The value of engineering teams in the age of digitization

A startup's mission is the search of a business model that is repeatable and scalable. The search for such a business model must follow the scientific method, which is based on one or several testable hypotheses and the empirical validation of such hypotheses through experiments and result analysis. If the company succeeds in defining such a business model, it is considered a success. If it fails, the organization may cease to exist. As failing may cause the end of the organization, the startup is under immense pressure to quickly iterate upon ideas, gain relevant knowledge and to define best practices in order to drastically reduce the initial risks as fast as possible. Sometimes the learning processes causes severe organizational changes in order to adapt to new situations and insights. In other words, the number one predictor of success for young startups is rate of iteration.

The company on the other hand is designed to execute a business model that is repeatable and scalable. It already has a repeatable and scalable business model and is focused on executing it. The capability of executing the business model is also the result of successfully standardizing the relevant processes of an organization. During the years those processes were gradually optimized in order to guarantee predictable and desired outcomes. In this sense processes not only define a framework for employees of how to behave and interact with relevant parties, but they also favor conformity as there is no need to think outside the box. Why waste time on how to improve something when you get paid for following the process? Although standardized processes are essential in order to work efficiently within well known domains, they backfire once a company relies too much on them while refusing to invest the time to understand new situations and contexts (e.g. keep the status quo, keep well known control structures). As companies grow, the quest for standardization establishes communication flows and control structures, in which the search for innovative solutions is gradually replaced with finding consensus among way too many stakeholders. In such environments proactivity, boldness, openness and collaboration are the ideals everyone is supposed to strive for, but for some reason communication tends to become political and indirect. Where people should be in the focus, we talk about the state of tasks. Tasks that are in some cases so narrowly sliced that one may wonder why there is a task for that. And after a long business day the stakeholders and leads like the generated output, although the teams only managed to discuss those tiny tasks and collaborativley changed their states. But at least they followed the process.

In other words, by focusing too much on processes and their correct execution in way too many areas, the company not only looses its ability to focus on outcomes but also its ability to build to learn. And this threatens its capabilities to adapt.

I believe that agile and lean principles are not only a tool for corporations to implement `waterscrum` but that their application provide real benefits for people in large organizations to not only successfully deliver customer validated top notch user experiences, but also to help them keep and/or retrain their desire to learn, innovate and improve.

This can be achieved by:

- putting people before profits

- incentivizing behavior not performance (someone has to do something to achieve that performance, so there is a cause and an effect; you incentivize a cause that will hopefully yield the effect; the cause is behavior)

- building to learn

- focusing on value not output

Keeping these aspects in mind may support you in achieving both building a healthy software engineering culture and strong teams.

In these days, corporations are literally forced to reinvent themselves for their digital futures (Digitization) in order to stay competitive. There are four key elements that need to be addressed:

1) Digital strategy - How do you make progress without being certain of the endgame?

2) Business model - How do you rediscover the raw customer need, unconstrained by today's model?

3) Enablers - Do your current data, technology, operating model and talent enable or disable digital progress?

4) Orchestration - Are you moving beyond experimentation to scaling digital across the enterprise?

Strong engineering teams are capable of significantly contributing to all of the above.