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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on the philosophy with which you approach your professional life, you may easily end up becoming irrelevant and outdated.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.

1. Clarify your purpose

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.

2. Focus on outcomes

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.

3. Instrument and use the data

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.

4. Automate repetitive tasks

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.

5. Lean into the future

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.

6. Build skills, not position

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.

7. Think holistic

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.

8. Be proactive

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.

9. Empower 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.

10. Engage with your ecosystem

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.

 

Occasionally, I enjoy playing computer games and during the Christmas break, I spent some time in Eve Online. It’s an open-world game, set in space, where you’re free to do any activity that you like. As I was spending time in this game, I realized something interesting: the return in terms of in-game currency that I was earning with certain activities was orders of magnitude different from other activities I could be spending time on. Not 10 percent but 10x, 100x or even 1000x!

When I look at my life outside of computer games, I notice something similar. Whether it’s publications (I am a professor after all), financial returns (I invest in startups as well as traditional passive index funds) or health returns (I exercise daily), how I decide to spend my time has a significant impact on the return I generate. For instance, standing on a cross-trainer at a moderate setting for 40 minutes has, according to some research that I read, the same cardiovascular effect as three times one minute of full-out high-intensity interval training (HIIT). Three minutes has the same effect as 40 minutes – that’s more than 10x!

In the companies I work with, in various capacities, I see a similar pattern: most of the people I meet are hardworking and well intentioned, but for many, the return on their activities is quite limited. Especially for us, who are toiling to drive change in companies, many initiatives run out into the sand without any real impact as the resistance to change proves too powerful. It easily starts to feel like “shuffling chairs on the deck of the Titanic” – you’re close to the lifeboats, but you’re doing little to avoid disaster.

Many moons ago, when I was working for Nokia, one of the senior leaders in the company mentioned a quote that I’ve taken to heart and try to operationalize as often as I can: activity isn’t the same as progress. Being raised traditional Calvinist protestant, the importance of working hard was ingrained deeply in my upbringing. My dad often told me: “There are workhorses and there are show horses. You’re a showy workhorse.” And I must admit that I’ve never met a successful person who didn’t work his or her behind off.

Hard work is necessary but not sufficient. It’s also about working smart. How do we make sure that the things we spend our time on indeed result in the outcomes we’re looking for? The Pareto principle states that 80 percent of the outcomes result from 20 percent of the causes. The effect of this principle can be found in wealth and income distribution, taxation, quality management, sports and even software development, as the hardest 20 percent of the code takes 80 percent of the time.

The Pareto principle also applies to how we spend our time as professionals. Being creatures of habit, it’s easy to step on the treadmill of our daily, weekly, monthly, quarterly tasks and just execute as this is how we’ve always done it. However, as everything we and our companies do commoditizes over time, we should always look for ways to automate or remove activities that add little value in order to free up time for new activities and initiatives that have the potential of an order of magnitude higher return of investment (RoI). Innovation doesn’t only apply to products and services but also to how we organize our workday.

My challenge to you is to evaluate the effect or impact of the activities you spend your time on. Identify the 80 percent that results in little impact, find ways to automate or get rid of those and use the freed-up time to do more of the 20 percent activities that have the high RoI. In the end, we all want to make a difference and just working hard isn’t the optimal way to get there. Remember: activity isn’t the same as progress.

 

 

Last week, I gave a presentation to a company that’s in the midst of a strategy revamp. I always admire leadership teams that take on the incredibly difficult challenge of taking a step back and reviewing how the organization is operating right now and what change in direction is required to achieve future success. As the saying goes, what got us here in many cases won’t get us there.

One of the key discussions was concerned with the R&D resources allocated to providing customizations to some of the most important customers. Large customers often know how important they are to the company and, as a consequence, typically negotiate very hard for special treatment in terms of customization, free services and similar. As these companies provide a significant part of the revenue, the short-term and immediate response often is to go along with their requests.

When a company becomes increasingly dependent on a small set of powerful customers and these customers exploit their power, the consequence may easily be that all R&D resources are consumed by providing customizations and customer requests. The result will be that the company’s offering becomes increasingly commoditized as the R&D resources aren’t used for building differentiating functionality.

Of course, there’s nothing wrong with listening to your customers and understanding their needs and wishes. However, there’s a major difference between “customer-first” and “customer-unique” features. Customer-first features are requested by customers that are in many ways leading and happen to identify the need for new functionality before others. Such a feature will be relevant for more customers besides the one initially requesting it and hence valuable. A customer-unique feature is functionality that’s only relevant for the requesting customer. These features are often a drain on the company as their return is quite low.

At the company where I gave the presentation, the discussion led to an interesting question: are these important but demanding customers really the right customers for us? In this context, I brought up the difference between “making customers happy” and “making happy customers.” The company had worked incredibly hard at making customers happy, but were they really the customers they wanted to serve? Although the discussion didn’t lead to a clear conclusion, it was clear that it was far from obvious that this was the case.

As part of the strategy work that every company needs to engage in, it’s of critical importance to identify the profile of the customer you want to serve – for at least three reasons. The first is that a company trying to be everything to everyone is bound to fail. As you can’t stand out and differentiate yourself without focus, the only strategic path left is to be the lowest-cost player, which is a hard game.

Second, strategic investments made by any organization should be prioritized such that the skills and capabilities the company has used to differentiate itself from the competition are developed further and those that the company needs going forward receive sufficient attention. Failing to be strategic in these investments typically leads to a politicized process and a scattered set of results.

Third, as any company needs customers and it’s the sales function that’s delivering these, it’s important to ensure that those active in sales are aware of the profile of the customer that the company seeks to serve. Failing to provide sales with a clear customer profile easily leads to the aforementioned problem where sales has promised customers specific customizations and extensions as part of the process of “closing the deal.”

