With all the focus on data and AI, it was simply a matter of time before the countermovement started. Reflecting on several discussions around this topic that I’ve had over the last year, the key theme seems to be that data and AI are predicting the future based on the past and as long as … Read moreDoes data-driven decision-making make you boring?
This article is the last of four where I explore different dimensions of digital transformation. Earlier, I discussed business models, product upgrades and data exploitation. The fourth dimension is concerned with artificial intelligence. Similar to the other dimensions, our research showed that there’s a clear evolution path that companies go through as they transition from … Read moreThe AI of digitalization
Over the last months (actually, more like years), we’ve studied the digital transformation of several companies in the Software Center. Professor Helena Holmström Olsson and I developed a model to illustrate how they actually transition from their legacy business rooted in atoms to a digital business based on bits (see the figure). It has four … Read moreDigital for real: business model
Imagine the following scenario. A (sizable) team at a large company writes customer documents in response to customer requests. They request help from the automation team to reduce their repetitive tasks. The automation team brings in an AI company, which develops an ML model that generates the customer documents automatically and virtually eliminates the need … Read moreWhy you’re not deploying AI
Digitalization is fundamentally enabled by three core technologies: software, data and artificial intelligence. The common denominator, which is inherent in a digitalized business, is that automation is at the heart of it. Digital technologies allow for automation to a much more significant extent than traditional technologies. We see this reflected in companies: whereas in traditional … Read moreDigital business: automated at heart
DevOps, DataOps, MLOps – the number of different “Ops” combinations seems to have exploded over the last year or so. There are manifestos, meetups, lots of blog posts and research articles about these various approaches. In order to get clear on terminology, I think it’s good to define what we’re talking about. So, first, DevOps … Read moreWhat’s with all the Ops?
Few technologies create a level of hype, excitement and fear these days as artificial intelligence (AI). The uninitiated believe that general AI is around the corner and worry that Skynet will take over soon. Even among those that understand the technology, there’s amazement and excitement about the things we’re able to do now and lots … Read moreAI engineering: making AI real
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
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
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