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Depending on who you speak to, AI is either gonna fix every problem in the business by next Tuesday or take everybody’s jobs before lunch. The truth is a little less dramatic and a lot more useful. AI is already changing how work gets done across our industry. Some roles will reduce, others will change, and entirely new areas of expertise will emerge.

But the businesses that handle this well will not be the ones that chase every shiny tool or refuse to engage with any of it. They’ll be the ones that understand where AI adds value, where humans’ judgment remains essential, and how to build a workforce capable of using both.

Welcome to The Animal Health Show by Essate. I’m your host, Shannon Wood, and this is where we talk about the latest trends shaping the animal health industry. We’ll be sitting down with leading voices from across the sector, sharing practical insights to help you grow your animal health business, build a stronger team, or land your dream role.

If you’re looking for your next dream job, or you’re ready to hire your next standout team member, reach out to us at Essate Expert Recruitment Solutions. You’ll find our contact details in the show notes for today’s episode. All right, let’s talk animal health

Before we get into it, if you’re planning your workforce for 2027, now is the time to act. In the last episode, I explained why October and November are the best hiring months and why waiting until January can put you months behind.

Whether you need a future-focused leader, a technical specialist, or someone who can bring stronger commercial capabilities into the business, get in touch with S8 Recruitment Solutions. Our contact details are in the show notes or Google S8 Recruitment Solutions and give me a call.

We’ll help you work out what capability you actually need and find the right person to deliver it.

There’s a phrase I hear constantly at the moment. We should be using AI for that. Sometimes it is said with genuine excitement, sometimes it’s said with a mild sense of panic, as if everyone else received a handbook that explains exactly how to transform their businesses

and get lost in their posts, and sometimes it’s followed by a very quickly, “Do we even need that role anymore?” Across animal health and animal nutrition, I am seeing a real mixed bag. Now, some businesses are embracing AI enthusiastically and testing how it can improve everything from administration and customer contact to research, diagnostics, and on-farm decision-making.

We see veterinary businesses automate phone answering, appointment handling, and routine client communication. At the other end, there are leaders who remain highly cautious, who are hoping this whole thing settles down if they leave it alone for long enough. As an industry, I think we’re moving more cautiously than some others, which is not surprising.

Animal health is technical, regulated, and quite relationship-driven. Decisions can affect animal welfare, food production, biosecurity, commercial outcomes, and public confidence.

The computer said so is not a particular reassuring explanation when the stakes are high. But cautious cannot mean passive.

AI is not sitting outside the industry, waiting for everyone to vote on whether it should come in.

It’s already here, and it goes well beyond generative tool writing emails or summarizing meeting notes.

Veterinary imaging is one clear example. Machine learning systems are being developed to analyze images and assist with recognizing patterns that may support diagnosises. In livestock, researchers are combining cameras, microphones, sensors, GPS machine learning in to monitor behaving, detect changes, and give producers early information about health and wealth.

Now, CSIRO has reported work using computer vision to monitor cattle behavior and animal welfare, and the most recent research in using AI and computer vision to estimate sheep live weight and fleece weight without the traditional manual process. Meat & Livestock, or MLA, has also supported the projects involving AI-assisted cattle eye disease and AI parasite management.

Those examples matter because they show what this technology is particularly good at. It can process huge amounts of information, identify patterns, and automate repetitive analysis far faster than a human could.

In research and development, that may change how teams work with data assets, identify promising pathways, and evaluate information.

So in formulation and nutrition, AI can support modeling, optimization, and faster analysis of the different inputs. So look, in technical and compliance functions, it can assist with document review, monitoring, data handling, and the first pass of routine work. In veterinary practices, it can reduce administration tasks, lead through transcription, scheduling, record summaries, and routine communication.

In production environments, it can help monitor animals consistently and scale that would be difficult for a person alone.

That doesn’t mean some work will be reduced. We should be honest about that. If a task can be automated, a business is unlikely to keep paying someone to do it manually forever because it feels traditional.

Some roles that are heavily administrative or repetitive may become smaller. One person using the right technology may handle that work that previously required several bus- people. Businesses will reduce headcount in some areas. Pretending otherwise does not help employees, and it certainly does not help employers prepare.

