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Florian Ramseger, economist, data scientist, and futurist

Source: Florian Ramseger

Florian is an economist turned data scientist with wide-ranging work experience in different countries and in different fields — the private sector, the public sector, and NGOs and international organizations. In his current role as senior product specialist he helps Salesforce customers from different industries implement their data projects. Prior to Salesforce, he was with the International Red Cross, the World Economic Forum, and the WU Vienna business school. Florian has a BSc in economics and geography from University College London and an MSc in economics and economic history from the London School of Economics. Currently based in Frankfurt, Germany, he has spoken and written about how the digital transformation affects organizations and societies, based on his experience with implementing IT projects of various shapes and sizes and drawing on his background as a trained economist.

Alexander: We have talked about the digital transformation of society on several occasions; how do you think about digitalization and how does it change our lives?

Florian: The digital transformation over the last few decades can be broken down into three stages, and each stage comes with its own benefits. When a process has become “digitalized” we notice it, either because the input form is now digital or because the end product is presented in a digital format. Capturing and presenting information in digital form started some 40 years ago with the widespread adoption of the personal computer.

The decreasing costs of all sorts of sensors and the rise of the Internet of Things, as well as advances in machine vision, voice recognition, and natural language processing, mean that we digitize increasingly more analog content. The value associated with this first stage of the digital transformation, sometimes referred to as digitization, was traditionally seen in the savings in physical or human resources, but nowadays digital content is also the building block for the other transformations that we are seeing.

With the advent of network computing, the Internet, and later smartphones, we moved to the second stage of the digital transformation, which allows more people to access the digitized information. The value here lies in how information flows faster from A to B. This can eliminate small inconveniences in life, such as having to call for a taxi, by using a service like Uber instead, or it can help with providing lifesaving real-time information, as in the case of natural catastrophe warning apps. It also means that information can be fed back in real time, as when Google Maps uses the travel speed of smartphones to estimate traffic flow on streets. 5G and the proliferation of blockchain technology will further accelerate this stage.

Another reason that digital information can make our lives better, and this is the third stage, is that we can generate additional insights from the stored and shared information. This has been around for a while in the form of business intelligence (BI) — basically ever since we had databases. But several factors, including the rise of cloud computing and the general increase in computing power, have led to the impressive developments in the field of artificial intelligence (AI) that we have seen over recent years. With quantum computing, which is on the horizon to become widely available, this stage will explode.

While these different technologies emerged more or less sequentially, which is why I call them stages (see Figure 5.1), we are now in a situation where technological advances are happening at each level. Further, as already mentioned, innovation in AI and in different network technologies leads to ever more content becoming digitized, which in turn can be accessed and mined for new information, meaning that we see self-reinforcing loops between the different building blocks. This explains the rapid acceleration of the digital transformation that we are currently witnessing.

Alexander: What role does Big Data play in this transformation, or is that just a buzzword?

Florian: Big Data is a real paradigm shift. Whereas in the past we had to rely on small samples to make inferences about the wider population, in the statistical sense of the word, in many cases we now have data points about every single unit in the population. This could be individual shoppers who frequent an online store. Or it could be the fuel consumption of the vehicles in the fleet of a delivery company. This brings with it two advantages.

Figure 5.1 The three stages of the digital transformation

First, we have more data to play with when we want to make predictions, so data scientists can throw in everything but the kitchen sink to find interesting patterns. The sky is the limit for what businesses can do with big data. I have a device under my mattress that can provide me information about my sleep quality as well as my risk for sleep apnea. Although it costs less than EUR 100, it can approximate the results one would get in a sleep lab using an algorithm that was probably trained on a large set of medical data.

This device also illustrates the second advantage to Big Data, namely, that individual units can be identified, and tailor-made action can be taken. We are all familiar with applications of this in our daily lives, such as when we get personalized movie recommendations from Netflix. But there is still huge potential for society on many other fronts. In the future, just as my sleep analyzer does, many of our devices will notify us when our personal data deviates from the norm. Researchers are already working on smart toilets that can automatically analyze your urine!

Alexander: Do you see every country and industry moving through these stages as you described them?

