFe Fellow and SPCT at Project and Team. Thanks for joining me today, Jeff. It’s so great to have you on the show. Jeff Shupack: Very excited to be here, Melissa. Melissa Reeve: So in this episode, we’ll discuss continuous learning culture. Why it’s important, what it looks like, and how to start creating one in your own organization. Let’s get started. So Jeff, let’s start out and ask what is a continuous learning culture? Jeff Shupack: That’s a great question for me. A continuous learning culture is where we have an organization that is collectively trying to increase its knowledge and have that knowledge integrated. That helps performance. It helps innovation. It helps competency. It’s about these short, quick cycles that I can learn from which I can then immediately put into place to have internal innovation and more market competition. Melissa Reeve: Well, it seems like such a good thing for an organization to have, and yet it seems really hard. So what happens if an organization doesn’t have this continuing learning culture? Jeff Shupack: They become stagnant and ultimately lose some market dominance that they may have. It turns out that the products that either business customers or consumers want today won’t be the products that they necessarily want tomorrow. And if competition realizes that and adjusts to those changing market conditions quicker than the organization, that you’re representative, your product lines are going to become stagnant and no longer competitive. Melissa Reeve: Can you talk a little bit about how we got to where we’re at today? It does feel like many organizations have always tried to optimize their business. So maybe you can talk us through the history of what that’s looked like over the past 40 years or so. Jeff Shupack: Based on a lot of the work from Carlota Perez and Mik Kersten who have spent a lot of time studying the technological revolutions, I invite listeners to also check those out, but essentially there’s been a shift in how our organizations are optimizing and what they’re optimizing for. Back in mass production, you know, going back to the birth of Lean, we were optimizing for a number of goods that could be created through our system; the number of widgets or sprockets that we can get out to our consumers. And we focused heavily on leaning out various types of waste, you know, Ford, Toyota production system. Our great methodologies to kind of start from where you saw a lot of this. And as we optimized to reduce waste, we also optimized to reduce learning inadvertently. And that’s very different from what we want to optimize today. Today, we want to focus on rapid learning. And I can accept the fact that the widget that I’m building today is not the widget that I necessarily want tomorrow. And I want to learn as quickly as possible so that widget can be more useful for our consumer of that value at the end of the day. Melissa Reeve: So, it sounds like we need to learn quickly. And what I’m hearing you say between the lines is that we actually need to create a little bit of space for that learning. And even though Lean is really focused on eliminating waste, we can’t allow that space for learning to be considered waste. It’s actually very valuable. Jeff Shupack: Absolutely. There used to be significant barriers to entry for a lot of these organizations and the competitive advantage was I think these large manufacturing spaces. But now we’re finding that organizations must adapt rapidly to this changing business climate. You see it, right? We hear about AI, artificial intelligence, machine learning, and big data and cloud. And as these organizations are becoming more data-centric, or they build out this data foundation, they’re getting rapid insights from this data that’s informing where they can expand where they need to change. And so what we need to do is really focus on learning and optimizing around learning. And then we need ultimately an operating model that sits on top of this data-centric core that enables us to adjust rapidly. And that operating model itself needs to enable learning. Melissa Reeve: So are there a couple of companies that you could highlight that are doing this well? Jeff Shupack: Yeah, I think there are some that are definitely leading the space. I think all are trying to do it stronger, but let me give you a couple of examples. The classic example we tend to look at is Tesla, right? So Tesla will actually certify a car for roadworthiness on an individual car basis. This is highly unusual in that space. In general, we usually historically have certified a model year for the entire duration to be certified. And what Tesla’s doing is they’re saying, well, maybe we want to change or learn from car to car. This car on your Tesla may have a different adhesive than say my Tesla coming right off the line. I think another example really going back to Elon Musk’s company is SpaceX. If SpaceX is launching three rockets a week, I don’t want a factory that continues to produce the exact same rocket. I want every rocket to be an improvement based on the results of a rocket launch or the experiment of the rocket launch that came previously. So we’re trying to have these rapid learning cycles as quickly as possible. Tesla is famous I think in the software space for having, I believe they have four-hour sprints, four-hour iterations. Which are basically saying, we want to experiment every four hours to rapidly learn to see how we can adjust. Melissa Reeve: I’m listening to you talk about this. And my mind is really blown thinking about certifying a car for a model year versus learning every four hours. It’s just such a radical shift. I mean, how does somebody even start down that journey of shifting their thinking? Jeff Shupack: It starts with a mindset. So if you were to ask and I think Elon’s tweeted on this, what is Tesla’s product? He’s not