Expert insights
In this eBook you will learn about important generative AI trends, application cases and advice for getting started with NashTech.
Tech leaders interested in finding out about the latest technology trends
Executives who want to gain a competitive advantage
C-Suite IT leaders needing to meet the boardroom's ask to ‘implement AI, and fast'
Companies looking to keep up with ever-changing market demands
How generative AI can enhance efficiency and reliability in software engineering processes
Understanding how generative AI is easy to use and accessible to everyone
An actionable roadmap for getting started with generative AI.
Insights
Integration used to be treated as a behind-the-scenes technology task: something for IT teams to solve once the business had decided what it wanted to do.
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Artificial intelligence may dominate technology conversations, but for many organisations, the biggest obstacle to AI adoption is not the technology itself.
Overview On 10 June 2026, the Pet and Equine Insurance Association (PEIA) brought together insurers, brokers, veterinary professionals, technology providers and legal experts in London for a full-day forum on one of the most significant shifts facing the sector: how artificial intelligence is reshaping the journey from the veterinary clinic to the insurance claim. Sponsored by NashTech, the day combined frontier thinking with practical, grounded experience from across the pet and equine insurance market. The result was a genuinely rare conversation - chief executives, clinicians, legal experts and technologists in one room, disagreeing productively and thinking out loud about a future none of them can navigate alone. This overview shares the themes and the standout moments for a wider audience: partners, prospective members, the media and anyone with a stake in where this industry is heading.
Machine learning (ML) models are only as good as the data that trains them. High-quality, well-organised, and accurately labelled data is the foundation of successful ML projects. However, preparing this data in-house often takes significant time and puts a strain on resources, making it an expensive and challenging task for many teams.
