The AI buzz is everywhere. With the global pet care industry growing rapidly and North America market expected to reach USD 230.42 billion by 2034, AI is on pace to be a key contributing factor. However, for enterprises operating in the pet care and animal health industry today, adoption of AI seems to be at a nascent stage.
While 36% of pet food companies actively use AI tools, the majority, 52% have either not implemented the technology or have been uncertain about its benefits, according to a report. Coincidentally, the most successful companies have already started capitalizing on AI to realize operational efficiencies, get real-time customer insights, and implement faster innovation cycles that traditional solutions just can not match. Early adopters of AI have already started seeing advantages compounding.
The question that needs to be asked is how they did it. How did enterprises rise above the market trends to actually see the value from AI? Can enterprises that have not explored AI yet match the pace with competitors who have been on AI transformation journeys? If yes, where do they start?
This is what we will cover in this blog.
Although the potential AI has is immense, many companies in the pet industry are still hesitating to move on implementation. A report by Boston Consulting Group says that only 26% of organizations go beyond AI proofs-of-concept to get real value. Meanwhile, a staggering 74% of organizations are still struggling to scale the impact they can see with AI.
Wondering why? Some of the biggest fears with AI are listed below. See if any of these resonate.
There is no doubt that AI implementations can get complex but many businesses overestimate the expertise they need for it. Effective AI implementations are always focused on addressing clear business problems, working behind the scenes to address specific pain points or business opportunities like innovation, demand forecasting, accuracy in insights, or personalized strategies and recommendations. AI is not a one-size fits all solution, but it is a multi-purpose tool to be strategically applied to optimize business processes and solve complex business challenges.
In the current environment, every organization already has systems like ERPs, sales systems, supply chains, and customer feedback, which have all been creating valuable data. This is data that is waiting to be collated, filtered, and put to use for the most effective use cases. The advantage with AI models is that they work on what companies already have and build from there to improve data quality over time. Clean, organized, robust in-house data is not a requirement, but a strategic goal of AI.
The answer to this is a pilot project that starts with solving a single business need or pain point. Starting with a small project with minimal investment over 8-12 weeks could help enterprises see the current gaps and identify optimizations that maximize returns from an AI implementation. This is where the crawl-walk-run approach starts and is discussed in more depth below.
Yes, AI is still evolving but competitors are already putting their foundation in place amidst this change. Implementing AI-led transformation is no longer just about a singular competitor trying an innovative solution–it has become a competitive advantage in terms of returns, revenues, and efficiencies. Those waiting will see the gap widening every quarter in risk mitigation, innovation, and user experiences, ultimately impacting the bottom line. The cost of inaction will surpass the cost of measured experimentation.
AI implementations don’t need PhDs or multi-million-dollar tech budgets; they need intent. The companies in the pet and animal health industries reporting the best returns began with small but smart pilot projects that followed the crawl-walk-run approach. This helped them understand the exact scope of returns from AI solutions and charted a path to steer their businesses in rapidly evolving space.
Let’s delve deeper into the framework.
With the crawl phase, enterprises can identify a small area of inefficiency or redundancy in a business function and start by implementing some custom AI solutions to automate it. This would set the base to understand the real-world impact of implementing AI and seeing the returns from AI-led automation, freeing up the time resources spend on it.
We have captured some actual examples of use cases that various verticals in the pet and animal health industry could consider.
Pet Manufacturers:
Typical ROI: Within 6-8 weeks
Retailers:
Expected ROI: About 8-10 weeks
Researchers:
Positive results: Expected in 4-6 weeks
In the walk phase, enterprises can expand the AI automation projects to help innovate new products, research projects or competitors, or optimize internal workflows and external strategies. This phase can be used to refine models that showed promising results in the pilot phases, optimizing them based on early learnings. This phase should also ideally include integrating effective AI models with IT infrastructures and aligning AI goals with business KPIs.
This phase focuses on the bigger and broader innovation, operational efficiency, and velocity goals that enterprises want to achieve with their AI transformation journeys. Enterprises can build predictive capabilities for various business functions to cut costs by 20-30% while accelerating product launches and streamlining customer insights for continuous learning.
Speed is everything in a highly competitive market that is rapidly evolving with customer expectations and tech advancements. For companies in the pet and animal health space, this translates to leveraging the emerging technologies in AI and Data to reduce reliance on quarterly reviews and guesswork and moving to more real-time insights.
Pet Manufacturers:
Real Impact: Enterprises can ideate, strategize and launch smarter, more faster innovations, enabling accelerated speed-to-shelf, cost savings, and higher success rates.
Retailers:
Real Impact: 15-25% increase in inventory turns; 10-15% reduction in markdowns.
Researchers:
Real Impact: 3-6 months faster publication cycles, accelerating drug and product approvals.
With the advancements in technology, enterprises in the pet industry don’t need to sustain manual workloads which waste time and resources. While traditional workloads have served their purpose, upgrading legacy systems can unlock significant efficiencies and reduce the risk of errors in the process.
Pet Manufacturing:
Retailers:
Researchers:
This directly plays into meeting customer expectations as the demands for proactive health management has been replacing reactive care. Customers today expect faster, more accurate diagnosis and personalized nutrition that brings the best of what companies have to offer for their pets. Meeting this means higher levels of loyalty and lesser churn in a highly competitive market.
Pet Manufacturers:
Veterinarians & Clinics:
Retailers:
Just like the products customers expect today, the marketing for it also needs to be personalized and precise. AI models enable several use cases to help with precise targeting that reaches customers with varied probabilities of conversion.
Pet Manufacturers:
Retailers:
Veterinary care is undergoing significant shifts, moving from manual diagnosis to a more precision-based approach that can factor in multiple parameters to increase accuracy. This opens multiple routes to enhance pet care.
Integrating AI into pet and animal health businesses is a necessary step for businesses wanting to not only survive, but thrive in this new age of AI. Although it may seem complex and overwhelming at times, it doesn’t need to be. From fragmented legacy systems, internal data silos, suboptimal processes and workflows, to misaligned goals and unclear business objectives—the route has turns and twists that need expert understanding, skilled maneuvering, seamless implementation, and optimized ROI.
The right partner will bring deep industry and technology knowledge, the ability to tailor solutions that address enterprise-specific challenges while standing the test of time, and a careful analysis of mechanisms that can accelerate value realization while mitigating risks and barriers to adoption.
If you are a pet or animal health leader and have specific AI use cases or questions that you don’t see covered here, or you have thoughts on how these technologies can enhance pet care and business performance, drop us a line at sales@cambridgepettech.com and join the conversation on ways to shape the future of the pet and animal health industry by embracing the art of the possible.