gonna say Tesla’s product is a Model S or the Model X or the Model 3. He’s going to actually say the factory’s the product. We need a factory that is built as a system that builds systems that can learn as quickly as possible. And that’s just a mindset shift. It’s a whole optimization shift from our classic Taiichi Ohno Toyota Production System type of Lean. It’s, how do we actually have a factory that can change based on these learning cycles? So, it probably starts the mindset more than anything. And then, and I think the second is probably going to be a lot of experimentation. You know, how are we measuring that we’re actually getting the outcomes we want. So, if I were to guess on three things, I think the first is probably to generate transparency: where is learning occurring and where is it not occurring? Are we actually allowing ourselves to learn in the areas that are a competitive distinction? Or are we only allowing ourselves to learn in these auxiliary areas outside of our core competency? So really applying that systems view. Melissa Reeve: Yeah, that makes a lot of sense. So getting the right mindset, does that involve, how do you find people with the right mindset in order to start generating some of that transparency and getting into the right frame of mind? Jeff Shupack: I think you start with the few of the folks that already have it and you get them aligned in a way that can help them make it more visible. There’s a couple of tools that I think are, you know, historically pretty successful at getting this out there. We’re pretty familiar with the concept of value stream identification, I think that helps also. But I like to also mention Simon Wardley’s model that helps us understand competitive advantage. You know, what should we insource and what should we outsource? And so here’s an example. If we are manufacturing an autonomous vehicle, and I’m still really focusing on a braking system as my competitive advantage, maybe I’m focusing on learning in the wrong area. Maybe I should really be focusing on collision avoidance and looking to outsource braking. So, I think part of it is really to make sure that we’re intentional on where our limited focus is. And then how can we double down on that focus with these rapid learning loops? Melissa Reeve: All right, so we’ve talked a little bit about the mindset. We’ve talked about the need to generate transparency. You’ve just pointed us in the direction of some alignment tools, you know, Simon Wardley. It also feels like it could be really risky when you’re thinking about learning this quickly, that, you know, typically when you’re in a learning cycle, you need to be able to fail. How do you do that when you’re in four-hour sprints? Jeff Shupack: Yeah, right. I think if we asked any of these companies about the risk tolerance most are going to want to say “we’re just risk intolerant.” The risk of failure, the risk of the market reaction. If you’re working in a highly regulated environment like DOD or the intelligence space, it could be catastrophic consequences, not just for the organization, but for the global, you know, footprint of us all. So I think risk recovery is critical. One of the things that we tend to neglect is an elegant way to recover. See, the best way to deal with risk is to draw down on risk in small batches. And I have to architect them up-front, an elegant way to recover. You know, the classic example is it’s easier to not just troubleshoot, but rollback, in general, 100 lines of code, opposed to 50,000 lines of code. And I think sometimes when we consider how we’re architecting a system, how we elegantly recover—and the keyword there is elegance—tends to be a second-class citizen. And I think that has to happen very up-front in our development cycles. Melissa Reeve: So, I can see that with code, how it’d be easy to update 100 lines of code or push an update into a system. What do we do in our Tesla or our SpaceX examples where we’ve just tried a new adhesive and it’s not quite working? Jeff Shupack: Yeah, it’s a great example. I think the answer is to become more data-centric. So, playing on the Tesla example, all these cars are really giving real-time road feedback back to a centralized server back at Tesla that is saying, how is this experiment performing? Does it turn out in a short amount of period that that adhesive had unusual wear or the vibrations based off this shock mount were having some other creations? Because we can take a look at every single product that’s going in there as a separate SKU, we can treat each car as a different experiment and let that real-time feedback inform the next batch. And it may not happen on an iteration per iteration basis, but because I have the data going back somewhere where I can process it, I can start using data insights and correlate some of what’s traditionally uncorrelated data and really have some decision-making based off that. So, in addition to just running experiments, I need the data to then inform what the next experiment is I should run. You know, and I know I’m using Elon Musk’s company as an example, but it’s a great public example. I recall when one of the rockets did not land appropriate and Elon’s tweet was something to the order of magnitude of, “This is great. We got all the data, we needed. It wasn’t, “we lost the rocket and the mission wasn’t successful,” it’s we got all the data we needed. And that’s the point; it was a data-driven test. And that’s, that’s a mindset shift, you know, that not how we used to run off rockets in the past. Melissa Reeve: Absolutely, there needed to be finger-pointing and somebody to blame and, oh my God, what just happened? Jeff Shupack: So there’s a culture play, right? So continuous learning culture is how do we actually get the shift within the organization to not finger-point? And how do we get the shift that everything that