Transforming enterprise workflows: How IBM is unlocking AI's potential

Learn how IBM deployed and integrated AI tools in the ultimate enterprise environment.

January 15, 2026

Transforming enterprise workflows: How IBM is unlocking AI's potential

Learn how IBM deployed and integrated AI tools in the ultimate enterprise environment.

In this episode of Leaders of Code, Stack Overflow Chief of Product and Technology Jody Bailey chats with Matt Lyteson, CIO of Technology Platform Transformation at IBM, about the processes and challenges of adopting AI within an enterprise environment. They explore IBM's strategic approach to integrating AI into workflows and emphasize the importance of fostering the right behaviors among employees, particularly regarding automation and AI assistance.

The discussion also:

• Explores what it means for a company like IBM to truly embrace AI, with Lyteson sharing strategies for integrating AI into every workflow to maximize productivity across the organization.
• Highlights key challenges like data privacy, security risks, and the critical need for workforce reskilling in an AI-enabled world.

Notes

• Connect with Matt Lyteson on LinkedIn.

TRANSCRIPT:

Eira May:

Hi, everyone. Welcome to the Stack Overflow Podcast. Today we have another episode of Leaders of Code, where we're chatting with tech leaders about the work they do, how they build great teams, and the challenges they're facing. My name is Ira May. I am the B2B editor here, at Stack Overflow. And I'm joined today by Jody Bailey, who is our Chief of Product and Technology, as well as Matt Lyteson, who is the CIO of Technology Platform Transformation at IBM. How are you guys doing today?

Jody Bailey:

Great. Thank you.

Matt Lyteson:

I'm doing fantastic. Thanks for having me today.

Eira May:

Oh yeah, our pleasure. So glad you could join us. So I wanted to just kick us off by posing a question to Matt. So as IBM's CIO, I imagine you're at the center of the company's internal AI projects, AI transformation. So I'm wondering what does it mean for a company like IBM to embrace AI, to lean into AI? And what is that looking like on a day-to-day basis for your teams?

Matt Lyteson:

Well, thanks, Eira. You started off with such an easy question here. As you can imagine, we've got a lot of AI and technology. Part of our strategy, core to our strategy is hybrid cloud AI, and of course, the emergence of quantum computing. Internally at IBM, where I'm responsible for delivering the solutions and technology platforms that every IBMer can operate on to ultimately be more productive. We try to turn that into how do we use our products, in some cases before they get into the hands of our clients.

But I would say more importantly, how are we looking to inject AI into every single workflow, into every single task that IBMers are participating in, in order to help them be the best at what we need them to do to help the company grow and accelerate.

So I would say that's a high level summary, and I think we're going to get into a lot of the specifics and details around that.

Jody Bailey:

What I like about what you said is it seems like you have an outcome defined, in terms of what you want to do. You want to help everybody do their jobs better and more effectively. I'm curious, what is the desired outcome of implementing AI at IBM?

Matt Lyteson:

Well, I think it all comes down to what I said a few minutes ago. It's how do we have every IBMer be the most productive they can be? And I think you're absolutely right that this means let's focus on the outcomes. Ironically, this is what we CIOs were talking about 10, 15, 20 years ago. I think in some cases we got it right, and other cases not, but what's the value of the technology that you're implementing for the organization? I think AI almost gives us an opportunity to have that conversation much more intentionally than we did before.

So when we think about our internal AI use cases and where we're going to focus on the workflow, we do a couple things that I like to think are interesting and I think resonate with a lot of my peer CIOs that, first of all, we distinguish from what we'll call everyday productivity where we're putting AI into simple tasks, that's how do you and I maybe save 15 minutes from developing a presentation, or how do we summarize email, or how do we read documents faster because we can summarize and use rag patterns in order to get intelligence much faster. That's helping all of us make better decisions, operate faster, take some of the mundanity out of our work and do more exciting things, but that's on one side of the equation.

On the other side of the equation, here, we're thinking about how do we put AI into the end-to-end workflow? And when I think about it through that lens, I can start to talk about my outcomes in terms of, are we growing revenue faster? If we're focused on the operations functions, are we getting better at operations? Which means am I doing a workflow faster? Am I producing the output of that workflow at a lower per unit cost? Which helps me to think about how my company can scale a little bit differently. And then there's a third bucket we think about is, how am I using AI to reduce my risk posture or manage risk overall effectively?

We put in those three buckets, we can have very focused conversations as we're running experiments, as we're building AI solutions in the workflow, that's a little bit different from, I'm going to save everyone on the team 5 to 10 minutes because we don't need someone taking notes in the Teams meeting that we're having, for example.

Jody Bailey:

I love that. I'm curious, are there, in any of those three categories, any specific examples that you'd want to call out where you feel like you and the team got it right and had a pretty significant impact?

Matt Lyteson:

I think there's a couple examples. I'll maybe list two here. One is an example, and I like to think in we, as CIOs, and having engineers on our team, how do we lead by example? I think we are seeing a shift of how do we get this into every single business function.

Internally, we did this with what we call Ask IT, and we really took out some of our level one, level two support, had AI in front of that, did that in about 100 days. This is about a year and a half, almost two years ago, and said, "What if every single one of our 280,000 IBM employees asking for IT support, how do they get that first through an AI-based approach?" And then AI, on the backend for those things where it's not able to handle on its own, multilingual translation, and now my IT support agents are able to handle much more complicated tasks much more easily and actually get better satisfaction from the role because they aren't trying to tell you, Jody, how you can reset your password where we gave you clear instructions and there's an auto password reset. I think that's frustrating to people who love to help people with their technological needs.

A more recent use case that I'm super excited about, if you think about this, there are a lot of steps that's required when we're thinking about AI from an enterprise perspective that's maybe a little bit different than if we were running a startup, I need to make sure that I'm focused AI for the right things within my operation, that I can trust that, that I have the right human in the loop, that I am respecting data privacy, cybersecurity. And typically, this comes across and touches a number of different teams when you're contemplating or considering the type of use case. So we've got an AI ethics review, we've got an office of responsible use of technology, we've got our CIO teams that own the platforms. We really started to take a look at that, and on the mantra that we've had on our overall evolution here in the past couple years at IBM, eliminate, simplify, automate, and some of us will even add in AIFI at the end of that as a fourth category.

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