Moving from tactical to strategic use of AI

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Let me start right off the bat: many incumbent organizations risk missing out on the strategic opportunity of AI — instead focusing on tactical optimization initiatives.

General purpose technologies like AI have the potential to change how companies operate and create value, disrupting entire industries along the way. History proves however that new technologies thrive under new paradigms. That means their immediate applications are often far off from the real potential that they will bring in the long run. The good news is for AI that these paradigms are already emerging— they are just unevenly distributed.

Using AI for both…

Five key principles to build an AI production platform

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AI is beyond the hype and entering a phase of industrialization. The belief about the imperative of AI is widespread— universally across industries. However, only about 5% of global enterprises is ready for industrialized growth of AI (according to this study). To reap the real benefits, organizations need to be able to scale AI solutions. This all sounds obvious: everybody talks about AI and scaling is the name of the game.

But what does scaling AI mean exactly? And what requirements does that pose to an organization’s data and technology stack? How is that different from most legacy infrastructures? …

Why the real impact of AI is yet to come and how to make that happen

There is a famous story about how Ford and Mazda applied new information technology entirely differently — one of them successfully. The story takes place in the early nineties, when most corporations had heavily invested in automation technology like SAP. Ford and Mazda had both implemented ERP systems to improve their procurement processes. Ford owned a minority stake in Mazda and therefore had insights into their operations. At some point, Ford noticed that Mazda employed only about 5 people to run procurement whereas Ford had over 500. How could both companies run the same process, for the same type of…

A must-have capability after COVID-19

Imagine you are a telecom operator with €27bn to invest. You have to choose between hundreds of thousands of possible options. Different network areas, different technologies and different cell tower locations. All with different costs, revenues and returns. The only catch: the expected returns for all individual options vary widely and are hard to know in advance. What do you do?

In aggregate, that is the yearly challenge for all European telecom operators combined. It is a multi-variable optimization for which the outcomes have to be predicted. Traditional approaches fall short, especially done at scale. As it turns out, AI…

Wouter Huygen

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