we begin to do is treated as an experiment with a benefit hypothesis? So, I’m not sure I’m going to get this benefit. I need to prove by doing this thing that I got the benefit I thought, and then ask your classic Lean-startup question: Do we go on to the next? What did we learn? And let that inform the next action that we’re going to take. Melissa Reeve: Alright, so I’m an executive, I’m learning about this continuous learning culture. It sounds great. I think I want to try and roll it out to my organization. What are some of the pitfalls for me to avoid when rolling out, either roll out or engender this in my organization? Jeff Shupack: I think one of the biggest challenges is really realizing what we’re optimizing for. So the goal here is to optimize for learning. And when we hear that, I think we tend to think about the individual learning, and here’s what happens, Melissa. Let’s say you’re a subject matter expert on a team. You know something. I’m a subject matter expert on another team. I’ve learned something. What we’re really integrating at the end of the day is knowledge. You know, and to borrow a software example, if we’re both branches of knowledge, the game becomes, how do I integrate your branch of knowledge and my branch of knowledge into a single trunk so it becomes shared. So, knowledge sharing is really the goal of the continuous learning culture. It’s not that we had the individual components learn, it’s that we were able to share that learning to the system of systems as a whole. And that’s probably the pitfall. I think some organizations have really established strong innovation or ad hoc learning on a localized level. The real game is how do I bring that into the rest of the organization so we become a learning organization or a learning organism, opposed to the ad hoc approach. One of the things I’ll mention is, you know, from Peter Drucker, right, he’s got the quote, “what gets measured gets done.” So are we measuring learning and the integration of that learning back into the organization? Or are we really just talking about it, but because we’re not measuring it, we’re not seeing it move. Melissa Reeve: Yeah. I mean, that makes a lot of sense to me. And it does feel like it’s a challenge for any one of these very large organizations. You know, how does the left hand know what the right hand is doing? And there’s got to be these pockets of learning. How do organizations start to tackle that? How do you stop branching the learning? Jeff Shupack: I think part of the pitfall is the word continuous. I’m comfortable with continuish, right? I don’t need it all the time. I just need it to start somewhere. The classic example for better or for worse is a hackathon, right? Where we bring in these SMEs, we let them try to create some sort of innovation, experimentation, get quick feedback. We allow them to pivot without mercy or guilt, and we’re giving them some time and space. The problem is if I run a hackathon, every, you know, every month, it’s like an innovation day, Melissa. Alright, this next Tuesday, next week, I want you to come in and be innovative. We need to think beyond that. But the reality is, that’s a fine spot to start. You want to just start shifting the mindset where we take pride in innovative people. We as leaders are giving them the time and space, we’re allowing them to have true experimentation. We’re creating, you know, these innovation riptides where somebody on a team can come up with an idea that can actually get funded and enabled down the road. You’ll see a lot of experiments around innovation and learning dojos. I think that’s fantastic, where we’ve set aside corporate funds to help encourage this. Can we tie some compensation into that? Can we make it fun? That’s a big aspect of it also is how are we making the learning aspect fun rather than just yet a tack-on of more work. Something’s got to give in the time and space to allow this to occur. It can’t be a bolted-on, an additional thing we’re asking our SMEs to do. Melissa Reeve: Well, I love that mindset. And I love that approach. And I personally have found once you get into the groove of this mindset of experimentation, so much transforms. Because instead of asking the questions of, are you right or are you wrong, it’s kind of this fun, playful, “Hey, let’s go find out.” Let’s go experiment and see what happens and what a great way to energize the workforce. Jeff Shupack: It’s big. You know, we do talk a bit about Daniel Pink and his thoughts around what motivates our people, right? And the answer is, well, we need some autonomy to let people actually explore and not micromanage your work. We need some mastery, which this is all about: give space where people can actually be innovative and chase something they’re excited about. But I think the last one is the one that most of us struggle the most with, which is purpose. I need to have some purpose. I want to see that this thing, this good idea actually drives and shifts our organization’s strategic approach. And I think that’s part of it too. The motivation is huge. It’s not just about creating time and space, but we want to encourage folks to share the learnings rather than also holding them close to their chest. Melissa Reeve: Those are some great insights. And I really appreciate you spending some time with us today to talk about continuous learning culture. Jeff Shupack: Oh, thank you, Melissa. It was fun. I enjoy this topic clearly and I’m excited for folks to go try it out. Melissa Reeve: Thanks so much. And thanks for listening to our show today. You can find helpful links about topics we covered today in the show notes at scaledagile.com/podcast. Be sure to revisit past topics at scaledagile.com/podcast. Speaker 1: Relentless improvement is in our DNA and we welcome your input on how we can improve the show. Drop us a line at email@example.com.