With over 65 million daily customers and operations in more than 100 countries, McDonald’s runs on more than just burgers — it runs on data. As the company pilots AI-powered drive-thrus, chatbot-enabled kitchen tools, and text-to-SQL analytics, the need for trusted, well-governed data has never been greater.

In this second installment of a two-part interview, John Tucker, Director of Enterprise Data Governance at McDonald’s, speaks with Amy Kyleen Lute of Acceldata about what it means to lead governance from the business side while working hand-in-hand with IT, data product teams, and GenAI innovators.

Tucker explores how McDonald’s is aligning business and technical stakeholders, embedding AI governance early, and evolving data products beyond single use cases — transforming governance from a compliance necessity into a driver of innovation and value.

Edited Excerpts

Q: What does working closely with business data consumers look like in your role?

Governance can sit between the business or within IT. I’ve done both. When I first started my journey in governance, my department was based in IT. We were in financial services, and being in IT positioned us well to align with the various business functions.

I now sit on the business side but still work very closely with my IT counterparts, our data product teams, the data producers, and also the consumer side — our insights teams, our data science teams, and our GenAI teams. We work closely together. I wouldn’t necessarily call myself a middleman, but it is very much a hybrid role.

You need to have a bit of technical knowledge but also be educated on what the business is trying to achieve with the data. That way, you can influence how that data needs to be positioned from a technical lens as well. It really helped me.

Being on the business side gives you that understanding of where we’re trying to go as a company. When you’re on the IT side, it’s more about: how do I keep the lights on? How do I deliver what’s needed?

When you’re on the business side, you’re looking at: what’s the actual outcome we’re trying to achieve? And then working backward — what data do we need to support that hypothesis or reach that conclusion?

Q: More enterprises are adopting the title of “Data Product Manager.” Do you think this reflects a growing focus on applying product management principles to bridge business needs and technical solutions?

We’re doing the same thing even at McDonald’s. We have a group of business data product owners, and then we have our technical data product owners. They’re starting to work well together not only to establish the purpose of the product and how it should be used and leveraged but also to understand that while a product may be created for one use case, it can deliver value across multiple use cases throughout the organization.

It’s really good to see the business and technology teams coming together to define the scope of work and explore how to maximize value. They’re also bringing in teams like mine to make sure we have the right controls, policies, and standards in place as those products get rolled out.

Q: Data governance often focuses on compliance and efficiency in many organizations. With the rise of AI and other technologies, do you see an opportunity for data teams to go beyond that, to help drive revenue and enhance the customer experience?

I do. In the last couple of years, AI, especially GenAI, has become a major topic. There’s been a lot of progress around how it can create operational efficiencies in day-to-day tasks, and also how we can use data, machine learning models, and AI to drive better decision-making.

At McDonald’s, we’re thinking about how to improve the customer journey, how to make things better for our crew members in the restaurants, and also how to enhance experiences for our corporate teams.

There are several ways we’re exploring this. On the customer-facing side, for example, we’ve done POCs around automated drive-throughs and order-taking to improve order accuracy. In the restaurant, we’ve introduced different types of chatbots to support management tasks — things like processing time-off requests or helping staff understand how to prepare a menu item. Having a visual reference, like a digital menu card, is a lot more helpful than flipping through a manual or searching for materials.

We’re also experimenting with smaller, practical use cases across restaurants and customer touchpoints.

On the corporate side, we’re focused on accelerating productivity with tools like text-to-SQL, making it easier for both analytical and business teams to get to insights faster. We’re also looking at how AI can help enforce standards, so teams get the right information and can trust it.

A lot of these use cases are still in progress. They haven’t all fully materialized yet, but we have quite a few in the hopper. We’re in the process of testing and proving where the real value lies.

Q: Underlying all of this is the need for trusted, reliable data to power AI, right?

We have a robust framework. We’ve established an AI governance working group, a combination of our legal team, our data privacy team, cybersecurity, data architecture, and a few key business functions focused on the customer and crew journey. On the business side, we also look at how we’re using the AI model — what its intended purpose is, and what kind of value it brings. That could be operational efficiency, or it could be business value through marketing use cases and similar efforts.

Governance actually has a seat at the table. We ask: What is the data being used? Can we trust that data? Where did it come from? How does the model work internally? What exactly is it doing? That way, if a regulator ever comes knocking, we can clearly explain: Here’s what the model does, here’s what it doesn’t do, here’s how the data is being used, who’s using it, how often it’s tested, and what the quality of that data looks like.

We’ve definitely built a solid framework over the last year and a half. The goal is to make sure our corporate teams and staff can trust the data. And if we’re ever asked why we performed a certain activity or used a certain model, we can confidently articulate the work we’ve done to put proper controls and trust in place.

Q: It’s exciting to see a company with McDonald’s scale implementing these ideas. Do you think this is how AI starts to feel more real, moving from abstract conversations to practical, everyday experiences?

As multiple companies go through this journey, we’re all starting to see the patterns emerge. You hear about it from Gartner and Forrester reports saying this is where corporate teams are starting to actually see AI driving value. Or, in five years, we might look back and realize the hype was there, but it didn’t fully materialize. We’re now really starting to think through the use cases and making sure there’s either clear value or risk avoidance by having that working group look at all those various use cases.

CDO Magazine appreciates John Tucker for sharing his insights with our global community.