AI & Agentic AI ·
Most AI initiatives don't fail because of the technology
Why AI projects stall between pilot and production, and what to fix first.
Insights
Practical perspectives on AI, agentic AI and cloud engineering, from the people who build and run these solutions.
AI & Agentic AI ·
Start with the business, not the technology
How to identify the AI opportunities worth pursuing before choosing a model or a platform.
AI & Agentic AI ·
AI doesn't end when you go live
Why production AI needs monitoring, evaluation and continuous improvement.
AI & Agentic AI ·
What makes an AI agent enterprise-ready?
Controlled tool use, scoped identity, evaluation, human oversight and monitoring: the practical checklist for putting AI agents into production.
Cloud & AWS ·
Beyond lift and shift: choosing the right path to AWS
The seven migration strategies, and how to decide which one fits each application.
Cloud & AWS ·
The Well-Architected Framework in practice
What the six pillars cover, what a review looks like and why it is worth doing before problems appear.
Cloud & AWS ·
Cloud cost optimisation is a habit, not a project
Why one-off savings fade, and how to build a cycle of visibility, optimisation and operation.
Business Use Cases ·
Where AI pays off in finance operations
Reconciliation and document processing are good starting points for AI in finance, when they are designed with people in the loop.
Business Use Cases ·
From dashboards to conversations: AI assistants for operations teams
How conversational assistants grounded in live data help operators and customers get answers without navigating reports.
Industry Perspectives ·
Why energy AI starts with the data pipeline
Forecasting, anomaly detection and storage optimisation all depend on one thing: clean, aligned, timely data.
Industry Perspectives ·
Edge AI and privacy: send metadata, not video
How running models on the camera keeps video out of central systems in vehicle-recognition projects.
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