Jan Bosch is a research center director, professor, consultant and angel investor in startups. You can contact him at jan@janbosch.com.
During the last weeks, I reflected on the research activities in the Software Center for which I’m the director. I realized that everything we do with the partner companies and the universities is concerned with increasing the pace at which we’re delivering new value to customers. A decade ago, most companies relied on product generations for this, but over the last decade, we’ve seen a shift to periodic software updates and now continuous deployment with very frequent updates to deliver new value. We’re moving to a world where every system we use gets better all the time.
Although continuous deployment is still a challenge for many companies, it doesn’t stop there. The next hills to take are already up ahead. These include conducting A/B testing using systems deployed in the field as well as (federated) reinforcement learning to allow systems to fully autonomously experiment with their own behavior to continuously improve the value experienced by customers.
Everyone talks about digitalization as a goal in itself. In my view, however, it’s an enabler to achieve something else: a fundamental shift from a transactional business model and relationship with our customers to a continuous one. The transition from keeping products as static as possible after they leave the factory to continuously changing and improving already deployed systems requires a significant change across the company. Digitalization is such a hard challenge as it touches every function and capability in the organization, ranging from business models to deployment and from sales to finance. To survive and, preferably, thrive in this transition, companies need to develop new capabilities to capitalize on the new model of delivering value to customers.
In traditional value delivery, each product that leaves the factory is basically frozen and deteriorates over time until the customer decides to replace it. The new product is typically a little better than the previous one, but it also will deteriorate over time and be replaced. In the continuous value delivery model, the value provided by the product, system or offering is continuously increasing. This occurs through the continuous deployment of new software but may also require periodic replacement of electronics and occasional replacement of mechanical parts. The system gets better every day I use it.
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To successfully transition towards continuous value delivery, companies need to build new capabilities across the organization. Major changes are required in at least eight areas.
1. Business
The shift from transactional to continuous value delivery has business strategy implications and changes the business model and the customer interface, including sales and support.
2. Architecture
Both at the system and the software level, the architecture needs to be addressed to support a superset platform approach, modularization and instrumentation.
3. Process
The ways of working in a continuous context require much faster cycles of iteration, which affects our processes. Many tasks need to execute in parallel instead of sequentially, which of course is the nature of Agile. However, it also requires a mature build and test infrastructure. Finally, the short cycles allow for data-driven ways of working that weren’t feasible earlier, but these need to be capitalized upon.
4. Organization
Traditional, functionally organized companies are great at efficiently performing repeatable tasks in low-change environments but very poor at responding quickly in high-change environments. We need new ways to organize, new skills and different leadership styles.
5. User innovation
Especially in software-intensive industries, most innovation was traditionally of the sustaining, evolutionary type. In a digital transformation, we need radical innovation that works quite differently as we’re inventing new ways of serving customers through new offerings and services.
6. Technology innovation
The constant flow of data-driven technologies, including experimentation approaches such as A/B testing and machine and deep learning, require us to invest in technology-driven innovation to complement user innovation.
7. Automation
One of the sayings in Agile is that when it hurts, you should do it often. Digitalization and DevOps create this reality and the consequence is a significant increase in the need for automation. The challenge is to find where it hurts and then automate in such a way that we keep relevant flexibility in the areas that matter.
8. Business ecosystem
The superset platform, continuous value delivery and data from the field allow for a very different interaction with the business ecosystem around the company. By platformizing, we can open up to third parties to build extensions to the entire portfolio, creating multi-sided markets with network effects.
This may seem like a lot to take in, but it hammers home the challenge of digitalization and why so many companies struggle with the transformation. I see many companies take initiatives in one or a few areas and then ignore other important dimensions, resulting in failure, disappointment and loss of valuable resources. In the coming weeks, we’re going to work our way through each of the capability areas and deep-dive into each of the key capabilities in these areas.
Surviving and even thriving in a digital transformation changes the entire company. It requires building new capabilities in at least eight areas: business, architecture, process, organization, user and technology innovation, automation and ecosystems. Focusing on one and ignoring the others results in failure and disappointment. Instead, focus on the promise of continuous value delivery to customers and lasting, continuous relationships with your customers where everything gets better all the time. Who doesn’t want that?