The high-tech industry is extremely innovative. Design thinking is an effective method to innovate effectively and even faster, says Rex Bierlaagh, trainer of ‘Innovation with design thinking‘.
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Over thirty years ago, I started my first company. Since then, I’ve started more companies, acted as an angel investor in more than ten ventures and as a board member or advisor in several others, and interacted with numerous founders and entrepreneurs. In addition, I’ve collaborated with intrapreneurs inside large organizations trying to get innovations off the ground and commercialized, both while working inside those organizations as well as through consulting arrangements.
Over these years, I’ve collected several experiences and learnings that I feel might be worth sharing. Some might be counterintuitive and some might be obvious, but either way, I’ve been surprised how often people know one thing and do the other. In the coming weeks, we’ll be diving into ten lessons I learned the hard way, meaning that I lost money, time or both. Of course, there’s no better way to internalize a lesson than through personal (negative) experience. Still, quoting Elon Musk, starting a company is like eating glass and staring into the abyss, and it’s much better to learn what you can from others and make your own, novel mistakes instead of repeating old ones. So, here goes…
Most startups that I work with love the idea of not having to worry about funding for a good long while. However, I have some examples that show that too much funding causes a company to focus internally, build products based on the opinions of the founders and staff and ignore customer input. By being budget constrained all the time, the focus is on generating revenue, which automatically causes everyone to focus on building what customers are willing to pay for.
One of the hardest challenges in startups is the balance between building based on your conviction on what the world needs or is going to need and feedback from customers and the market. Only listening to what customers say will result in too small a delta compared to already existing products. However, ignoring input from the market and blindly building what you believe is needed is a sure way to fail. Entrepreneurs are by necessity rule breakers and tend to ignore the market input longer than what’s good for the business.
One of the most painful lessons I’ve learned the hard way is that ideas and research that were presented to potential customers with great and positive feedback didn’t convert well at all when turned into a commercial offering. I have cases where hundreds of people were interested in research results and we completely failed to build a cash-flow positive business around the commercial offering based on the research.
Once a company gets off the ground, there seems to be a number of points, in terms of company size, where growth stalls. In many cases, the reason for this is that the leadership team isn’t effectively restructuring the roles, responsibilities and authority in the organization. Often, the founders have a high need for control and don’t dare to properly delegate and, by extension, become the bottleneck for growth.
Few topics lead to such heated debates in startups as the pricing of the offering. The fallacy many fall into is that we need to get the pricing right from the beginning as we can’t change it once we’ve established the anchor point in the market. In practice, however, pricing needs significant experimentation with customers, meaning that you need to develop mechanisms to experiment without burning bridges.
The holy grail of sales is to create a lot of noise on social media and to see a slew of customers knocking on your door, desperate to buy what you’re selling. Several of the startups I work with start with the ambition to use that model. In my experience, until you have a valuation exceeding a billion or more, the best way to generate sales, especially in B2B contexts, is to simply scout for potential customers, fill the pipeline, start taking prospects through the funnel and get them to sign on the dotted line. Despite having started my life as an engineer, I can’t stress enough how hard sales is and how much work it is to work the funnel and to close deals.
Many of the entrepreneurs I work with, especially intrapreneurs, have experience in the industry they’re looking to disrupt. Mature industries tend to be horizontalized and each company fills a specific role and position in the business ecosystem. When starting a new company, many want to focus their energy on precisely their core area of expertise, expecting the remainder of the offering to the end-customer to be built by others. The challenge is that for new, innovative offerings, there are no ‘others,’ meaning that you need to take much more responsibility for the end-to-end, vertical integration of the offering to customers.
Even if raising funding can be quite addictive, many entrepreneurs are looking to maintain as much ownership of the company as possible. One strategy is to bootstrap the business by complementing the product work with consulting to pay the bills. This is a perfectly viable strategy, as long as you realize how much things get slowed down by having fewer resources and splitting attention between two fundamentally different types of businesses. Also, there’s a significant chance that the certainty of consulting income is far more comfortable than the glass-eating risk that comes with building a product company, causing the product side to atrophy and disappear over time.
Every company wants to be a platform business connecting multiple groups of stakeholders and levying a tax on the value created in the ecosystem enabled by the platform. Once you’ve established that position, it’s one of the best places to be as a company. However, getting there is really, really hard and it takes an amazing amount of time – if you even get there at all. The only approach I’ve seen work in this context is to first build a viable business with one stakeholder group and only after you’ve established some modicum of success, start to open up to other stakeholder groups to platformize the business.
Everyone, including myself, and perhaps excepting seasoned serial entrepreneurs, underestimates how long it takes to grow a business. The media often presents successful startups as overnight successes, but the reality is that these overnight successes have been quite long in the making. For instance, those working with mechanical systems may know WD-40 as a product to help, eg loosening bolts. What many don’t know is that WD-40 was the 40th try of the company to build a successful product. Similarly, Rovio, the company behind the Angry Birds game, had built 51 failed games before the ‘overnight’ success. For all the gung-ho attitude many associate with a startup, it’s more of a marathon than a sprint.
