Combining innovation and operation

One of the well-known struggles of every company I work with is to combine innovation with efficiency-oriented operations. This is the classic problem of ambidexterity: the company needs to deliver on today’s revenue and margins while securing its future. The problem is not that companies aren’t aware of the challenge but that they lack the … Read moreCombining innovation and operation

Are you building a minimal viable elephant?

As part of the research in Software Center, I work with dozens of companies in the software-intensive embedded systems space on a variety of topics. One of these topics is the development of new products. Having worked with online companies, as well as startups, I’ve become indoctrinated with Steve Blank’s ideas and the “lean startup” … Read moreAre you building a minimal viable elephant?

Finding your AI business case

Having worked with companies on the use of AI, I’ve noticed an interesting pattern: although most of the attention is spent on algorithms, data storage infrastructure, training and evaluation of applications, the hardest part very often seems to be coming up with a promising concept in the first place. When exploring promising concepts, many start … Read moreFinding your AI business case

Why your strategy fails

During the last weeks, I’ve experienced multiple situations where an organization (industrial or academic) simply doesn’t have a business strategy or a strategy concerning a key area for their business. When probed and questioned on the strategy, I’ve observed at least three types of responses. First, leaders in the company say that there is a … Read moreWhy your strategy fails

How to generate data for machine learning

In recent columns, I’ve been sharing my view on the quality of the data that many companies have in their data warehouses, lakes or swamps. In my experience, most of the data that companies have stored so carefully is useless and will never generate any value for the company. The data that actually is potentially … Read moreHow to generate data for machine learning

AI is NOT big data analytics

During the big data era, one of the key tenets of successfully realizing your big data strategy was to create a central data warehouse or data lake where all data was stored. The data analysts could then run their analyses to their hearts’ content and find relevant correlations, outliers, predictive patterns and the like. In … Read moreAI is NOT big data analytics

Why your data is useless

Virtually all organizations I work with have terabytes or even petabytes of data stored in different databases and file systems. However, there’s a very interesting pattern I’ve started to recognize during recent months. On the one hand, the data that gets generated is almost always intended for human interpretation. Consequently, there are lots of alphanumeric … Read moreWhy your data is useless

The game plan for 2020

In reinforcement learning (a field within AI), algorithms need to learn about an unexplored space. These algorithms need to balance exploration (learning about new options and possibilities) with exploitation (using the acquired knowledge to generate a good outcome). The general rule of thumb is that the less is known about the problem domain, the more … Read moreThe game plan for 2020