Turning AI Hype into Reality, Now!

The AI industry has reached a critical inflection point. While billions continue to pour into experimental projects, a sobering MIT report reveals that 95% of AI initiatives are failing to reach production and generate real business value. The question is no longer “what’s the next big breakthrough?” – it’s “how do we operationalise what we already have?”

After years of breathless hype about AGI being just around the corner, enterprises are confronting an uncomfortable truth: Large Language Models alone are not enough. The industry is fragmenting into distinct technical approaches – machine learning for analysis, rules-based systems for automation, agentic frameworks, RAG databases for knowledge retrieval, and symbolic reasoning for precise decision-making. Each has its strengths. Each has its limitations. Understanding which tool fits which problem is now the difference between success and expensive failure.

But there’s a deeper issue at play. When you tell an LLM “do not hallucinate” or “follow my rule book exactly,” you’re not actually giving it instructions – you’re just providing input tokens that influence output tokens. Your institutional knowledge isn’t a first-class citizen in the tech stack. It’s merely context that can be forgotten or misinterpreted. For the 25% of AI use cases in regulated industries where the consequence of error is intolerable, this isn’t good enough.

James Duez – a veteran of Rainbird’s 13-year journey to build trustworthy AI – cuts through the noise to reveal what’s actually working. He explains why world models matter, how symbolic reasoning differs from probabilistic prediction, and why the future isn’t about waiting for the next shiny model but about deploying hybrid approaches that make knowledge computable with the same precision that Excel applies to numbers.

Stop waiting for the next best thing. Learn how to turn AI hype into reality now.