Start with observable behaviour
Look at how work moves, where decisions stall and which information disappears between teams.
Research
Evidence before opinion. Systems before tools.
RevOpHubs studies how B2B organisations connect commercial work — and which conditions make automation and AI genuinely useful.
How the work is approached
A public methodology for separating observation, inference and recommendation.
Separate symptoms from constraints
Low pipeline, poor adoption and unreliable forecasts can originate in very different layers.
Prefer connected evidence
Combine operating data, process observation and qualitative context instead of treating one dashboard as truth.
Make recommendations testable
Every recommendation should imply a change in behaviour, a measure and a reasonable review window.
Current research questions
The publication develops these questions through field notes, structured guides and diagnostic tools.
AI readiness
Which operating conditions predict whether AI creates capacity or merely produces more output?
Read foundation →CRM adoption
How much of adoption is explained by interface training, and how much by the quality of operating design?
Read foundation →Cross-functional loss
Where does value disappear between marketing, sales and customer teams — and how can it be observed?
Read foundation →