But a task disappearing is not always the same as a professional disappearing or a profession. Most jobs are bundled of activities.

AI take over some of the lower value or repetitive parts while changing that human is there to do. AI technical employee who previously spent hours searching, compiling, or reformatting information may spend more time on interpreting it, challenging it, and explaining what it means to a customer or leaders. A veterinarian may have better tools supporting image analysis or record keeping, but the client still needs someone who can examine the animal, understand the broader clinical picture, explain the options, and take responsibility for the decision.

A commercial leader may have received better forecasting and customer data, but it still needs to decide what to do with it and bring people with them.

This is where I think the conversation becomes much more interesting than simply asking Which jobs will AI replace? The better question is: what will people be uniquely valuable for?

In the industry, expertise and relationships will become more important, not less. Now, people search online or ask AI tool about their pet symptoms before calling a clinic. A producer may have a technology identifying a possible issue within a flock or herd. But when someone is making a serious decision about treatment, welfare, an outbreak, or putting down an animal, they do not want a generic answer spat out by a machine.

They want a capable human being who can look them in the eye, understand the context, and help make a difficult decision.

Now, the Australian Veterinary Association’s guide on telemedicine makes a similar practical point. Technology can improve access and delivery, but there are circumstances where a physical examination and genuine veterinarian-client-patient relationship remain important.

AI can support professional judgment. It does not automatically replace accountability, empathy, or the ability to notice that situation in front of you does not fit neatly into data, right?

The same thing applies in recruitment. There will absolutely be more automation. AI can help search information, identify patterns, draft material, and reduce administration.

Candidates are already using it to write applications and prepare for interviews, and employers are using it in a different parts of the hiring process. But many of the senior leaders I deal with do not want to send an AI-generated job brief to an AI recruitment platform and wait for an AI-generated candidate application to pop out at the other end.

At some point, an actual person needs to understand what the business is trying to achieve, right? What sort of leader will succeed there? Why the last appointment didn’t or did not work, and which candidate has the judgment, credibility, and personality to fit the environment.

They wanna speak with someone who’s known in the market and can say, “Yes, I know what you mean.

I know what kind of person you’re looking for. I understand why that matters, and I know where we may find them.” That kind of context is difficult to reduce to a prompt. A job description rarely tells you about the relationship between two leaders, the unspoken dynamics within a team, or why one technically qualified candidate will earn credibility with customers while another will struggle.

Good recruitment involves judgment, trust, and honest conversations. AI can strengthen the process, but handling the whole thing over to a machine is not future-proofing, it’s outsourcing responsibility.

So what should leaders be looking for in their workforce? First, I believe AI capability will become a basic expectation. In a few years, saying that you do not use AI may sound like saying you do not really use computers. You do not need every employee to be a developer or an AI strategist.

You do need people who are curious, adaptable, and capable of using appropriate tools to improve their work.

They need to know how to ask questions, evaluate an output, protect confidential information, and recognize when answers look polished, but are simply wrong. That last point is critical. In technical industries, AI can produce confident nonsense. It can miss context, repeat bias from its data, or give an answer that’s broadly plausible but unsafe in particular situation.

Employees cannot switch off their professional judgment because the output arrived quickly and used impressive language.

The more powerful the tools become, the more valuable genuine expertise become, because somebody still needs to know whether the result makes sense.

Secondly, leaders should place a premium on human capability.

I’m talking about communication, commercial judgment, critical thinking, empathy, leadership, and relationship building. Technical veterinarians who can translate complex information for customers will remain enormously valuable. Commercial leaders who can interpret data, make decisions, and, bring the teams through change will be important.

Specialists who can understand both the science and how technology can be applied. Responsibility will be difficult to find.

The person who can use AI well and build trust is gonna be much, much more valuable than a person who can do only one of those things.

Thirdly, businesses who need to decide what they are going to do with the capacity AI creates.

If a tool saves someone 20% of their week, the conversation should not immediately be, “Great, with 20%,” of that person we can remove. Sometimes reducing headcount will be right commercial decision. In other situations, that time can be redirected into better customer service, deeper technical work, stronger relationships, new product development or growth.

