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Generative AI has moved fast. Large language models are now widely available, increasingly commoditised, and easy to experiment with. Yet many organisations are still struggling to get real value into production. The reason is no longer the model. It’s the data. The most effective GenAI systems today are built on three pillars: strong data engineering foundations, retrieval-augmented generation (RAG), and clear production discipline. When these are missing, even the best mode

AI is everywhere in business right now, but it only works if the data behind it is ready. A lot of AI projects stall or fail because the data is messy, incomplete, or poorly structured. That’s where data engineers come in. They make sure the data is clean, reliable, and accessible so AI can actually deliver value. Here are five data engineering skills that every company looking to adopt AI should prioritise. 1. Data Warehousing Data warehouses like Snowflake, BigQuery, and Re

AI initiatives don’t fail because of models. They fail because the data isn’t ready. Across the UK and Europe, organisations are investing heavily in AI, machine learning, and GenAI yet many struggle to move beyond pilots. The common blocker is fragmented pipelines, unreliable datasets, and a lack of experienced data engineering ownership. At Raice AI Recruitment, we help companies hire high‑quality data engineers, contract or permanent who clean, structure, and scale data so

At Raice, we help organisations hire outstanding Generative AI researchers, Research Scientists, and PhD‑level AI talent across the UK and Europe. We are a specialist recruitment agency focused on GenAI, foundational models, and advanced AI research roles, with a strong understanding of the academic and commercial AI landscape. If you’re finding it difficult to hire researchers who can translate cutting‑edge theory into real‑world impact, Raice is here to help. Why GenAI & Ph

Artificial Intelligence is now embedded on both sides of the recruitment process. Candidates are using AI to write CVs at scale, while employers are relying on AI-powered Applicant Tracking Systems (ATS) to filter, rank, and reject applications before a human ever sees them. On paper, this sounds efficient. In reality, it’s creating a quiet arms race and not necessarily producing better hires. At Raice.AI Recruitment, we’re seeing the impact first-hand. The Rise of the AI-Wr

Raice.AI - January 2026 2025: From Experimentation to Accountability If 2024 was defined by hesitation and budget scrutiny, 2025 became the year UK organisations grew up around AI. Across financial services, retail, energy, life sciences and the public sector, the conversation shifted decisively. Artificial intelligence was no longer treated as a future bet or a boardroom buzzword but it also lost its “silver bullet” status. Leaders moved beyond pilots and prototypes, focusi

Building an AI team from scratch can feel like navigating a maze. There are countless roles, technologies, and approaches but not every hire matters equally in the early stages. At Raice AI Recruitment , we help companies prioritize the right talent to turn AI strategies into reality. Here are the first five hires that truly matter  when building an AI team in 2026. 1. AI/ML Engineer Your first hire should be someone who can turn prototypes into production-ready systems . Why

In the evolving AI landscape of 2026, companies are increasingly hiring for roles that sound similar but are fundamentally different: Data Science  and AI Engineering . Misunderstanding these distinctions can lead to misaligned teams, stalled projects, and wasted recruitment budgets. At Raice AI Recruitment , we guide organisations in building high-performing AI teams. One of the first steps is helping leaders clearly differentiate between these two critical disciplines. Data

Over the past few years, AI strategy decks have become a staple in boardrooms. Vision statements, roadmaps, maturity models, and vendor shortlists fill slide after slide. The language is confident. The ambition is bold. And yet, many of these AI strategies never leave the PowerPoint. At Raice AI Recruitment , we see the same pattern repeatedly: companies invest heavily in defining what they want to do with AI, but fail to invest in who will actually make it happen. Without th

Generative AI has moved far beyond experimentation. By 2026, GenAI is no longer a niche capability owned by a handful of research teams it is embedded into product development, operations, marketing, customer support, and decision-making at scale. Yet hiring for GenAI talent remains one of the most misunderstood challenges facing companies today. Many organisations still chase buzzwords, academic credentials, or tool-specific experience, only to discover that these hires stru

Pressure to “do something with AI.” Yet despite unprecedented investment, most AI projects still fail before they ever reach production . At Raice, we see this pattern repeatedly. The problem isn’t lack of ambition or intelligence. It’s a disconnect between experimentation and execution. Here’s why most AI projects stall and what successful teams do differently. 1. AI Projects Start as Experiments, Not Products Many AI initiatives begin as proof-of-concepts (POCs) built by in

At Raice, we believe in activating senior-led networks that deliver real outcomes in AI and tech. That’s why it was a pleasure to connect with Dr. Simant Prakoonwit, professor at Bournemouth University and fellow innovator whose recent project was selected as one of Innovate UK’s top success stories. His work, Protecting image-sharing users from inappropriate content with AI, tackles a critical challenge in online safety using intelligent filtering and classification. It’s no

In our fast-paced digital world, businesses are always looking for ways to stand out and thrive. With the rise of artificial intelligence (AI), the landscape of competition has changed drastically. Modern organizations must adopt creative strategies to leap ahead of rivals. One effective approach is the race-inspired philosophy, which focuses on speed, flexibility, and execution. This post delves into how businesses can use race-inspired AI strategies to boost their competiti

In a world where artificial intelligence (AI) evolves at lightning speed, knowing how to integrate this technology wisely is crucial for businesses. It is not just about how quickly you can develop AI but how effectively you can use it. This blog post digs into how race-inspired philosophies can help organizations utilize AI strategically. By embracing a thoughtful approach, businesses can keep up with technology and succeed in implementing AI with confidence. The Race to Use

In today's fast-paced digital world, adopting artificial intelligence (AI) is not just an option—it's necessary. Organizations aim to leverage AI to boost their operations, improve decision-making, and fuel innovation. However, the path to successful AI integration can be daunting. This is where a race-inspired mindset becomes vital, providing a clear framework to navigate the complexities of AI adoption. The "race-inspired philosophy" focuses on speed, clarity, and action—qu