Florian: A lot of what I described is not that new: machine learning, Big Data, Internet-connected devices, and so on have been around for a number of years already. However, there are huge differences in the adoption of these technologies across countries and industries, but also within them.

I went to a regional newspaper in Germany to give a software training. They had neither laptops nor guest WiFi. Instead, we had to use a training room in the basement that reminded me of the computer lab in my high school 30 years earlier. On the other hand, when I visited the data journalism team of the newspaper Die Zeit, I felt like I was on the Google campus. They had top-notch equipment, and they were hosting data science meetups in a room complete with beanbags and a beer-filled fridge. They are producing cutting-edge data journalism.

No matter what the starting point is, we are now seeing a wider adoption of digital technologies, because these technologies are becoming commoditized, meaning they become more accessible and easier to implement.

Especially here in Europe, concerns about data privacy have held us back in many regards. Perhaps we have good reasons to be skeptical; we Germans are all too aware how authoritarian regimes can use information about their citizens for total surveillance. Nonetheless, public acceptance of technology that relies on private data is slowly increasing. I am actually amazed that Germany put out a coronavirus warning app that lets you know when you were in the vicinity of someone who later tested positive for COVID-19.

Alexander: So where do you see this going in the future? In what fields do you see the biggest changes?

Florian: All sectors and industries will be affected, because most business processes involve moving information around.

The most obvious ones are sectors such as banking that already have gone through the first stage of the transformation — bank account records are already nothing but 0s and 1s. With the advent of so-called robo-advisor apps and other fintech innovations, we now see second- and third-stage technologies being used to democratize investment products that in the past were accessible only to the rich, who could afford their own private wealth managers.

But healthcare is where I see the biggest potential for society, both in medical research and in clinical practice.

Diagnosing a patient is, after all, just a pattern recognition task: a doctor considers your symptoms, lab results, MRI images, and so on, and compares them to what illnesses they have learned about and encountered in their careers so far. But medical professionals, like all humans, are affected by different behavioral biases, such as recency bias, which can lead to unfortunate cases of misdiagnosis. Further, consider the fact that, in the EU alone, 30 million people are estimated to suffer from one of more than 6,000 different rare diseases — and those are just the ones that we know about. No doctor could possibly learn about all these diseases in medical school.

So, one could imagine that computers might be able to assist doctors in diagnosing patients by comparing their health records to the different patterns of diseases in a database. Already today, there are studies showing that computers can be better than humans at spotting potential tumors in mammograms.

With machine learning, researchers are currently studying how innovative types of medical data, including, for example, the gut biome composition, relate to different illnesses, meaning that we will have more noninvasive tests that can catch diseases early, before they cause any symptoms.

Also, in a future where anonymized digital patient records of whole populations are available to scientists, they will be able to slice and dice the data in many ways to find more relevant insights for different subgroups of the population. Because they are typically limited in size, traditional clinical trials often gloss over differences between different age groups, ethnic groups, or groups of people with different comorbidities.

There is the potential for different digitally facilitated services to help us reduce congestion in cities. In the US, Uber offers a service called Uber Pool. Their algorithms find efficient routes for several passengers to share a ride, instead of each taking a separate car. That's brilliant. We need more approaches to society's problems like this!

Alexander: So, there are many developments on the horizon that will make our lives better!

Florian: Yes, but the digital transformation also comes with its problems. Any new technology can be used for both good and bad. For example, authoritarian governments can use facial recognition technology to implement Orwellian surveillance in public spaces.

Even in more democratic societies, the Cambridge Analytica scandal opened our eyes to how our personal data can be abused for political manipulation. Machine learning algorithms have been shown to pick up racist or otherwise discriminatory patterns from the training data that their makers feed them.

Cloud and Big Data technologies require big servers to run on, which use large amounts of energy. If not sourced from renewable sources, these contribute to air pollution and global warming. You might have seen the line “Consider the environment before printing this email,” but maybe we should also add a warning about the environmental effects of reading your mail online!

