Director of Data & Insight at Kolonial.no

Nina Walberg has built up and is leading the Data & Insight discipline in Kolonial.no (now 30 people). Before Kolonial.no, Nina established and built up Data Analytics in VG and then established Insight across the Media houses in Schibsted as part of Schibsted Product & Tech. She spent seven years in BearingPoint as a management and technology consultant leading both Business Analytics and Financial Services in Norway. Nina has a Master of Technology from NTNU in Industrial Economics and Technology Management.

Tell us about your first encounter with artificial intelligence (AI)?

My first hands on experience must have been at University some years after Y2K. I did my specialization in Operations research and decision support. We implemented a bunch of techniques that I think is included in some definitions of AI.


What is your competence within the field of AI? 

  1. In a bigger project at Univeristy I tried to design a dynamic optimization algorithm that would control which store meat products should be transported to and adjust their expiration date based on sensor data. My master concerned optimization of oil production, a highly manual process of valve adjustments at least back then. 

  2. After University I have experience with models to predict credit risk in banking and several applications of machine learning in a few different industries. In recent years I have had little hands on work myself, but have been involved as a sparring partner and stakeholder in many projects in Kolonial.no and before that Schibsted. One example was the work done on forecasting our demand where we have several models in production.


Why did you develop an interest in AI? 

Being someone who have always loved math and physics at school, it was wonderful to discover how these methods could be applied to real life problems for problem solving and process improvements. 


Can you recommend a relevant book or film about AI? 

I remember reading the book “Competing on Analytics” by Tom Davenport and being inspired just after I was done at Univeristy. It is though very old, so I don’t know how relevant it is today. I just saw an episode of Peppa Pig the other day with my daughter called “The Electric Car”, and was surprised how AI ethics was included even there. My five year old started asking me a bunch of cool questions about self driving cars and who is responsible. 


Why should we, or should we not, be afraid of AI? 

I am a very optimistic person, and that also includes technology. I think many are afraid because it is complicated to understand and therefor trust as it is still new for us. That is not the first time we see this with new technology. I though believe we will need regulations in this area as most other aspects of society. The biggest problem I see right now is that regulators in many cases struggle to catch up, and are not always seen as relevant. 


Which field, in your opinion, has the most to benefit from AI – and why? 

I see a lot of opportunities in most industries and problems I have faced. A few weeks ago I attended a data strategy workshop with NAV. I hope we as a society will allow them to apply these kinds of methods on relevant data to automate and speed up the bureaucracy. The potential is huge in improving their service level and also ability to personalize and become more relevant for their users. 


How should the use of AI develop in the future?

I think we are at a quite interesting time right now where we start to see these methods applied across many different problems and industries. Consumers are starting to have trust in these (i.e. self driving mode on newer cars). At the same time I think we are in early days, and the field still feels quite immature in many areas. I really look forward to follow and try to be part of this development in the coming years and decades.


Why should participants tune in during AI+? 

The presentation is potentially an application of these kinds of methods applied in circumstances where others would not have thought about using it. We had limited data, high degree of uncertainty and the stakes where huge for us as a company. I am proud that we where able to apply a structured decision making process in preparation of the board meeting. The contents will be understandable for all, independent of background, and could potentially be inspiring for this kind of decision making for many circumstances.

Nina Cecilie Walberg is a speaker at the AI+ virtual conference 2021.