AI Value Strategy
Closing the gap between AI ambition and measurable value, for both customers and partners.
5 numbered blogs + throughline + token-bill sequel
Executives, CFOs, partner leaders
Read in order 1 → 5, then 7
Reading path
Start at Part 1, or jump to the part you needWhy 95% of AI projects fail, and how to put your clients in the 5%
95% of enterprise AI projects never reach production.
From aspiration to accountability: use case metrics that define AI success
Programme-level KPIs do not tell you whether an individual use case worked.
Bridging the value gap: what an AI value strategist owes both sides of the delivery table
The project is delivered and both sides celebrate; six months later the CFO asks what was achieved.
Mapping the pools: why AI ROI depends on where value actually lives
AI dashboards track whether a use case hit its ROI target, not whether it was the right use case.
Business Value Realization (BVR): the programme that makes the contract real
The value gap between ambition and outcome does not close on its own.
The throughline: this AI value series started in 2023, before I knew it
How do the four value articles connect?
Your AI programme has a token bill. Does it have an ROI number to match?
Token spend is now managed like compute (FinOps Tokenomics), but most programmes cannot show the matching return.
Other series
The Autonomous NOC
A self-improving AI reasoning layer that turns an alarm storm into one root cause in under 90 seconds, safely enough to act on a live network.
Bank Foundation Models
Why and how a bank should train a foundation model on its own event data, from privacy-first data to Nova Forge.
GAD-P: Governed data access
Natural-language access to enterprise data with governance enforced by code, not prompts.
Dark Web Fraud Signals
Turning dark-web chatter into early-warning signals for bank fraud models.
Real-time Fraud with TGNs
Real-time graph fraud detection with sub-100 ms inference that generalises across payment domains.
The Fast Path
Cutting bank loan decisions from days to minutes in six weeks.
Synthetic Data
Measuring and using synthetic data where real data is restricted.