As an economist, I am also deeply concerned about the gig economy and the potential for it to create a precariat of unseen scale. Jobs with platforms like Uber, Deliveroo, and Amazon deliveries can be stepping stones for people who are able to use that income to invest in their future, but these same jobs can also create dependencies that expose workers to the pricing algorithms of these companies and leave them without any safety nets. This is why our politicians need to build the right social and economic fabric for these technologies to become a force for good for society as a whole.

Alexander: You already alluded to the changing world of newspapers and other media organizations. Given that you have had a number of clients from that sector, how do you see the digital transformation play out in that sector?

Florian: Newsrooms have been hit by several disruptions simultaneously, and I fear for quality journalism, which is in rapid decline, perhaps with the exception of the top-tier newsrooms. That is a real threat to our democratic societies, for which the so-called Fourth Estate is absolutely vital, with investigative journalism helping to hold our elected leaders accountable.

The first disruption was the loss of revenue from classified ads that followed the rise of dedicated websites such as eBay, Craigslist, Gumtree, and others (stage-one replacements of an analog product). Local newspapers were particularly hard hit by this.

Second, big tech and social media companies have become the de facto newsstands of our times, because they distribute individual news articles either directly, as in the case of Apple News or Google News, or by controlling what gets shared on their platforms, in the case of Twitter and Facebook (stage-two technologies). The result was that subscriptions and newsstand sales plummeted.

Even more worrying, industry insiders have told me that the tech giants can tell newspapers exactly what type of content and in what form they want to distribute, not to mention the power of the social media distribution algorithms that decide who gets to see what. In a way, then, they are becoming the de facto editors too.

Third, the only revenue stream left to news organizations was online ads. Yet again, big tech companies are driving this business model. Google Ads is deeply embedded in most news websites because they have all the information from tracking people across the web to serve the reader “relevant” ads — including ads for toasters, weeks after you just bought a new toaster (a stage-three technology).

The issue here is that the ads only get seen when people open the links they see in their news apps or social media feeds. Thus, a lot of what is published is often sensationalized, no matter how trite the story, so as to get you to click the headline.

Alexander: Do you mean clickbait?

Florian: In a way, yes. Engineers at social media companies can tell you that it is difficult to use natural language processing algorithms to filter out fabricated news stories on their platforms, because they are so similar to real news. Partly that is because many people who distribute false news actually believe them to be real. But the opposite is also happening: real news is using a lot of the same tactics to lure people to their sites. Quality journalism gets drowned out in a world of false and trivial content.

What is worse, in the US, there is a trend where we see political organizations, especially on the far right, buying up failing local newsrooms to spread their messages of hate and division. This is really troubling for our societies.

Now, I don't like the blanket news bashing that we hear in pub conversations or in press conferences of certain politicians, so I want to make it clear that there are still fantastic newsrooms out there, such as the Financial Times, the Wall Street Journal, and Die Zeit here in Germany, among others. By pivoting toward digital subscriptions and away from advertisement, they are able to withstand some of these negative influences. In Switzerland there is a great project, called Republik, which is a newsroom that is entirely reader-financed. But these examples are unfortunately becoming the exception and not the norm.

Alexander: You mentioned how some companies pick up on new opportunities and others miss out on them. How then should business models evolve to survive and thrive in an increasingly digital world?

Florian: Many companies use different types of digital technologies to automate and streamline their business processes, but I think you have to look at it not only from an optimization perspective, but also from the perspective of what new value you can deliver to your customers.

B2C businesses in particular have to think about how they can use digital technologies to play in the “experience economy,” as Joseph Pine and James Gilmore call it.¹ Brands like Apple, Starbucks, and Tesla understand they don't just sell phones, drinks, and cars, but that through the various interactions with the customers they create experiences, long-lasting feelings, and, if done right, a sentiment of belonging to a tribe.

It depends of course on the industry, but many businesses can still do better at providing information about their offerings on the web — a simple stage-one technology. Others could think about stage-two strategies, such as interacting with their customers via an app or creating an online marketplace. An example of a stage-three strategy could be to think about what information you can gain from your data that could be valuable for your customers in real time. This could justify a switch to a subscription model, as we see happening in so many markets.