Starting new ventures, either as an independent startup or in the context of a larger, established, organization is hard. To increase the likelihood of success, it’s helpful to start with the right mindset and set of beliefs. As the saying goes, a smart person learns from his or her own mistakes, a wise person learns from other people’s mistakes. I hope that you, as a wise person, can benefit from my learnings over the last decades. Don’t worry about failure; you only have to be right once!
Humans are habit-driven creatures. Some research suggests that up to 95 percent of the day, the average human is purely on auto-pilot, executing according to the habits that have been built up over the years. Habits have many advantages, including not needing willpower to execute them, but of course, there are risks. The primary risk is that one easily gets stuck in operating in an activity-driven way rather than an outcome-focused way.
Once you’ve clarified your purpose (rule 1), the next step is to concretize this purpose in tangible, concrete and measurable outcomes. Failing to do so often leads to a major gap between what you say you do and what you actually do. An illustrative example is often found in startups. Every startup wants to grow its business, but translating that ambition into actual outcomes requires setting specific targets. Yesterday, I talked to a startup where the co-founder responsible for sales had a very concrete goal for the year: go from the current 4 paying customers to 26. You may debate if 26 is the right number, but it for sure is concrete and specific.
Translating your purpose into concrete, tangible and measurable outcomes allows you to evaluate whether your actions and tactics are having the desired effect. For example, most companies want to shorten the time to market for new functionality. Specifically for functionality realized in software, doing more frequent updates in the field is obviously the way to go. Transitioning from yearly to quarterly releases, however, also means that release testing, updating documentation and all other activities related to a release have to be performed four times as often. Initially, many companies look to maintain the same, frequently manual, processes. Soon, however, it becomes clear that simply executing these processes faster won’t result in the desired outcome as the overhead is too high, people complain about the repetitive nature of the work, and so on.
When it turns out that the desired outcomes aren’t realized, the next step is to change your tactics. In our example, this means automating much of the work that’s now being conducted manually, so incorporating continuous integration and testing to increase the quality of the software well before the point where a release is scheduled. There are also tools for automatically generating necessary configurations of software, documentation, test case selections, and so on, that further limit the manual effort required to allow for more frequent releases.
If only a high-level intent had been expressed of shorting the time to market for new functionality, it wouldn’t have become clear that the current processes are insufficient. Instead, everyone would have complained about the difficulty of accomplishing things and the ways of working wouldn’t have changed.
One challenge I wrote about earlier is that we generally don’t control the outcomes of our actions. However, we can influence the outcome while allowing for other factors on which we have no influence. This means that when our actions and tactics aren’t resulting in the outcomes we hoped for, we need to assess whether this is caused by factors outside our control or our actions. For example, in stock market investing, poor returns can be the result of our selection of stocks and funds or due to a general bear market. The answer to this question can be easily answered by comparing your returns to a stock market index, such as the MSCI world index. If you’re doing worse than the index, it’s because of you. If not, it’s factors outside your control.
Translating a qualitatively defined purpose into quantitative outcomes is far from trivial. One of the challenges is that the defined outcomes often feel like approximations rather than accurate incarnations of your purpose. Here, the general advice is to follow the “perfect is the enemy of good” approach and allow yourself to start with some imperfect metrics. Once you’ve used these for a while, you start to learn where these work and don’t work. Following an iterative process, you can then, over time, come up with a better set of outcome definitions. Until, of course, you feel the need to redefine or adjust your expressed purpose.
I’m certainly not the first one to talk about these topics and several approaches exist for companies and individuals to use, including Hoshin Kanri and the Objectives and Key Results (OKR) model. The challenge, however, isn’t to pick the perfect system to follow but rather to sit down and translate your purpose into quantitative targets. For example, the Software Center for which I have the privilege to act as its director has the ambition to grow in size and impact. For 2021, the quantitative outcomes we’re looking to accomplish include adding two new partner companies and to double the number of social media connections we have on Linkedin, Youtube and Twitter.
Defining a purpose without connecting concrete, tangible, quantitative outcomes to it easily becomes aspirational without actual progress. Many have ambitions along the lines of exercising more, losing weight, eating better, and so on, without ever doing something about it and, consequently, never achieving the goal. Having clarified your purpose (rule 1) without clearly specifying the outcomes (rule 2) results in the same situation. Define a set of outcomes and, even if you’re far from satisfied with them, execute and iterate to improve over time. If done right, you’ll realize that you’re acting more and more in line with what you want your life to mean. Why settle for less?