Efficiency is valuable, but efficiency without strategy is simply helps you do more activity. Leaders need to be clear about where we are saving time or should go to better performance lookalikes. This also needs to be handled honestly with employees.

If people believe that every efficiency they identify will be used as evidence that their role should disappear, they will quietly resist the technology or hide how they’re using it.

That is not healthy path. Leaders should explain why the business is adopting AI, which problems it is trying to solve, what will remain human-led, and how employees can develop. There will still be uncomfortable decisions in some organizations, but vague reassurance is not a strategy either. Treat adults like adults.

Tell people what’s changing and give them genuine opportunity to build the capabilities the future business needs.

So for employees my advice is not to wait for employer to hand you a perfect training program. Start learning. Understand the tools relevant to your work, and experiment responsibility.

Look at the repetitive parts of your role and ask whether they can be completed more efficiently. Then look beyond the tools. Build the skills that become more valuable when the information is easier to produce. Judgment, influence, communication, customer understanding, and the ability to solve ambiguous problem.

Your value will not come from being the fastest person at producing a first draft. It will come from knowing what the draft should say, whether it’s accurate, and what needs to happen next. For hiring managers, start asking better questions during recruitment.

Do not simply ask, “Do you use AI?” because almost everyone’s gonna say yes.

Ask the questions, how they have used it to improve an outcome and how they check accuracy, and when they deliberately choose not to use it. Ask what changed in the quality, speed, or commercial result of their work. Their answers will tell you whether they are genuinely capable of simply know a few fashionable phrases.

You should also review the roles you expect to hire over the next few years. Which tasks are likely to be automated? Which decisions require professional accountability? Where will stronger technical capability be needed? And where the relationships remain central. Do you need another person performing the existing process or someone capable of redesigning it?

These are workforce questions, not just technology questions.

Buying a tool is often the easy part. Building a team that can use it intelligently is where the advantage will come from.

One of the reasons we keep our finger on the pulse at s8 is that the answer will keep changing, right? We’re on the ground, we’re speaking with employers and candidates we’re attending industry conferences, listening to the technical conversations, and watching where investment and demand are moving That view matters because workforce planning cannot be done once a year in isolation.

The capability employers need two years ago is not identical, and what they need now, and the gap, will widen as AI becomes more embedded in the industry. I do not pretend to know exactly what every animal health or animal nutrition role will look like in five years from now. Anyone who tells you they know precisely what AI will do is probably trying to sell you something.

What I do know is that resistance alone is not a strategy, and blindly replacing people with technology is not much of a strategy either.

The strongest businesses will combine good technology with talented people who have judgment, expertise, and the ability to build trust. Future-proofing your workforce means understanding which will work in changing and developing the people you already have, and recruiting capability for where the business is going rather than where it’s been.

It means accepting that roles will shrink. Some will be redesigned, and new specialists and leaders will emerge.

Most importantly, r- it means remembering that in an industry built around animal health, welfare, science, and relationships, human responsibility still matters.

If you’re thinking about what your team needs for twenty twenty-seven, now’s the time to have a conversation.

Do you need someone with stronger AI capability? A technical specialist who can interpret emerging tools? A commercial leader who can turn information into decisions? Or are you not quite sure how the role should look yet?

Get in touch with S8 Recruitment Solutions. We can help you think it through, understand what is happening in the market, and find the right person.

Or our contact details are in the show notes, or Google S8 Recruitment Solutions and give me a call. And if you’re working in the industry and wondering what these changes mean for your own career, reach out as well. A confidential conversation can help you understand what employers are looking for and where your experience may fit.

Thanks for listening. If this episode was useful, share it with someone in the industry who’s trying to work out what AI means for their team. I’m Shannon Wood from S8 Expert Recruitment Solutions. Thanks for listening, and I’ll chat to you in the next episode of The Animal Health Show.

Thanks so much for tuning in to today’s episode of The Animal Health Show by S8. If you found the value in this conversation, please share it with your colleague, an industry mate, or on social media so we can keep the discussions moving across the sector. If you’d like to get in touch, all of our details are in the show notes for today’s episode.

I thank you for listening, and I look forward to chatting with you again in the fortnight.

* Transcript created by AI – may contain errors or omissions from original podcast audio