I rolled my eyes when I read the other day that a meal-delivery startup described itself as a “tech company” because they have all that data about their customers. But there might be some truth in it. From my inputs in their app, they know that I like spicy curries and that I don't eat raw fish. If they use that information to set up a just-in-time supply chain for fresh produce but also let me see where my order is at any point in time and maybe also provide me with additional content, for example in the form of tailor-made recipes, then they are actually closer to a tech company than a traditional grocery store.

“What would your business model be if you were founded as a tech company?” This is especially true if you are a brick-and-mortar company. Because if you don't ask that question, someone else will and will roll up your market.

Most businesses need to ask themselves, “What would your business model be if you were founded as a tech company?” This is especially true if you are a brick-and-mortar company, because if you don't ask that question, someone else will and will roll up your market. In other words, companies have to watch out that they don't get stuck in what Clayton Christensen called the innovator's dilemma.²

Alexander: What are some of the mistakes you see that lead to this dilemma?

Florian: Many businesses and organizations realize that there is value in digitalization. But they often just replicate analog processes in digital form, often with the goal of saving physical resources or reducing labor costs. In other words, they are stuck in what I earlier described as stage one of the digital transformation. The companies that are leapfrogging the competition are the ones that don't necessarily try to replicate the analog world, but use the digital building blocks from all three stages to create something completely new — including that customer experience that I just mentioned.

Audi, for example, doesn't give out paper brochures anymore, as most customers peruse Audi's website instead. However, the website doesn't provide that much additional value over a paper brochure. True, you can play around with different configurations, but if you want to buy a new car, you still must haggle with a car dealer over the price.

Contrast that with the experience of buying a Tesla. You can reserve a car and pay by credit card right there and then, just like when you buy a pair of Nike shoes from Zappos. And you never get any buyer's remorse that comes from being pressured by a car salesman.

It starts with the shopping experience, but it doesn't stop there. From the way you contact customer support to providing software updates over the air to unlocking the car with the phone, every rock of the traditional car experience has been turned over to see how it can be improved. Tesla was not successful in breaking into a market dominated by traditional firms because they made cleaner vehicles. Instead, Tesla built the first car for people who think of themselves as digital natives.

Alexander: What will happen to companies that don't level up in digital maturity and organizational readiness?

Florian: As one of my colleagues at Salesforce puts it, it's “digital or disappear.” The best examples are taxi businesses in many countries. Only a couple of years ago you would have thought that, because they are part of the service economy, they are immune to threats by technological innovation. With the rise of the so-called ride-sharing apps, we now know that that was wrong.

But it is a mistake to think that that is only because Uber and Lyft are cheaper. Instead, these challengers successfully built their business models on stage-two and stage-three technologies. They created added value by letting the customer order and pay for a ride with a single click on the app. Compare that with my experience, late at night after a trans-Atlantic flight, having to find a taxi driver at Frankfurt airport who was willing to accept credit card payment. It is mind-boggling that in this day and age many of them still insist on cash payment.

Don't forget the value that the Uber customer gets from having a real-time map showing the suggested route. Anyone who has ever been taken on the scenic route by a taxi driver wanting to squeeze the customer for an extra dollar appreciates this real-time information.

The fate of the taxi industry was not inevitable. The shakeup could have been avoided in two ways. First, taxi businesses everywhere should have emulated London black cabs, with their clean, spacious, and safe cars and their courteous, knowledgeable, and honest drivers.

Second, they should have done earlier what some taxi companies eventually did: they got together and introduced a taxi app.

Alexander: And how can companies increase their digital maturity? Which technologies or digital capabilities are essential for a digital strategy?

Florian: It depends of course on the industry, but it is no secret that machine learning and other forms of artificial intelligence are today's game changers.

I like to say that AI helps organizations “outsource decision-making to computers,” thereby allowing them to automate more of their existing business processes and create new offerings that were unthinkable only a few years ago.

The classic example of process optimization is banks that use algorithms to automatically approve loan applications that meet certain criteria. Some of the more innovative use cases that we have already talked about, whether self-driving cars, ride-sharing services, or smart toilets, demonstrate the potential this technology has for new products in all sorts of fields.

Alexander: But will every decision eventually be outsourced to computers?