One of the wonderful aspects of western society is that if you’re willing to work and have acquired a decent set of relevant skills, you can always find a way to make a living. I’m sure that there are exceptions, but I believe that, by and large, this actually is the case for many of us. Once you get to that point, the question becomes what you want to work on. What’s the work that feels worthwhile to spend your life energy on?
There are many answers in our culture, including making a ton of money, having a career that leaves others in awe or finding ways to get into the public eye so that you can build a reputation. Others view work as a necessary evil that we should spend as little time on as possible. Instead, we should look for ways to minimize the time allocated to work and spend time on other activities, which may include recreation or volunteering.
During most of the bigger vacations, such as summer and around Christmas, I often meet people who are incredibly happy to be away from work for a while. When you probe a little, the way many talk about their job, colleagues and responsibilities isn’t very positive. The vacation is viewed as an escape from the “living hell,” as someone called it, or the “prison” that I heard others refer to. I often wonder why one would stay in a position that one clearly hates or isn’t satisfied with. Why not choose a life that you don’t want to escape from?
That brings me to the first rule for thriving in a digital world: clarify your purpose. We’re all driven by a set of extrinsic and a set of intrinsic motivators. The extrinsic motivators are those that give some form of external reward, ie a form of reward-driven behavior. Your intrinsic motivation comes purely from within and doesn’t expect an external reward. Conducting the work brings its own reward because it aligns with your values, your purpose and what you experience as meaningful.
After I left academia to spend close to a decade in industry, in several vice-president level roles, I worked more hours than I had ever done in my life before that. Nonetheless, I couldn’t stop writing research articles. I kept at it during late evenings, weekends and vacations and even though my productivity dropped dramatically from when I was in academia, I still managed to publish 4-6 papers per year. I realized that writing research papers is my way of making sense of complex, chaotic topics and I relish the intrinsic reward of creating models or frameworks that help me understand and create order in the chaos. One of my intrinsic motivations is to use research to create novel insights and share those with others.
From this experience, I learned that I was unable to clarify my purpose and intrinsic motivation by sitting in an ivory tower and reasoning through things. I had to actively experiment with different roles, activities and contexts to experience what works for me and gives me energy and what works less well and drains the life out of me. Some people figure out early in life what they want to focus their life on, but many are less clear. My advice to those is to experiment more. You can’t figure out what your purpose is by sitting in a chair and thinking. You have to go out there and do stuff. As Steve Jobs said in the closing of his 2005 commencement speech at Stanford: stay hungry; stay foolish.
The reason why I believe that clarifying your purpose is so important in a digital world is threefold. First, the competition is, in many ways, much tougher as we’re all connected in a global network. Rather than competing with the others in your village, town or city, you’re now competing at a world level. As the saying goes, if you’re one in a million in China (or India, for that matter), there are still a thousand people just as good as you. To be successful, you need to operate at the top of your abilities. And you won’t be able to do that unless you’re completely aligned towards the work. That only happens if you’re intrinsically motivated and 100 percent in the game, which requires alignment with your purpose.
Second, digitalization allows for the automation of not just blue-collar work but even more so of white-collar work. ML/DL models are already better at diagnosing medical images than radiologists. NLP algorithms are better at scanning documents than lawyers. Autonomous-drive algorithms already are better than most drivers in all but a very small set of situations and soon will surpass humans in all contexts. The consequence is that every job or task that’s repetitive and can be described in a process will be automated. The repetition allows for the generation of data that ML/DL models need for training and the process will give a basic structure for algorithms to operate in. To stay relevant, you need to put your energy into the tasks that require all of your human skills and abilities.
The third reason is concerned with self-actualization. Maslov’s pyramid is a well-known framework for reasoning about this and once the basics are in place, which is the case for most of us, the focus is and should be on growing as a human being and as a professional into your full potential. Anything less than that is a waste of human life energy. That requires you to understand in which directions you’d like to grow and then take ownership of that journey and not allow excuses, such as blaming others, to get in the way.
I believe that all of us have come into this life to accomplish something. Something that will leave the world in a better place after you shuffle off this mortal coil. Everyone I meet wants their life to have meant something. For all but the lucky few, however, we don’t wake up one morning with a completely clear understanding of our purpose. It requires active exploration and, at some point, choosing a purpose that, based on your best understanding of your intrinsic motivators and what you experience as meaningful, is your best approximation. That purpose might change over time, but at least, for a while, you have a north star to sail by.
My professional purpose in life is to help accelerate the adoption of digital technologies in the software-intensive industry. I believe that technology, by and large, is a force for good in the world and solely responsible for the amazing progress that humanity has made over the last century or more. At this point in time, digital technologies are the most important technologies as they hold so much unexploited potential. For all the criticism of large corporations, it’s companies that bring technologies to market and through that create the associated societal benefits. So, where I can help accelerate the introduction of new technologies into our society by industry, I help improve the quality of life for humankind and help reduce unnecessary human suffering. And this professional purpose is why I chose to run Software Center, why I’m on the board of several companies and why I invest in startups. To the best of my understanding, at this moment, this is the best use of my time and energy.
