Home Our Insights Articles Animal Health Has Spent Years Catching Up to Pharma. AI Could Change the Story.

Animal Health Has Spent Years Catching Up to Pharma. AI Could Change the Story.

Michał Osuch BU Head, Animal Health Commercial
8 min read
07.09.2026
Table of contents
  1. 1. The Gap Is Real And It Exists for Good Reasons
  2. 2. Following Pharma Has Often, but not Always, Been the Right Strategy
  3. 3. AI Levels the Playing Field
  4. 4. Animal Health May Have More Advantages Than It Realizes
  5. 5. AI Doesn’t Replace Strong Foundations
  6. 6. AI Amplifies the Existing Expertise
  7. 7. The Opportunity Won’t Stay Open Forever
  8. 8. A Chance to Rewrite the Story

For as long as I’ve worked with animal health organizations, I’ve heard the same comparison repeated:

“Animal health is five to ten years behind human pharma.”

There is truth to that statement. Human pharma has traditionally had greater financial resources, larger IT organizations, and more capacity to invest in digital transformation. Whether we’re talking about CRM, commercial analytics, manufacturing technologies, or data platforms, animal health has often followed paths that were established years earlier by its larger counterpart.

But I don’t see that as a weakness. In many ways, it has simply been the reality of operating in a smaller industry with different economics and different priorities. When investment budgets are tighter, every technology decision carries more weight. Organizations naturally become more selective about where and when they invest.

What makes today’s situation different is that AI is already delivering practical value across the industry.

Veterinary clinics are beginning to use AI to automate clinical documentation, helping clinicians spend less time on administrative work and more time with patients. Livestock producers are applying computer vision and behavioral analytics to monitor herd movement, feeding patterns, and early indicators of disease. Manufacturers are exploring predictive maintenance, demand forecasting, and AI-powered quality inspection to improve operational performance.

These aren’t futuristic concepts. They’re practical use cases that organizations can implement today. And that raises an interesting question: could AI be the first major technology wave where animal health doesn’t have to play catch-up?

The Gap Is Real And It Exists for Good Reasons

The technology gap between animal health and human pharma didn’t emerge because companies lacked ambition or vision. It developed because the industries operate under very different conditions.

The first factor is scale. Human pharmaceutical companies have historically had significantly larger budgets dedicated to digital transformation. That makes it easier to modernize infrastructure, replace legacy systems, and invest in enterprise-wide technology programs.

Second, many animal health organizations continue to deal with fragmented systems and organizational silos. Commercial, manufacturing, regulatory, and operational data often reside in separate environments, making it difficult to build a unified view of the business.

Finally, there is the challenge of standardization. Different regions, acquisitions, and business units frequently operate with different processes and technologies. Introducing enterprise-wide solutions into that environment is naturally more complex.

None of these challenges are unique to animal health. But together they explain why the industry has historically been behind human pharma when it comes to digital transformation.

Following Pharma Has Often, but not Always, Been the Right Strategy

To further explain the current state, we have to recognize that there’s tangible benefit to staying a step behind. The phrase “playing catch-up” often carries a negative connotation. I think that’s unfair. Being a fast follower has frequently been the most rational business decision.

Human pharma has invested billions of dollars in digital transformation over the last two decades. Along the way, it has learned valuable lessons about CRM implementation, commercial excellence, omnichannel engagement, advanced analytics, and manufacturing optimization. Not every investment produced the expected return.

Animal health has benefited from being able to observe those successes, and shortcomings, before committing its own resources. It’s good to learn from your own mistakes, it’s even better to learn from somebody else’s.

But there is another side to this dynamic, particularly for companies operating across both human and animal health. In some cases, solutions developed for human pharma were simply rolled out to animal health, even when they did not fully fit its business model. Shared procurement could make platforms such as CRM more accessible and cost-effective for animal health, but the accompanying implementation blueprint was often designed around the needs of human pharma. That could limit configuration options and force animal health teams to adapt their processes to systems built for a different commercial reality. The same pattern could apply to analytics, loyalty solutions, and other digital capabilities.

That has allowed organizations to focus on proven approaches rather than becoming early adopters of every new technology trend. AI, however, changes the dynamic. Unlike previous waves of digital transformation, it isn’t arriving in human pharma first and animal health years later.

AI Levels the Playing Field

Today’s AI technologies are becoming available to organizations of every size at essentially the same time. Whether you’re a global pharmaceutical company or a regional animal health manufacturer, you have access to the same foundation models, cloud AI services, copilots, and development platforms.

That doesn’t mean everyone will generate the same results. But it does mean everyone starts from the same technological starting line. The competitive advantage is shifting away from simply having access to the modern technology, and toward applying it effectively. We’re already seeing this happen.

