Why redesigning the process must come before automating it
Automation applied to a broken workflow doesn't fix the workflow — it scales the breakage. A practical framework for sequencing redesign ahead of AI deployment.
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Perspectives
Field notes from AI-native transformation.
Practical thinking on process redesign, AI-led execution, and the governance that makes performance gains last — drawn from our work across industries.
Agentic AI as an execution layer, not a chatbot
The most valuable agentic AI deployments run operational processes end to end. Here's how to identify which of your workflows are ready.
Read the piece →The four levers of business performance, and why they compete
Profitability, efficiency, experience, and growth pull against each other more often than leaders expect. A model for balancing all four at once.
Read the piece →Governing AI in the mid-market: a lighter-weight framework
Enterprise AI governance models are often too heavy for SMEs. A right-sized approach to controls, ownership, and oversight.
Read the piece →Mapping cost-to-serve before you cut it
Margin recovery programs that skip a proper cost-to-serve diagnostic tend to cut the wrong things. Here's the diagnostic we run first.
Read the piece →Data foundations most AI programs get wrong
The AI model is rarely the constraint. The data feeding it usually is. What a credible data foundation actually requires before go-live.
Read the piece →Sizing an AI-led transformation for a mid-sized enterprise
How to scope a transformation program that matches the resources and risk appetite of an SME, without diluting the ambition.
Read the piece →The quarterly rhythm that keeps performance gains from decaying
Most performance improvements erode within twelve months without a governance cadence. What that cadence should actually look like.
Read the piece →Customer journey redesign in the age of generative AI
Generative AI changes what's possible at each touchpoint — but only if the underlying journey has been redesigned around it.
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