I realize that some or even many may completely disagree, but it was never the intention to convince you of my purpose. The goal is to convince me of my purpose and it works for me to guide my actions and how I allocate my time. And, believe me, coming to this point and being able to clarify my purpose to myself took me many, many years.
My challenge to you is to clarify the purpose you choose for your life. What actions are you taking to that end? And if you even have the slightest spiritual side to you, what’s the reason that you’re here, in this life, and are you living up to that purpose? In a world where the opportunities are seemingly infinite, you need direction to ensure that you’re using your time and energy in the best way possible so that when you come to the end of your life, you can be satisfied with how you’ve used that amazing gift of life. As Robert Byrne famously said: the purpose of life is to live a life of purpose.
Digitalization often focuses on new products, solutions and service opportunities through the use of software, data and artificial intelligence. Although this is an entirely relevant and valid viewpoint, many tend to forget that it’s not just the offerings that change. The people that work in a digital world also need to change. Our priorities, norms, values, practices and routines need to change as we transition from the traditional to a digital world.
During my engagements with large numbers of people working at a very diverse set of companies, I frequently run into mindsets and viewpoints that clearly are set in the old world, rather than a digital one. When you’re not reflecting on the philosophy with which you approach your professional life, you may easily end up becoming irrelevant and outdated. This limits anyone’s professional opportunities.
This year, I’ve been thinking quite a bit about the differences between the traditional and the digital world from a personal and professional perspective. What does digitalization mean for the typical professional out in industry? How should we adjust the way we work and operate? One way I thought might be helpful to address this is by stating a number of rules on how to act, based on my experience. Together, these capture the essence of the requirements for thriving in a digital world.
In a fast-changing world, some things move more slowly. Being clear on what you believe your purpose is, on what it is that gives meaning to your life, helps you set direction and guides your actions. Although your life purpose may evolve, it will do so much more slowly than everything else and provide you with an anchoring point.
Once you’ve started to formulate your purpose, the next step is to look towards operationalizing it by selecting tactics. These tactics, however, are hypotheses on how you believe you can realize your purpose best. It’s critical to focus on the outcomes that you’re looking to achieve, rather than the tactics that you currently use, to ensure that you’re indeed delivering on the intended purpose. These outcomes are preferably expressed in quantitative, measurable terms.
In a digital world, collecting data by instrumenting devices and processes has never been easier. So, rather than relying on your fallible perceptions and memories, focus on measuring what matters and tracking quantitatively that you’re indeed progressing towards your desired outcomes.
A digital world is a programmable world. Any repetitive task that you perform regularly or periodically should be automated. The goal is to free up your time for the novel and unique work worthy of your attention and unique skill set.
As the speed of change all around us is constantly increasing, you need to lean into the future, rather than try to hold on to the past. This requires a curious, experimental mindset where you open up and allocate time to exploring new ideas, technologies and products to understand why and how these might be an improvement over the contemporary. It also requires the courage to let go of old ideas, old ways of doing things, old relationships, to create space for the new. Change is continuous, dynamic and unpredictable and it wants you to embrace it.
In a digital world, hierarchies are increasingly fluid, constantly changing and focused on current needs. Relying on positional power to accomplish your goals is therefore risky as you may lose that position at any point. Instead, focus on building skills, becoming an expert in some and proficient in others to create as many opportunities as possible and, consequently, deliver on your purpose.
In a stable world, you can afford to focus deeply on understanding one aspect or component as its context won’t change very much. A digital world is dynamic and highly interconnected and as everything is changing at the same time, focusing on one aspect or component tends to lead to the wrong conclusions. Instead, you need to think more holistically and increase the scope of your attention. Even if your interest is the specific aspect or component, you need to understand its context as this context will change continuously.
Never wait for others to tell you what to do. Always be proactive, initiating action based on your best understanding of the right course of action, considering purpose and outcomes. And when you really don’t know how to take things forward, be proactive in asking for advice from those around you.
Rather than looking to control the people around you, agree on the outcomes that you’re both looking to accomplish and then empower those around you to select the strategies and tactics they feel are best. Then evaluate them based on outcomes. When purpose and outcomes don’t align, despite your best efforts, then let go of the person. It frees both of you up to pursue your respective purposes and reduces suffering all around.
Traditional business ecosystems were very static, meaning that a partner stayed a partner, a competitor stayed in that role and your customer is the same as yesterday. In a digital world, business ecosystems are in continuous flux. Your supplier becomes your competitor. You become your customer’s competitor. You partner with companies that you never heard of a month ago. By continuously engaging with your ecosystem, you can detect the changes early on and you can identify when the time is right for you to reposition in your ecosystem.