So, as part of your strategy process, make sure to create a clear profile of the type of customer you’re looking to serve. And that profile may require you to “fire” customers that no longer fit that profile, or at least put them in maintenance mode and not invest in any further customization work for them. In the end, the goal isn’t to make all customers happy but to make the customers that you want to serve happy.

 

 

 

During the Christmas break, I read several books including “Sense & respond” by Jeff Gothelf and Josh Seiden. The authors frame the difference between traditional and digital companies in terms of the absence or presence, respectively, of a continuous dialog with the market and the customers. Of course, traditional companies interact with the market and talk to customers, but once a product specification has been defined, the product developed and the product is ready for sales, the discussion is mostly concerned with getting customers to actually buy the darn thing.

If the sales are disappointing or if there are many requests for changes, there often is an upgrade or next version of the product. However, the frequency of updates tends to be low and is often measured in years. In automotive, a car model usually lives for 5-7 years and has a mid-life upgrade after 3 years. Even traditional software products experience a yearly release cycle at most.

Digital companies, on the other hand, are in a constant dialog or conversation with the market. Through a variety of different metrics and mechanisms, they continuously learn from the feedback from customers, make changes and measure their effectiveness. These mechanisms include instrumenting the product to understand how the product is used, the ability to provide different feature versions and ways to assign users to different groups. And, of course, digital companies assume continuous deployment (DevOps) to be in place as the feedback cycle should be as short as possible and preferably be measured in days and weeks.

Another main difference between traditional and digital companies is that the former tend to focus on what customers say while the latter focus on what customers actually do. This is a very important distinction as, although we all view ourselves as rational people, it turns out that in almost all cases, there’s a significant gap between what people say and what they do. Deciding based on what people say can therefore really throw you off the optimal path as the things you build or add to your product may not deliver any actual value.

Going from focusing on what people say to what people do can be a difficult transition, especially in B2B contexts. Customers may walk as they feel you’re not listening to them. Also, it’s really hard to tell them that they may believe something about their behavior that’s actually incorrect. However, the alternative is to build functionality that won’t add any business value in the long term.

Especially for embedded-systems companies, there often is a belief that these principles only apply to software, but not to electronics and mechanics. The interesting development is that more and more companies are now starting to explore mechanisms to deploy new electronics and, perhaps, even mechanical components to products in the field. Of course, the release frequency is lower for anything that includes atoms, but it’s entirely possible to also have a dialog with the market for these types of components. It’s mostly a matter of finding the right business model to support it.

Many in R&D don’t realize the economics of R&D budgets. For a company that allocates 5 percent of its revenue to research and development, it means that every euro invested has to generate 20 euros of business value. It’s not enough that the R&D costs are covered. The best way to ensure that R&D activities deliver the 20x return is to take small steps, measure continuously whether you deliver on the expectations and change immediately if the response from the market is different than expected.

Digital companies are in a continuous dialog with the market, have short feedback cycles and focus on customer behavior (rather than on what customers say). As digitalization continues to be the theme for 2021 as well, evaluate your business and technology strategy to ensure that you’re incorporating these principles and transitioning towards these. The only constant is change and not changing has a very predictable outcome: extinction. And who wants that?

 

 

Many years ago, together with a few colleagues, I wrote a paper on the BAPO model. The BAPO model says that business and business strategy should drive architecture and technology choices (A). These should, in turn, drive process, ways of working and tooling choices (P). Finally, these should be used to define an organization that makes sense to realize the business and technology strategy (O). Although this model is intuitively very appealing and in many ways makes a lot of sense, the challenge is that it misses an important point: technology is becoming increasingly central in business and the way that companies traditionally work with business strategy is becoming increasingly obsolete.

No matter what business you’re in, you’re a digital technology company. The success of the clothing store Zara is often attributed to its ability to detect trends in fashion and respond to these trends in weeks. The only way it can do this is by using digital technologies. Similarly, banks are huge IT houses and the budget for IT often is on par with, if not larger than, the other functions in a bank. The implication is that developing a business strategy without incorporating technology is impossible and makes no sense. Instead, the technology and business strategy are so tightly interwoven that these need to become one and the same.

One reason for this is that the nature of business is changing from a one-directional, transactional model where the company stresses the features of the product and tries to convince customers to buy it, to a bi-directional, continuous model where there’s a continuous dialog between the company and its customers. This dialog can take more of a qualitative approach, such as surveys and questionnaires. With the increasing digitalization of the industry, however, it often tends to be more quantitative and driven by mechanisms to measure customer behavior, such as A/B testing.

A second reason is that digital technologies allow for the automation of processes to an extent that was infeasible even a few years ago. Ranging from robotic process automation (RPA) to full AI solutions, processes requiring significant human effort can these days be fully automated, reducing cost and error rates and significantly speeding things up. And with the increasing prevalence of no-code and low-code solutions, processes can be automated by teams mostly consisting of domain experts with minimal support from engineers. All this is of course not constrained to the boundaries of the organization but can, and in fact should extend to your ecosystem of partners, suppliers and customers.

Third, the pace of technology development is now so high that the traditional infrequent, time-consuming strategy process needs to become continuous as well. We don’t do strategy once per year, but continuously. New technologies enable new business models, new ways of serving customers, and so on, and if you don’t exploit those immediately, others will do it for you. Similarly, new business models require new technologies, which in turn often require new partners to interact with.

Business strategy and technology strategy are becoming one and the same. This requires us to suspend the traditional division of the business side and the R&D side of the house, and build a continuous conversation with the market, continuously engage in the strategy process and constantly experiment and test to inform the strategy. Remember, no matter what business you’re in, you’re a digital technology company. So, you better start behaving like one.