Zoetis Leads the Way in AI-Powered Veterinary Diagnostic Solutions

When we think of AI, we tend to think of companies on the leading edge of technology. Most of us probably don’t think about AI and animal care. But, according to a recent article and video published in CDO Magazine, “advancements in machine learning and more sophisticated technologies are increasingly influencing how we monitor, interact with and care for our animals.”

Ben Blanquera, VP of Technology and Sustainability at Rackspace Technology and Global Editorial Board Member at CDO Magazine, explored the innovative use of AI in animal care with Zoetis, a leader in the field. He interviewed Krishna Cheriath, Zoetis' Chief Data and Analytics Officer and Head of AI, to gain insights into their groundbreaking practices. The insightful discussion has been shared through both an article and a video.

Cheriath discussed how and why Zoetis deployed AI to improve pet and animal care. He shared the challenges of overseeing an expansive digital infrastructure and optimizing AI to drive innovations in animal health care — including the application of AI in drug discovery and clinical development.

On being creative with AI innovation

Zoetis is not new to using leading-edge technologies. It’s been applying AI and data analytics to drive groundbreaking scientific innovation for optimal health outcomes for years. Today, the company is dedicated to harnessing technology to develop revolutionary solutions, like medicines and vaccines, to serve a wide range of animals — from pets to livestock.

In his role at Zoetis, Cheriath manages a global team responsible for multiple departments along the company’s biopharma value chain, including AI, analytics, intelligent automation, digital solutions and data strategy.

Cheriath’s innovative approach to using AI has included creating cross-functional “explorer” teams, inspired by his favorite Netflix series, Drive to Survive. The teams were tasked with discovering both external and internal opportunities for AI use cases. Outside of the company, they engaged with big tech venture capital, startups and industry thought leaders to understand the focus, trends and changes shaping AI innovation.

On identifying priority AI use cases

From there, the teams identified seven use case for the company and allocated Series A funds for promising proof of concepts that align with its priority use cases, setting clear exit criteria based on technology and value feasibility. “Like traditional venture capital investments, we expect some proofs of concept to succeed and some to fail,” said Cheriath. “The successful ones are developed further to scale with a strong focus on adoption and change management.”

Also important to achieving innovation is the human element of AI, according to Cheriath. “If AI tools aren't embraced, trusted or integrated into workflows effectively, human potential remains unrealized. We must proactively design strategies to gain human buy-in, foster trust and provide training.”

Cheriath call himself a "boundary spanner," because he blends his skills as a business strategist, digital and AI strategist, technologist and communicator to achieve the results he envisions. “This multifaceted approach helps to build trust among team members and facilitate effective collaboration,” he said.

Learn more in the article and video

Read the article and watch the video to learn more from Cheriath, including:

• How he applied a venture capital model to accelerate AI innovation
• Why he prioritizes both human ops and AI ops to effectively scale solutions
• His "think big, start small, run fast and scale well" approach to AI
• Why he prompts his business leaders to complete the sentence, "I wish I knew” or “I wish I could"
• What role company culture plays is driving AI innovation and collaboration
• His top five playbook takeaways for achieving excellence in AI

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