Operating as a professional in a digital world requires a new mindset, a different set of priorities and, in some ways, a new toolbox. These ten rules aren’t concerned with new skills or capabilities, but rather with more fundamental behavioral patterns. Digitalization is of course concerned with new products, solutions and services, but it’s also defining a new paradigm. And unless you adopt this paradigm, you’ll struggle to thrive in a digital world.
Gandhi said: “Your beliefs become your thoughts, your thoughts become your words, your words become your actions, your actions become your habits, your habits become your values, your values become your destiny.” Everything starts with your beliefs and the digital world requires a new system of beliefs, a different paradigm. So, my question to you: do you have what it takes to be successful and thrive in a digital world?
Last week, my spaceship got destroyed, I read about NFTs and I pondered about bitcoin. Allow me to explain how this is all related. As I mentioned in an earlier post, occasionally I play Eve Online, an open world set in space. The most valuable areas in the game in terms of resources and loot are also the places where there are no rules. In this dog-eat-dog world, any player can attack you, kill you and take all your valuables. This is exactly what happened to me – which made me really sad and upset. Until I remembered that we’re talking about a few flipped bits in a computer in, I think, Iceland.
This weekend, I read about NFTs or non-fungible tokens. According to Wikipedia, these are cryptographic tokens that represent a unique digital asset, such as art, digital collectibles and online gaming assets. Who cares, you might ask, but when NFT-based art pieces are starting to get sold by Christie’s to the turn of 3.5 million dollars and NFT sales last year totaled about 250 million (up 300 percent from the year before), it’s obvious that there’s a group of people that considers these NFTs valuable.
Last week, I also read that Tesla has added 1.5 billion dollars in bitcoin to its balance sheet. A decentralized digital cryptocurrency. A bunch of numbers on a computer. Difficult to calculate numbers, admittedly, as it’s cryptographically ensured and hence bitcoin mining, but still. A car company buying some numbers to the tune of 1.5 billion? What the heck’s going on here?
None of these assets are real as in we’re unable to physically touch or hold them. And during my run this morning, I realized that this is nothing exceptional. Many of the things we use in our day-to-day existence aren’t real. The prime example is money, of course. This asset plays an enormously important role in our lives and industry, but of course, it isn’t real. Even if you hold pieces of paper or coins, their inherent value doesn’t represent the actual value. Money works because we’ve created a common, inter-subjective illusion of the value of money that makes society work.
As a society, we’re constantly creating new forms of value. With digitalization, these are becoming increasingly virtual. This is great from an environmental perspective as increases in living standards are becoming less correlated with an increased use of physical resources.
The statement that more and more value is created in digital form may sound obvious, but many of us only realize the new form of value way too late to do something useful with it. I remember that an uncle of mine, who ran a very successful plumbing company, showed us his new mobile phone in the early 1990s. My father could, for the life of him, not understand why anyone would want such a device. This was when he drove 60,000 kilometers per year and he could have used all that time in the car to get half his job done with a mobile phone.
Human needs haven’t really changed over the millennia. We all want to feel safe, compete in hierarchies of our choice and learn and develop. However, the form this takes changes continuously. We have mobility needs evolving from sandals to shoes to horses to cars to trains to airplanes. We want to communicate with loved ones and this has evolved from speech to letters to phones to email to video conferencing and Facebook. This relates to the late Clayton Christensen’s notion of the job a product is designed to do. It’s never about the product, eg a truck, but always about the job that you’re looking to get done, eg transporting goods. And when a better way of doing that job is created, we move on.
One of my concerns is that I, and many with me, don’t realize the importance and relevance of new ways to create value, especially through digital assets. Interestingly, when software was first introduced and started to scale in the 1970s, there was a large movement among the programmers of that age that software should be freely shared with everyone as one couldn’t or even shouldn’t have to charge for non-real assets. The open-source community still maintains part of that culture.
Part of the problem of recognizing the value of new digital assets is that early in the creation of a new asset, there often is a religious fervor around it. Try to criticize a cryptocurrency like bitcoin to those holding these in their portfolio and see what their reaction is. There’s an incredibly strong belief among the supporters that bitcoin isn’t just an incredibly valuable asset that will only increase in value. It also is a way to break the control of governments and banks over oppressed citizens. For those of us that don’t feel that oppressed and aren’t calling for the revolution, it’s easy to get put off by the political aspects of a new digital asset and miss the actual value it provides.
The second challenge is that both customers and companies often feel that the new digital asset is viewed as fishy and unethical. For instance, still in the embedded-systems industry, the collection of data around the use of products and the monetization of this data is viewed as wrong and hence the collection and use of data is deprioritized until it’s obvious that everyone in the ecosystem accepts this as the normal way of operating. The problem is that by that time, all the business opportunities resulting from the data have already been captured by others and you’re playing a catch-up game to try and limit the damage to your business.
