How a new AI value framework and stakeholder focus keep Zoetis ahead of the pack

Most AI investment strategies fail not because the tool or platform underperforms, but because organizations didn’t clearly define what success looks like before they started building.

Through a new approach to measuring value, Zoetis chief digital and technology officer Keith Sarbaugh and his business partners have leveraged a value-driven framework to scale AI solutions across research, manufacturing, and customer experience. And they measure every investment against goals before, during, and after the deployment.

In addition, his team rolled out a model-agnostic gen AI platform now used by nearly 95% of employees, which turned early experimentation into enterprise-wide adoption. Sarbaugh’s current focus now is partnering with Zoetis’ CHRO to advance the $10 billion global company’s capabilities in managing organizational AI adoption.

How are you integrating AI into your growth plans at Zoetis?

We have an umbrella program we call AI@Zoetis, where we unify our AI work under an enterprise purview, which spans research and development, manufacturing, commercial operations, customer and colleague experience, and other business functions. We manage AI collectively to enable grassroots innovation.

For example, we made our generative AI platform available to everyone, so as many people as possible can experiment and innovate. Our colleagues have access to 10 different LLMs, and we’ve seen over 95% adoption rate among our user community, and more than 11,000 colleague-built agents.

One popular AI use case is helping colleagues build their own development plans. The agent guides a colleague through a conversational, coach-like experience to map out their career aspirations against Zoetis’ competency framework, which was key to its wide adoption. This idea came from people not in HR, illustrating the point that some of the best use cases come from our broader employee base.

What’s an AI use case that directly impacts customers?

We have millions of customer interactions across our channels. Our sales force is out talking to them, who are also in our digital platforms, and we receive thousands of calls through customer service. We’ve been using the industry standard Net Promoter Score (NPS) to measure customer loyalty and satisfaction, but NPS is a measure that can take longer to generate. Zoetis has accelerated our awareness of customer feedback into real-time listening, using AI to understand these customer interactions in a holistic way, and at a scale we couldn’t achieve before. NPS still matters for tracking long-term trends and maintaining a consistent industry benchmark. We simply use AI to listen, learn, and act quicker.

Our AI customer experience platform also lets us look across all our customer touchpoints like calls, emails, and websites in real time, immediately identify issues and insights, and then be smart about how we address them. We now have more than 10 times the feedback signals we had in the past. We see trends sooner and act faster, and as humans, we can’t do that without AI.

How are you deciding where to make your AI investments?

We use different lenses. One is AI for the masses, which is our generative AI platform for colleagues; second is our middle lens, where we drive value in a particular function; and the third is enterprise-wide transformation, the big bets that’ll fundamentally change our business. We don’t do many, but we do them in a smart way.

With the transformative investments, we focused on both our commercial business and R&D, which we knew had the highest probability of serious returns. We started with seven golden use cases and knew that if we hit on two or three, it would be a big deal. Of the original seven use cases, six of them exceeded their value target and went from PoC to scale.

Since those earlier days, we’ve broadened to include manufacturing and supply chain, and our enabling functions.

Overall, we didn’t go out of the gate looking for productivity gains. We thought about business transformation right from the start. Today, we’re scaling up those first-mover investments and continue to leverage our value-driven framework to identify more use cases.

Are you creating new value frameworks so you and the rest of the ELT are unified in your investment strategy?

We developed a business value realization framework, which isn’t as sophisticated as it sounds. Before we make a tech investment, we ask what category of benefit we expect to receive, whether it’s revenue uplift, cost reduction, productivity, or whatever. We predict what success will look like and how we’ll measure it. 

Because with AI, we’re trying to move fast. We put a value case together at this early stage and do a PoC, and if it hits its target, we update the value case and decide whether to scale. A key element of the framework is real-time measurement. Are we seeing what we wanted, and if not, how do we pivot for more value?

The speed of iteration and scaling decisions make AI investments unique, so we can’t use our traditional value frameworks for digital investments, generally. Measuring outcomes post-implementation has become even more important.

How is your CHRO partnership impacting AI value?

Our CHRO and I partner closely to ensure enterprise enablement. When driving new ways of working that impact your workforce, you need a comprehensive approach, clear communications, and genuine buy-in. Colleagues adopt faster when they help shape the change. We’re prioritizing our workforce strategy, understanding what AI means for jobs at Zoetis, identifying skills that matter most, and building a plan to upskill people.

Another focus area is organizational change management (OCM). We reviewed our first AI investments to learn from our mistakes, and one consistent theme was that we shortchanged OCM. We thought naively that what we build will be so compelling, adoption will just come. But we didn’t do the right communication and stakeholder management. We recognize our need to develop OCM as a core competency, so our CHRO and I are building an enterprise playbook for AI change.

What’s your pragmatic advice to other CIOs when it comes to OCM?

When you’re wrapped up in a change program, you know what’s coming, but no one else does. When you impact your entire workforce, you need a smart approach to stakeholder management and communications. Involving colleagues in the creation of something new will aid in adoption. Have a deliberate and intentional communications plan and cadence, and have the discipline and objectivity to measure and learn. Our first tries weren’t perfect, but we listened to feedback and pivoted, and those pivots drove further commitment.

Has your communication at the board level changed?

When I talk to my peers about their board conversations, half focus on risk and compliance, and the other half talk about transformation and revenue generation. I’m fortunate that our board cares about both and has great energy around generating revenue, and how AI will give us a competitive advantage. By managing risk and compliance, we can spend our time focusing on potential drug candidates and getting to market quicker. Our board conversation is both about enablement and compliance.

What advice would you give to tomorrow’s CIOs?

The role is now about orchestration, understanding the business, and realizing value from technology investments. If you want to work with the best technology and bleeding-edge innovation, you’ll get some of that as a CIO, but the focus is broader, centered much more on processes and complex business problems than ever.

My advice is if you love working with technology, you’ll get that as a CIO. If you love delivering meaningful outcomes for the business and the customers you serve, it’s a truly rewarding role, and you’ll be an even more successful CIO.