From Data to Wisdom: Novo Nordisk’s Tonia Sideri

**Sam Ransbotham** (0:02)
You might not often hear terms like empathy and design thinking when talking about AI projects, but on today's episode, find out how one pharma company's AI Center of Excellence takes a holistic approach to technology projects.

**Tonia Sideri** (0:16)
I'm Tonia Sideri from Novo Nordisk, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:22)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone who is somewhat innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:40)
And I'm Shervin Khodabandeh, senior partner with BCG, and I co-lead BCG's AI practice in North America. Together, MIT SMR and BCG have been researching and publishing on AI for six years, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:07)
Today, Shervin and I are joined by Tonia Sideri, head of Novo Nordisk AI Center of Excellence. Tonia, thanks for joining us. Welcome. Let's get started.
First, maybe can you tell us what Novo Nordisk does?

**Tonia Sideri** (1:19)
We are a global pharma company. We are headquartered here in Denmark and we are focusing on producing drugs, supporting patients with chronic diseases, such as diabetes, obesity, hemophilia, and growth disorders.
We are 100-year-old company, but still growing a lot, but still very committed to our original values of the company and to our social responsibilities. There's more than 34 million diabetes patients using our products and we produce more than 50% of the world's insulin supply.

**Sam Ransbotham** (1:54)
Currently, you lead the AI Center of Excellence.
So what is an AI Center of Excellence? What is your role there? What does that mean?

**Tonia Sideri** (2:02)
AI Center of Excellence can have different flares in different companies, but what we do, we are a central team located in the company's global IT.
We are a group of data scientists, machine learning engineers, and software developers working via a hub-and-spoke model across the company. So we want to minimize our distance from ourselves and our experts in the company, our data and domain experts, by working in cross-functional teams, product teams across the company.
And we also want to increase the speed from where we go from a POC of machine learning model to production. And that's why we have analytics partners working across the company. And we also have an MLOps product team focusing on creating microservices across the whole machine learning model life cycle. We want to take all the petabytes of data we consume as a company, all the way from our molecular identification to our clinical trials, to our commercial execution and production and shipping of the products, and take them from database, from flat files, from cloud storages and convert them to something that is ultimately useful for the company and ultimately support patients' lives. And that's what we are here for. We want to bring this data to life. We are around one and a half year old as a team, and we already have projects across the company. They were working with our R&D, for example, with using knowledge graph to identify molecules for insulin resistance. We have deployed different marketing mix modelings and sales uplift recommendations models across different commercial regions.
And last but not least, we have recently deployed a deep learning machine learning model that use a vision inspection in our inspection lines. And that's very important because it's an optimization on existing process. However, it gave us a lot of skills of how to have live machine learning models in a very regulated setup with this a GMP set of good manufacturing practices one.

**Sam Ransbotham** (4:09)
How does that work? Tell us more about that. That seems quite interesting.

**Tonia Sideri** (4:13)
We were already using visual inspection the last 20 years from a rule-based approach that we have optimized.
And now we have used different deep learning models to improve that. And of course with deep learning, we are increasing the accuracy and the efficiency of the visual inspection process and thereby increasing quality and reducing the amount of good product going to waste due to cartilages being wrongly identified as defect. So we save products and we optimize our products that way in a more efficient way. And we also produce less waste of good cartilages going to waste. But most importantly, what we get out of this project is the necessary capability of how to do machine learning in very regulated spaces. For example, like manufacturing or pharma.