Florian: No. While the realm of what AI can be used for will increase drastically over the next few years, there are still plenty of business decisions to be made every day that can't be automated, either because we don't have enough historical data or because we don't feel comfortable surrendering that decision.

This is where the often-cited data-driven decision-making comes in; I actually prefer to call it data-informed decision-making, because at the end of the day it is still the human who will make the decision. You want the human to be able to do so, after having consulted the available data. For that you need technologies that allow everyone in the organization to have access to the organization's data and to make sense of it — business intelligence (BI) tools.

Alexander: What other technologies are interesting besides AI and BI?

Florian: Cloud computing will continue to change things. Here in Europe especially, I see companies catching up in this space. Both in terms of the physical servers that their own app offerings live on, but perhaps even more so in terms of software as a service (SaaS) applications. It started with tools like Workday, which allows you to manage employee leave applications, but nowadays there is an off-the-shelf SaaS tool for almost any type of business process.

Cloud offerings like Amazon Web Services, Microsoft Azure, and Google BigQuery also allow companies to build their own applications and help power a lot of the AI and BI processes that we just talked about.

Besides these broad-scale technologies there are innovations that might be more specific to certain business processes or industries. Blockchain will enable fast verification of all sorts of transactions. Lamborghini, for instance, uses blockchain technology to certify the authenticity of their vintage cars. Buyers can follow the history of the car and all its spare parts and thus be assured about what they are purchasing.

But you could imagine applications that are useful not only for the super-rich. Anyone who has ever bought or built a property knows that it takes weeks to clear many bureaucratic hurdles. What if building permits could be issued via blockchain technology? What if a contract could be notarized with the push of a button?

Fast, reliable business transactions will be like grease to our modern business machinery. At the same time, by making transactions transparent, it could mean the end for another type of “grease,” namely, bribery, kickbacks, and other corrupt practices that endanger the trust of our societies.

Alexander: How can technology shift the roles and responsibilities of the workforce? You mentioned the commoditization of modern digital technologies before.

Florian: First, we already talked about how everything that can be automated will eventually be automated, whether it is capturing or presenting information (what we called stage one), transmitting information (stage two), or making inferences from that information (stage three). Therefore, fewer of those tasks will be done by humans.

Instead, your employees will generate new ideas, create new content, and make decisions that can't be based on historical data. The role of the knowledge worker will continue to become more important. In many organizations it is, for example, common for someone to take notes during a meeting and share them with the rest of the team afterward. AI-based transcription services can now automate that task for you. That means that that person who used to have their head buried in the notepad now can join the discussion and contribute new ideas.

Second, and this is where the commoditization of digital technologies comes in, everyone will in some ways become involved in setting up new automated processes, whether that is by working with ready-made SaaS offerings or by creating customized services with so-called low-code platforms.

We are even seeing first products that commoditize machine learning so that anyone can deploy AI technology on their data.

The integration of separate services also will become more prominent as B2B companies offer more holistic offerings, such as the Salesforce Customer 360 platform. Offerings such as those by MuleSoft allow you to string together your different data sources and the individual steps of business processes.

At a simpler level, the online tool IFTTT, which stands for “If This Then That,” lets anyone connect their popular business apps with one another, including Outlook, Google Sheets, Asana, and many more. For example, you can easily connect your SurveyMonkey form to Slack so that you can be notified when a new survey response has come in.

In other words, many things that traditionally were done by IT can now be done by the subject-matter experts themselves. Therefore, the role of IT changes too. It is more about managing and enabling, as opposed to creating new systems.

Alexander: How can companies today prepare their employees to achieve organizational readiness for the digital future?

Florian: Everyone should have a basic understanding of how machine learning works. I don't mean that everyone should know how to code, but they should be able to explain the concept to their grandmothers so that they can evaluate the potential of using AI-powered applications in different business processes.

Given how AI changes our everyday lives, I would recommend that everyone take the free online course “Elements of AI” that was created by the University of Helsinki.

I would also make sure that all my employees have some basic data literacy skills to be able to take part in the conversations. Once, when explaining some research findings in a meeting, I had a participant ask halfway through, “Why are we looking at all this data in the first place, when statistics is all lies anyway?” I had to take a long detour to explain the fundamentals of what we were doing.

