Enterprise AI has a measurement problem that looks like an adoption problem. Activation is high and proof is thin. Nobody can connect AI usage to a business outcome the CFO will accept. The operating layer failure is the absence of a metric architecture that turns telemetry into intervention and intervention into defensible value.
This is the oldest part of our work and the through-line of a thirty-year career. We have built statistical models, machine-learning predictive models, production NLP, computer vision systems, and now LLM-based agents. We design the metric architecture, define the input and output indicators, and build the scenario libraries that turn dashboard signals into the intervention paths an operations or customer success team can execute without us.
Proof in this industry
Case study. Designed the customer-facing measurement playbook for an enterprise AI productivity company serving CFO and CIO buyers, translating telemetry into specific intervention paths. The framework remains in production and became the foundation for the company's external positioning.
Career record. At Resonate, scaled psychographic-targeting algorithms applied to 230 million users as the second employee. At Bulletin Intelligence, pioneered machine-learning news and social analytics that added 50 percent growth and enabled the exit to Cision, delivering daily intelligence briefings to the White House and Cabinet agencies.