Liberty Mutual’s CIO tackles the complexity of digital transformation

As CIO of Liberty Mutual, James McGlennon is on a mission to transform the way IT and technology drive business success.

Leading a digital transformation for a global insurer that has been in business since 1912 and now employs more than 50,000 people worldwide is complex. Fortunately, McGlennon has a little help.

“We are fortunate to have 5,000 technologists and an entire army of software engineers to help us tackle some of the biggest challenges we face,” McGlennon said. “But that said, the challenges are many and varied.”

James McGlennon

Taking Liberty Mutual to the cloud is no small feat, and neither is developing a digital ecosystem.

Here, 2022 MIT Sloan CIO Leadership Award finalist McGlennon discusses some of his most pressing technology priorities, talks about Liberty Mutual’s digital transformation and AI explorations, and why giving employees flexibility is important. a key talent management strategy.

Dealing with complexity in digital transformation

What are some of your biggest technical challenges right now?

James McGlennon: The first is about technology debt. Many of our solutions are 20 or 30 years old, or even more. We’re on our way to the cloud, and a big part of that is rebuilding new apps in a cloud-native way. In many cases, we are looking for ways to run old workloads on new platforms. It is a difficult challenge.

The second area we spend a lot of time on these days is what I call data management – ​​how we prepare data for use in our machine learning models and our AI models. We have a lot of data going back 20 or 30 years — sometimes even more — and it’s not all in the same form.

The third challenge we have technologically is to ensure that everything can be enabled by the API. As we think about how we can participate in global ecosystems with new types of partnerships and partners, it’s very important that we can connect to their world and they can connect to ours, and that’s becoming a kind of homogeneous interaction model.

Including ethical AI

How did you start deploy AI and how has it changed your business?

McGlennon: We started a journey into AI maybe four or five years ago, really doubling down on our investments in machine learning and exploring how we could leverage it. We have also partnered with organizations like MIT, where we conduct research focused on questions such as: Is it possible to create synthetic data to be able to test our hypotheses and models? Are we able to validate why a model gave a particular recommendation? In this new era of AI and machine learning, we will need to be able to explain how models arrived at their conclusions. And regulators and people will demand it as we move forward.

We have a lot of data going back 20 or 30 years — sometimes even more — and it’s not all in the same form.

James McGlennonCIO, Liberty Mutual

We have hundreds of machine learning models in various stages of operation in Liberty Mutual. There’s hardly a part of our business where we’re not deploying new machine learning models.

For example, we have AI in our operations for our technology infrastructure that uses machine learning models that can predict when we’re going to have a problem. This way we can resolve this issue with the platform or with the solution before it causes a breakdown or gives a bad experience to our customers.

We have also deployed AI and machine learning in computer vision technology. If one of our customers has a car accident, they can use their phone to take photos of the car damage and upload them with us. We can feed these images into a machine learning model that we have trained on millions of photographs of damaged cars. This can help us detect the extent of the damage and make an initial assessment of the repair. In some cases, this can tell us immediately if the car is a total loss, so that we can resolve the claim as quickly as possible.

Another thing that comes to mind is that we use machine learning engine technology in our claims handling engines. If customers call us, we can intercept them with a bot and perhaps give them the answer much faster than if they had to wait for a human.

We are trying to understand how we can build a set of technologies that will allow us, if necessary, to recycle models. Sometimes, if you deploy a model in production, the data behind it can change or drift over time. We need to be able to continuously track and retrain the model as needed. This means that we invest heavily in new technologies to automatically deploy machine learning models through API connections that allow us to do so much faster. Speed ​​is therefore essential, as it allows models to be recycled throughout the value chain.

Responding to employees’ need for flexibility

What changes has the pandemic brought to Liberty Mutual, and which do you think will exist for the foreseeable future?

McGlennon: We are focusing on how to make sure that [employees] Stay logged in. It was a big challenge for us, given that we were all virtual. We needed to introduce new kinds of formalized approaches to checking in with people to make sure they felt part of the team and not isolated.

In today’s world, we will need to provide more flexibility to our employees than ever before. We take the initiative to ensure the safety of our employees and we continue to keep this at the center of what we do.

We have started a journey towards hybrid working. It will probably take a little longer for everyone to feel comfortable coming into the office a little more often, but our main goal is to make sure people have the flexibility they need. We have been very successful in doing the things we need to do during the pandemic. We won’t bring people back just to check the “I was in the office” box. It’s really going to be focused on the activities that people do and the need to come together for specific activities or specific tasks that are better in person.

Sharon D. Cole