Many companies are good at generating sustaining innovations in their mature, revenue generating products. We know what KPIs customers care about and we focus our efforts on continues improvement of these KPIs. For example, because of regulation as well as customer preference, the fuel efficiency of vehicles as well as their reliability has steadily improved over the years.
The challenge with digitalization is that the sustaining innovations that were successfully convincing customers to keep buying become less effective and customers want to see different forms of delivering value. As an example, the car that I drive (a German brand) has lane keeping and adaptive cruise control as a feature and I use it quite a bit. However, the functionality is not very good and the car has a tendency to act weirdly in specific situations. I am OK with that. The thing that annoys me to no end, however, is that I know that the functionality will stay equally bad for the entire time that I will own the car. Unlike my computer, my phone and other equipment, it will not get better!
Similar to how Nokia (for a wide variety of reasons that I will not go into here) missed the transition in consumer preference from product variants to apps on top of a product with minimal variation, many companies are at risk to miss a fundamental switch in customer preference due to digital technologies. As always, your customers will not be able (nor is it their job) to tell you what they want, but they most certainly will recognize it when they see it and change their buying behavior. Of course, you can keep going on momentum, brand and customer relationships, but you need to adjust or risk disruption.
When it comes to digital technologies, there are at least three major technological approaches that need to be adopted, i.e. DevOps, A/B testing and artificial intelligence. The essence of digitalization is a fundamental shift in value delivery from transactional to continuous. For most companies, this can only be achieved cost effectively by changing the software in the offering, rather than anything physical. Frequent updating of software in deployed products of course brings us to DevOps. For digitally born SaaS companies, this is obvious beyond belief and industry best practice for close to two decades now. However, for many cyber physical systems companies, however, this is still a work in progress. There are many reasons, including regulations and certification as well as many of the company internal justifications that we have discussed in this series of posts to date, but the the fact that it is hard is no reason to not get there.
The second major technological approach is concerned with A/B testing and other experimental approaches. When we are able to deploy new software in systems in the field, we are also able to get data back from these systems. This opens up a quite significant shift in how we work with requirements and features as rather than guessing about the value to customers of new functionality, we can actually measure it. By deploying small slices of new functionality in some systems and comparing the key KPIs between systems that have the new functionality with those that do not, we can quantitatively and statistically determine the impact of new functionality. That allows us to stop development of features that have no or even a negative impact and to double down on the things that really move the needle in a positive way. For anyone who has been in feature prioritization meetings between product management and R&D, the idea that we can decide what to include based on experimentation instead of rhetorics and storytelling, this should come as a relief!
No post on technology driven innovation can ignore artificial intelligence (AI) and this one is no exception. I have written about our work on AI and AI engineering in several earlier posts and my position has not changed: machine- and deep-learning (ML/DL) offers fabulous opportunities for new forms of value. In order to capitalize on that, though, ML/DL requires data, and often lots of it, in order to function well, which requires the constant flow of data from systems in the field. Similarly, ML/DL models should be subject to the same DevOps cycle (often referred to as AIOps or MLOps) as all other software in our systems.
Concluding, most companies are very good in technology driven innovation for their main revenue driving products. With digitalization, however, the innovations that drove product success in the past need to be complemented or replaced with digital technologies and technological approaches. Three of the main ones include DevOps to continuously deliver value to customers, A/B testing to quantitatively validate the value of new features and functionality before building it and artificial intelligence as it allows for much smarter system behavior in a variety of contexts. As Tim O’Reilly said: What new technology does is create new opportunities to do a job that customers want done.