The third challenge is that by making the digital asset valuable, other, more traditional technologies and assets are made less valuable. This typically affects existing power structures and hierarchies in existing companies. If mechanics and electronics are commoditizing and software, data and AI are increasingly differentiating, we need to prioritize resources differently, outsource technologies that used to be differentiating, reorganize around the differentiating technologies, and so on.
Digitalization brings with it new digital assets that are valuable in ways that for us skeptics and traditionalists are hard to understand and appreciate. Categorically rejecting these new forms of value creation, however, puts you at a significant disadvantage because, by the time you realize their value, all business opportunities have been capitalized on already. Instead, we need to develop hypotheses on how and why new assets might be valuable, validate the underlying assumptions and experiment with these assets to better understand the community driving this forward. In a digital world, nobody can afford to be an analog dinosaur.
Recently, I participated in a discussion set in the automotive realm. At some point, the conversation turned to platforms. After a while, I realized that there were several definitions of platform getting mixed up. Having worked with platforms since the 1990s, it has been really interesting for me to see how the very notion of platforms has evolved. Here, I’ll discuss three types: platforms for reuse, for DevOps and for ecosystems.
Initially, the primary role of platforms was to share commodity functionality between different products in a product line or portfolio, ie a platform for reuse. The train of thought was that if we could avoid each product team building the same functionality over and over again, it would allow for higher R&D efficiency as the product teams could work on the product-specific, differentiating functionality whereas the platform team would serve all product teams.
Having done work on software product lines for the better part of 25 years, it’s clear to me that this simple argumentation can work, but that there are many ways to mess up the benefits platforms can provide. Especially the coordination cost between platform and product teams and the difference in priorities between them can cause so many inefficiencies that the benefits of reusing functionality can easily be nullified.
My main lesson about platforms for reuse is that the focus shouldn’t be on (perceived) efficiency but on speed. Product teams should focus on maximizing speed and where a platform can help to move faster, then, by all means, use a platform, but where the platform slows product teams down, it should be dropped like a hot potato. Making the use of the platform optional for product teams drives the right priorities for the platform team as well.
During the last years, the meaning and use of platforms shifted because of the increasing adoption of continuous deployment of software to products in the field. When product software was released maybe once or twice a year, it was entirely feasible to spend a significant amount of manual effort on customizing the latest version of the platform software for a specific product and integrating it with the latest version of the product-specific software. When the release frequency goes up, however, the amount of manual effort required becomes unfeasible. This leads to platforms for DevOps. Here, the platform is the superset of the functionality in all products and the software for each product is automatically derived through configuration of the platform software.
When it comes to platforms for DevOps, the main challenge I’ve seen companies struggle with is the transition from customization to configuration of software. Especially in the automotive industry, OEMs tend to demand all kinds of customizations that, truth be told, often add little business value. If it really isn’t possible to avoid customization in all contexts, the ambition should be to define an interface between the platform and the customization software that strictly separates them. This allows for the independent evolution of the platform software in products deployed in the field.
The third type, which many aspire to have but few have realized, is an ecosystem platform that customers, partners and third parties can extend through a set of APIs. For their own purposes, to serve verticals not served by the platform provider or to provide solutions not covered in the platform for a broad audience. Every company I interact with aspires to provide the iPhone of their industry, but operationalizing this ambition is a hard, up-hill battle in most cases.
The typical tension in companies aspiring to provide an ecosystem platform is between that nascent platform and the existing product business. A software ecosystem needs scale, meaning that the same ‘apps’ are deployable in as broad an installed base as possible. This requires that each product in the portfolio provides the standard ecosystem APIs, which limits the autonomy of product teams. In addition, the prevalent product mindset focuses on including as much useful functionality in the product as possible whereas a platform mindset requires us to yield certain domains of functionality to the ecosystem to make sure that partners and third parties have a sufficiently appealing business case.
Platforms are great but discussions easily get confusing when different platform definitions are used without the participants being aware. I’ve tried to provide some structure here. For most companies adopting DevOps, the product-centric way of working is no longer feasible and a platform-centric approach is required instead. This has great advantages but calls for a careful strategy and way of operating as there are several pitfalls in the road ahead.
Recently, in one of the company boards that I’m part of, we approved the new product strategy. This strategy is quite a deviation from the previous one in that we balance much more between investing in the current, main product, which used to get virtually all investment earlier, and new product initiatives to serve adjacent markets and help grow the business. However, after the board meeting, we discussed operationalizing the strategy and the main topic was that the most difficult part would be to change the culture of the employees in the business.
As famously said by Peter Drucker, culture eats strategy for breakfast. Once you get a certain culture in place, the general experience is that it’s extremely difficult to change. You can present all the strategy slide decks you want, but it’s surprisingly difficult to actually make people change their ways of working. Although this perspective is quite accurate, it easily puts companies in a bit of a victim role: the culture is what we have and we have to live with it.