Veterinary clinics are adopting AI assistants that automatically generate consultation notes and clinical documentation, reducing administrative burden without changing the clinician’s role.

Livestock producers are combining cameras, sensors, and AI models to monitor herd behavior, detect changes in feeding patterns, and identify early signs of disease before symptoms become obvious.

Manufacturers are exploring predictive maintenance that identifies equipment failures before they occur, computer vision systems that improve production quality, and AI-powered forecasting that helps align production with changing market demand.

None of these organizations needed to develop their own large language model. The technology already exists. The differentiator is how effectively it is integrated into everyday business operations.

Animal Health May Have More Advantages Than It Realizes

One assumption I encounter regularly is that larger organizations are automatically better positioned to implement AI. In practice, that isn’t always true. Actually, in many cases it’s exactly opposite.

Animal health organizations possess several characteristics that may actually help them move faster.

First, they generally operate under fewer regulatory constraints than human pharma. While governance remains essential, organizations often have greater flexibility to experiment, validate ideas, and refine solutions without navigating the same level of regulatory complexity.

Second, decision-making is often considerably faster. Many organizations don’t need to move through multiple global committees before launching a pilot or expanding a successful initiative. Teams can move from idea to implementation in weeks rather than months.

Finally, there is often a greater willingness to experiment. Rather than attempting a massive enterprise-wide AI transformation, organizations can focus on narrowly defined problems that deliver measurable business value.

  • An AI assistant that saves veterinarians several hours every week.
  • A predictive maintenance model that prevents costly production downtime.
  • A demand forecasting model that improves inventory planning during seasonal disease outbreaks.

These projects are relatively easy to evaluate, relatively easy to scale, and often create the confidence needed for broader AI adoption. Ironically, some of the characteristics that historically limited large-scale technology investments may now become competitive advantages.

AI Doesn’t Replace Strong Foundations

It’s tempting to think AI can compensate for years of fragmented systems and inconsistent data. It can’t. If anything, AI makes those problems more visible.

If customer master data is incomplete, AI-generated recommendations become less reliable. If production data remains fragmented across disconnected systems, predictive models become less accurate. If governance is weak, users quickly lose confidence in AI-generated insights.

That’s why organizations that invested in cloud infrastructure, integrated data platforms, and governance over the last several years are now in a much stronger position to operationalize AI. The technology may be new. The fundamentals are not.

Organizations still need trusted data, clear ownership, modern infrastructure, and people who are willing to incorporate AI into the way they work. The most successful organizations will make AI a part of their operating model.

AI Amplifies the Existing Expertise

One characteristic I’ve always admired about the animal health industry is that people wear many hats.

Veterinarians split their time between patient care and administrative work. Manufacturing specialists are responsible for production, quality, and continuous improvement. Commercial teams manage broad territories while trying to stay close to customers whose needs evolve constantly. Unlike some larger industries, there’s rarely an abundance of people or time.

That’s what makes animal health a great fit for the use of AI. It’s enabling the various specialists to do more, allowing the expertise organizations already have to reach further.

A veterinarian still diagnoses the patient. AI helps prepare the documentation. A production engineer still decides when maintenance should be performed. AI helps identify patterns that suggest an impending failure. A commercial leader still defines strategy. AI helps uncover trends, segment customers more effectively, and identify opportunities hidden within large volumes of data.

In every case, the expert remains at the center of the decision. AI simply removes some of the friction that prevents them from spending more of their time on work that actually requires human judgment. That distinction matters because organizations that view AI as a decision-support capability, not simply a cost-cutting tool, are far more likely to achieve lasting business value.

The Opportunity Won’t Stay Open Forever

The reality is that not every organization starts from the same level of preparedness. Some have already invested in cloud platforms, integrated data environments, governance frameworks, and modern analytics capabilities. They’re well positioned to scale AI over the coming years.

Others are still working through legacy infrastructure, disconnected systems, and inconsistent data. That doesn’t mean they’ve missed the opportunity. According to McKinsey, most organizations are still at a relatively early stage of impactful AI adoption. But the window won’t remain open indefinitely.

As more organizations successfully operationalize AI, the competitive advantage will increasingly belong to those that already have the right foundations in place.

A Chance to Rewrite the Story

For decades, animal health has been viewed as the industry that follows human pharma. I believe AI gives us an opportunity to change that narrative.

Even without a massive growth in budgets resources, there’s a possibility of closing the gap. Because success in AI will depend less on who invested the most yesterday and more on who can combine strong data foundations with organizational agility today.

That is a competition animal health organizations are well positioned to win. The opportunity is real. The question is how many organizations will seize it before the gap begins to widen again.

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