Alexander: What can managers do to develop and foster the company's digital culture?

Such a culture would be one of collaborative problem solving, innovation, and constant introspection. Now, there are many factors that influence a company's culture, but here are a few things that any manager can practice.

First, encourage employees to point out problems and inefficiencies. I know an organization where every day a certain boring and time-consuming task is performed, involving the manual transfer of content from one system into another. For over 10 years, complaints by employees fell on deaf ears. Some employees were admonished for their lack of enthusiasm. Not surprisingly, attrition is high on that team. A simple upgrade of the system that would automate the bulk of the process could have saved many labor hours and, more importantly, the morale of the team.

Managers should ask themselves whether they can apply the Japanese manufacturing principle of the Ando cord. In car factories this is a safety cord that any employee is allowed to pull to stop a production line if they think there is a problem. The tool in itself is secondary. It is the culture that it fosters — one where anyone's input is taken seriously, no matter their pay grade. If you want to use the digital transformation to grow your business, you need every input that you can get.

Second, because it is not enough to just optimize, you will want to keep some creative heads around too. People who can come up with innovative ideas that will help you leapfrog ahead, rather than just change incrementally. That means you need to learn to put up with their quirks and antics. In an effort to treat everyone the same, creative people are often pushed out of organizations, and teams become too homogenous in their thinking.

Third, empower your IT and subject-matter experts to come up with solutions together. When I worked at a business school, we asked the IT department to set up a form where students who wanted to change courses could put their names on a waiting list. Since IT didn't have time to help, we secretly proceeded to set up a form using an off-the-shelf SaaS tool instead. This sort of self-service culture should be fostered, not forbidden. But you still want IT to vet and manage the different solutions; you can't have everyone rebel against IT, fun as it was for us back then.

Alexander: Returning to the socioeconomic consequences of the digital transformation, 10 years ago you predicted that cloud applications like Google Docs and Microsoft Office 365 would change the way we work. You argued that knowledge workers could be more geographically mobile and less dependent on corporate employers, because they could own the tools of their trade — their laptops. Has this become a reality, and where do you see this trend going in the next 10 years?

Florian: Business applications in the cloud have absolutely made remote collaboration so much easier. I have worked with many geographically distributed teams and with tools like Google Docs, Slack, and WebEx, by and large, collaboration was as good as, if not better than, it was in in-person teams.

What hasn't happened yet is that all knowledge workers became freelancers, partly because of IT security concerns that prevent people from bringing their own devices to collaborate. The bigger issue, though, is that in many countries being a full-time employee is a requirement of becoming part of the social safety net. The US, where health insurance premiums and retirement fund contributions are paid by employers, is an extreme example.

All that said, the number of freelancers seems to be going up. In the US, 35 percent of the workforce has done freelance work in 2019, with 28 percent of the workforce doing it full-time — that is up from 17 percent in 2014.³

Alexander: Can a universal basic income (UBI) help here?

Florian: If implemented well, it could make it a lot easier for people to transition back and forth between employment, freelance work, educational breaks, and, yes, even time for self-discovery.

But it is not enough to simply give people EUR 1,000 a month. It is about changing the mindset that being part of the social net requires you to be an employee. It is about making sure that healthcare and retirement plans are continuous as you transition between different life stages — or even from one employer to the next — and that people who are stuck and can't find work get the necessary help.

The old model for the social safety net in Western societies, where transfer payments such as pensions and unemployment benefits are conditional on having held a “proper” job, worked for the industrial age, but the digital age might require a new framework.

Our politicians would love to see more tech startups in Europe. We need to provide people the flexibility required for modern work and life if we want the next Google to come from here.

Alexander: Whether it is to finance a UBI or to pay for unemployment benefits, some people have suggested that we tax machines to compensate those who lose their jobs as a result of the digital transformation. What do you think about that?

Florian: Taxing machines and technology would be the wrong approach. All long-term economic growth ultimately comes from the fact that machines make us more productive and that we can have ever more specialized division of labor. We can afford the things that make our lives better and easier, because technology enables specialists to produce them for us, and the market allows us to trade them in exchange for our own services in the domains that we are good at.