Diving a little deeper, I believe that this is a fallacy that we have to avoid falling into. Culture can be defined as the norms and values that we organize ourselves by. Norms are defined as the established standards or expectations and values are the set of beliefs and ideas that we uphold as important within the company.
For example, virtually every company I work with views itself as customer centric. However, the ways this materializes can vary greatly. In the company that triggered this post, the implementation of customer-centricity was to try to respond to every suggestion, request and demand from every customer. The result was a backlog of an infinite amount of minor features that may please some customers but that didn’t move the needle from a competitiveness perspective. The way we responded to this was by setting a strategy where we explicitly allocate resources using the three horizons model and within each horizon with an explicit allocation to roadmap work versus customer requested work. We still allocate resources to respond to customer requests, but significantly less than before.
Now, making this strategy real basically requires us to externalize the norms and values by which we’ve been operating. Once we’ve done this, we have a basis for discussion to realize the changes we’re looking for. Often, it’s not so much that a completely new set of norms and values needs to be defined, but rather that the balance between conflicting norms and values needs to be changed. For example, in some companies, an urgent request from a customer is always prioritized over roadmap work. When it’s time for a retrospective and it’s clear that few of the features on the roadmap were realized, everyone sagely accepts this as there were too many requests from customers that needed to be prioritized.
Read the last sentence again: “needed to be prioritized” is a direct expression of the company culture and something that you as a leader need to be highly sensitive to. Not reacting to such statements makes you immediately complicit in perpetuating the old culture and sabotaging the defined strategy.
To realize a new strategy and upgrade your company culture, I believe that at least three actions are key. First, make the current norms and values explicit. Not in general terms, but as specific as possible. Then indicate where the new strategy requires us to make changes in the way we prioritize our time and energy. Exemplify it and make sure that everyone rationally understands what the new strategy means in terms of cultural changes.
Second, as a leader, you need to embody the new strategy. When I lived in the US, many parents told their children: do as I say, not as I do. However, it’s perfectly obvious for any parent that children mimic the behavior of their parents, not their words. The same is the case for employees in a company: people will mimic the behavior of leaders, not their words. So, as Gandhi said, be the change you want to see.
Third, operationalize the strategy for every individual in terms of what it means in terms of day-to-day work, priorities and interaction and follow up on the new ways of working. Humans are habit-driven creatures and it’s too easy to fall back into old ways unless we’re constantly reminded of what’s expected from us. This also requires an escalation path as conflicts will surface between the old and new cultural patterns. If individuals are required to sort out these conflicts themselves, most will fall back into the old, safe ways to avoid any perceived risks. Allowing for escalation will make it safer to take risks as the responsibility is abdicated.
While it’s true that culture eats strategy for breakfast, it doesn’t mean that you can’t realize changes in the company culture that facilitate the strategy you’re looking to operationalize. It requires us to externalize the norms and values in the old culture to make the required changes in priorities explicit, for leaders to embody the new strategy and behave by it and for each individual to operationalize the strategy in concrete, day-to-day terms. This is incredibly hard and time consuming, but the alternative is to not change at all. As Mark Zuckerburg famously said: “The biggest risk is not taking any risk… In a world that is changing really quickly, the only strategy that is guaranteed to fail is not taking risks.”
Recently, the Netherlands experienced some inclement weather including snowfall and strong winds. Interestingly, the entire country shut down with trains not running, highways closed off and recommendations of staying at home. It led to a bit of hilarity in our Swedish household as we experience this kind of weather regularly during the winter months and we just go about our business without blinking an eye. However, Northern Europe had a few very mild winters recently and people started to expect those to become the new norm.
The pattern of assuming that the most recent data points can be used to accurately predict the future is a deeply human trait that we can see everywhere. A dry summer causes people to complain about global warming changing the climate. A successful quarter or two causes companies to assume that the next quarter will be as successful. Typically, we expect today to be like yesterday and tomorrow to be like today. The main conclusion has to be that human intuition simply sucks at applying statistical principles. It doesn’t mean that changes aren’t happening, but it does mean that we typically need a lot more data points before we can conclude anything definite beyond chance.
The primary defense against this is summarized in the proverb “hope for the best; prepare for the worst.” On the personal side, the Stoics practice a habit that I think can be incredibly helpful. Promoted by Seneca, the idea is that you daily imagine the worst thing that could happen to you and try to experience and live through it in your head. This helps you build up the mental fortitude for the situation where something bad does happen so that you don’t collapse under the weight of what life throws at you. And, of course, it also helps you feel grateful when bad things do not happen. A second habit of the Stoics is to, once per month, wear the most basic of clothes, eat the simplest food and sleep in the most rudimentary bed to experience what life would be like if you were to be down on your luck and lose everything. And to do all this, asking yourself that if this was the worst that could happen, is it really that bad?
