Applied AI

Practical thinking on AI implementation, operating architecture, and scaling professional services firms.

Context engineering: the skill that makes all your AI smarter

The difference between generic AI output and genuinely useful work is not the model. It is what the model knows before you ask.

Most AI projects stall for fixable reasons. Three operational problems explain the gap between adoption and value.

Your board wants growth. Your ops team is stretched. The answer is leverage: redesigning work so each person's effort compounds.

If AI makes your team faster and you bill by the hour, efficiency becomes a revenue problem. The fix is productised services.

Your AI tools are not failing. The layer beneath them is missing. Six elements that separate firms achieving real value from those running pilots.

79% of companies now use agentic AI. Fewer than one in 20 capture real value. The difference is governance, not technology.

Book a 30-minute discovery call. We will discuss your operational challenges and identify where to start.