Alexander: Today, many knowledge workers spend time analyzing data, creating slides, and writing emails. In the next 10 years, what do you think our work will look like?

Florian: It will be more data analysis, fewer emails, and about the same amount of time creating slides.

The more data analysis part is probably obvious. We will be inundated in data, whether we like it or not. The different steps of the analysis process will be more and more automated and commoditized so that even people with very limited experience in data analysis will be exposed to it.

The fewer emails part is perhaps wishful thinking, but I am hopeful that other forms of digital communication will replace it. Tools like Slack, Chatter, Quip, and Teams are great if used correctly.

Their greatest value is that they allow us to organize conversations around topics or projects, whereas emails are organized by who sends what to whom (and who is left off the recipient list). The benefit of that cannot be overstated, as it creates true transparency and free knowledge transfers within organizations. This only works, however, if people are intentional about this. I have seen it happen that a social collaboration tool was implemented and then employees used it to directly message their colleagues, just like they would with email.

One thing I can see happening is that we will use data dashboards more often in meetings rather than slide decks with static charts. Charts and figures are often the cues for further questions. My colleague Andy Cotgreave likes to say that the quality-control method of asking “five whys,” as pioneered by Sakichi Toyoda at Toyota, is what one should use when using data to answer questions: don't stop until you have asked “why?” five times.⁴ Being able to interact with data during meetings allows one to go down such routes of interrogation.

Alexander: Do you think that the automation of processes and the commoditization of digital tools will mean that we will have much less work to do? Would it be an option to reduce work time, let's say to three days per week, to avoid a loss of jobs?

Florian: Individual tasks can take up less time when new digital tools are introduced, but it is a fallacy to think that the overall amount of work will decrease as a result. There is no fixed stock of work that needs to be done. Instead, when new tools help us increase our productivity, we, as a society, tend to produce more. Hence the effect on economic growth I mentioned earlier. Also, don't forget people have to build these tools.

There are two caveats to this answer, though. First, individual jobs will fall away. Companies used to have typists who would type up the letters that their bosses dictated for them on a voice recorder. With the advent of the personal computer, workers can now write their own letters, and as a result many typists lost their jobs. This process will repeat itself continually as new technologies are adopted to automate tasks. Therefore, our education and welfare systems are crucial. They can ensure that people have the right skills for the right jobs and that they can transfer from those jobs that have become obsolete into those that society requires.

Second, we might still see a change in work time in certain professions. The traditional five-day workweek with eight-hour days made sense in the industrial age, where everyone had to be at the conveyor belt at the same time for the production process to work.

With the rise in knowledge work, that requirement is going away in many places. It makes more sense to ask what the best work schedule for a knowledge worker would be for them to achieve peak performance. For some it might be a three-day work week; for others it might be a different arrangement.

Perhaps we even must reframe it from “work time” to “time when you are contactable” and from asking people to sell you their time to selling you their productive output. You see, when you get your best work ideas in the shower, the concept of a workweek goes out the window!

Alexander: Earlier you mentioned self-driving cars and digital patient records. In many countries, efforts to further drive these innovations are hampered by local regulations. What role does the government play here?

Florian: If we, as a society, are to benefit from all the new technologies that are becoming available, regulation needs to not only catch up but actually spearhead the development, especially when it comes to crucial infrastructure investments.

In terms of self-driving cars, a lot of the public discourse centers around the question of who is liable when something goes wrong. That is an important question, but it shouldn't dominate the discussion and hamper any progress. This is a technology that already can save lives and will do so even more in the future. So, any day that goes by that we don't make progress on this front means more people will unnecessarily die or get injured in road accidents.

When people rightly point out that the computer vision used in self-driving cars sometimes struggles with bad road markings, that shouldn't be a warning about how bad self-driving cars still are. Instead, we should go out and make sure lane markers are painted properly. That is a paltry cost compared to the huge benefit we would derive from it.