In business, the key challenge very often is the focus on efficiency. It’s inefficient to put resources aside for cases outside a very narrow band for which companies optimize as these resources aren’t utilized sufficiently. So, companies often fail to proactively prepare for bad situations and need to scramble to respond whenever the market or society suddenly shifts. The ongoing pandemic is a great example of how many were caught off guard.
However, the main victim of the focus on efficiency typically is innovation. Innovation is by its very nature inefficient. As a team, you develop hypotheses of what might help develop and grow the business, but the fact is that you’re most likely wrong and will only find out after having spent resources on testing and validating the hypothesis. The very nature of innovation is such that the majority of innovative ideas don’t pan out and it’s the few that do that have to pay for all the failed ones. Of course, principles such as those defined in the lean startup or our research aim to minimize the resources invested in each idea by creating multiple proof points that need to be met to invest more. But the fact remains that innovation is inefficient by the metrics of modern companies.
The main concern of assuming that tomorrow will be like yesterday and today is that you’re woefully unprepared for “black swan” events and become extremely fragile as a business. Some companies invest significant effort in scenario planning where multiple more likely and less likely scenarios of how the future might unfold are evaluated and responses are developed for each of these scenarios. The scenarios themselves will never occur as predicted, but they’ll inform and accelerate the response to what will happen and help in the case of unexpected events.
Human intuition is extremely poor at statistical analysis and companies tend to suffer from “recency bias.” This makes them fragile and brittle, causing them to break and fail in the face of unexpected situations. As the future is fundamentally unpredictable, we need to protect ourselves from ourselves by balancing efficiency and preparedness, conducting scenario planning to have clear playbooks to use and investing in innovation. As Alan Kay famously said, the best way to predict the future is to create it. No matter how inefficient and resource consuming it may be. Remember: prepare for the worst while hoping for the best!
It seems that much of my work these days is concerned with bringing AI to the embedded-systems domain, understanding what the implications are and how companies should deal with it. In the discussions with technical experts and business leaders, however, I constantly run into several misconceptions.
First, there are still several people out there who think about ML/DL models as single-point solutions for a specific problem where we can build the model, integrate it, deploy the resulting software and be done. Although one could operate in this way, it’s basically ignoring the vast majority of benefits that you could deliver. Instead, it’s about continuous everything. It’s about a constant flow of software updates to the field, a constant flow of data coming back from your systems, constant retraining and updating of models. We all read about DevOps, DataOps and AIOps, but that really is what it’s all about. The shorter you can make the cycles, the more value you can deliver.
Second, many view AI as a technical challenge to be solved by R&D but unrelated to the rest of the company. The fact is that it will change your business model. If you’re currently using a transactional business model where you get a one-time payment when you sell the product but need to provide software updates throughout the economic life of the product, you’re in trouble as your cost is continuous while your revenue is not. The only way to get around this is to align your revenue model with your cost model. For most companies that I work with, it means combining the upfront product sales with a continuous service revenue model. This is actually a great way to generate more revenue from your customers.
Third, especially in companies that build large, complex and expensive systems, such as in automotive, telecommunications or automation, there’s no clear definition or understanding of the actual value provided to customers. The whole package sells and consequently, the whole package is valuable. When moving towards continuous improvement of systems, you need to choose where to focus your energy and time. And this requires you to understand what’s actually valuable in your offering as improving commodity functionality using ML/DL models or otherwise doesn’t help your customer. We’ve worked quite a bit with companies on modeling the value of their solutions or products to customers and it’s surprisingly hard to be precise and concrete.
Fourth, you can’t use AI in isolation. Machine and deep-learning solutions require data for training and operations. The data is generated by software that instruments the system. And of course, ML/DL models are themselves software. The only difference is that AI software is programmed by data and pattern recognition, rather than by humans in an algorithmic fashion. This means that you need to be good at all digital technologies.
The final misconception is that many believe that once they have trained an ML/DL model and it performs well in prototyping, the hard part is done. This couldn’t be further from the truth. The easiest part is actually creating a model and the hard part is industrializing it. Industrializing means setting up the data pipelines, putting monitoring and logging in place, ensuring correct (or at least acceptable) behavior in all cases, developing solutions for DevOps, DataOps and AIOps, and so on. In an earlier post, I wrote about our AI engineering research agenda where we capture the major challenges to be addressed.
AI is moving to the edge – and it should because it has the potential of delivering enormous value. The challenge is that there still are quite a few misconceptions out there about what this actually means in practice. I’ve discussed five of these and provided my viewpoint. AI belongs on the edge, but there’s a lot of work around it that needs to be put in place at the same time. And as it won’t happen by itself, it’s critical that we get going on this yesterday.
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