Insights that drive AI success
Stay ahead of the AI curve with expert perspectives, practical guides, and insider views from the Firemind team. Filter by topic, format, or industry to find the content most relevant to your journey.
The Rise of Managed AI: What Every Enterprise Needs to Scale Safely
Managed AI is the next step for enterprise AI. Learn how Firemind helps organisations move from impressive pilots to reliable, scalable, real-world performance.
Co-Innovation: The fastest and safest path to AI adoption in insurance
Discover why co-innovation with Firemind, AWS and NVIDIA accelerates AI adoption in insurance. Faster alignment, safer pilots, better outcomes.
Why small, high-Impact AI projects are outperforming Big transformations in insurance
Insurance AI works best when you start small. Explore quick-win workflows, low-risk pilots and funding options to accelerate adoption.
The companies winning with AI aren’t the ones who build it, they’re the ones who run it.
Over the past year, every enterprise has been racing to “do something with AI.” Proof-of-concepts are everywhere. Slide decks are full of promising results. But when it comes to moving those pilots into production, something breaks. By now, most companies can prove that AI works: that’s feasibility. Very few can make it reliable, governed, observable, secure and cost-efficient at scale: that is viability.
Insurance AI roundtable: Where AI is delivering ROI in insurance
AI is speeding up underwriting, enriching data, and improving decision-making. Key insights from our insurance AI roundtable with AWS and Convex.
How to identify the most common mistakes in GenAI data preparation?
Identifying common mistakes in GenAI data preparation is crucial for AI success. The primary errors—insufficient data cleaning, poor structure, inadequate labeling, and misaligned formats—can reduce model accuracy by up to 40% and significantly increase processing time. Rather than costly system rebuilds, managed AI solutions offer a practical alternative. These intelligent systems integrate with existing workflows to automatically identify and fix data quality issues through automated checks, intelligent mapping, and adaptive cleaning processes. By addressing these preparation mistakes incrementally, organisations can improve their AI outcomes within weeks whilst maintaining operational continuity and avoiding the risks of complete system overhauls.
From Chaos to Control: AI Incident Management as a Managed Service
From manual ticket triage to AI-powered incident management
Why 80% of Insurers Struggle with AI and How COOs Can Fix It
Around 80% of insurers are experimenting with AI, yet fewer than 5% have use cases live in production. The majority of projects stall at proof-of-concept,
How AI Improves Expense Ratios in Insurance
This post is part of a series based on discussions with senior leaders in insurance, including Arno de Wever, Head of Commercial P&C Insurance at
Legacy Systems & AI Readiness in insurance: A Practical COO Blueprint
This post is part of a series based on discussions with senior insurance leaders, including Arno de Wever, Head of Commercial P&C Insurance at Amazon