You could go further: Why do cars have to learn how to “see” lanes and stop signs and so on in the first place? Why haven't we thought about using technology so that the infrastructure can communicate with cars more directly? It is not that far-fetched when you think about the fact that trains receive signals from the tracks that they need to stop automatically when there is a red light, or that airplanes receive radio signals from the runway to indicate the perfect glide path. Incidentally, trains and planes are also much safer, generally speaking, than cars.

It is a similar story with digital patient records. I see, of course, the sensitive nature of digitizing this kind of data, but it is a tractable problem that can be solved, and, again, the opportunity cost of sitting on the problem is bigger than doing something about it. Countries like Canada, Denmark, and Estonia have shown that it can be implemented safely for the benefit of society. Other countries can learn from them.

Alexander: Thank you, Florian. What quick-win advice would you give that is easy for many companies to apply within their digital strategies?

Florian: Find out what the key strengths of your employees are! We have talked a lot about new digital tools, but who are the right people to operate these tools? There are two mistakes that are being made in this context.

The first is to think that just because the tool is simple to operate, anyone can now do so. I have seen this with the commoditization of survey tools that make it easy for anyone to create survey forms. It actually takes quite a bit of knowledge to set up a survey that will yield reliable results! That doesn't mean you have to hire people with the right skills for every task at hand. But you have to ask who might be able to learn the basics of good survey design.

There are of course many tools that don't require any specialized skills. Still, it would be a mistake not to think about who should be operating them. With modern content management systems, for example, it is easy to update the content of a website. But should the person who wrote a blog post for the organization's website also be the one to enter the text into the system? Perhaps not, because there is a huge opportunity cost of asking a talented writer to fiddle around with the website, both in terms of their time but more importantly in terms of whether they find enjoyment and fulfillment in their work.

Knowledge workers can be divided into two groups: the more creative ones who are good at abstraction and the more detailed-oriented ones who are good at implementing. So find out what your employees' superpowers are. There are different ways to go about it, but I particularly like the Strengths Finder test by Gallup.

Alexander: What are your favorite apps, tools, or software that you can't live without?

Florian: I'm a little biased, because I work for the company that makes it, but I am a huge fan of Tableau Prep, which is a tool for cleaning, restructuring, and integrating different data sources. It allows you to see the changes that you are making to a data table before you run the script. I am a curious creature and like to dig into all sorts of different data sets, so this tool has become a real game changer for me.

Another app that I recently discovered is Notion. I use it for my to-do lists, to keep web snippings, and to jot down ideas. But it has many other functionalities, including Kanban bords, and calendars, timelines, and it can be used by teams too. It is like a Swiss Army knife for organizing content and tasks.

Alexander: Do you have a smart productivity hack or work-related shortcut?

Florian: This is going to sound funny, given the topic of this book, but there are two hacks that I use, and they both involve protecting myself from digital content!

The first is that I have blocked Twitter, Facebook, and most news sites on my work laptop. In his book Indistractable, author Nir Eyal explains how these sites are designed to keep you on them for as long as possible, for example, by suggesting ever more content for you to check out. So I try my best to avoid tripping into one of these time sinks. Nowadays, I get my daily news update mostly by listening to the radio, and I try to stay in touch with friends mostly by phone and text message.

The second hack is that I try to avoid looking at any type of device after 10 p.m. The blue light emitted by screens suppresses the rise in melatonin that is required for us to feel sleepy at night. From the book Why We Sleep by Matthew Walker, I learned that most people who claim they can get by with less than seven hours of shut-eye are lying and are hurting not only their productivity but also their long-term health.

Alexander: What is the best advice you have ever received?

Florian: When I worked for the World Economic Forum (WEF), I was involved in several projects creating digital offerings for the WEF community, including what was essentially a social collaboration platform for the world's foremost leaders and thinkers. A colleague there introduced me to the P-O-S-T framework for thinking through new project ideas like these:

 People: Define your audience.

 Objective: Define what you want to do for your audience.

 Strategy: How will you achieve that objective?

 Technology: What technology do you need to implement the strategy?

This is extremely useful, because when we get excited about a new technology, the temptation is to try it immediately, without having really thought through whether it makes sense in the given context. With the T being the last step in the suggested process, the P-O-S-T framework is a nice forcing function to counter this inclination.