FEATURE: PROFITABLE AI
creates major blind spots. Teams miss key signals, struggle to align on strategy and rely on guesswork instead of shared insight. Nearly 70 % of GTM leaders report a lack of confidence in their revenue-critical data. That makes every QBR uncertain, every board meeting reactive and every forecast harder to trust.
CIOs solve this by designing the architecture that captures, contextualises and activates these signals, transforming fragmented activity into revenue context – the foundation for running revenue with AI.
When done correctly, this infrastructure enables AI to operate within workflows rather than outside of them. It provides every team – from sales to marketing to finance – with a shared, real-time view of what ' s happening in the business and the next levers to pull.
For decades, CRM has served as a static record system. Today, CIOs have the opportunity to transform it into a dynamic growth engine.
The rise of revenue context
Revenue context is the connective tissue that binds execution to outcomes. It tracks who did what, when, that led to what result across the entire customer journey. Unlike traditional analytics which often rely on static data snapshots, revenue context creates a living, time-aware model of the business, continuously updated with real-time signals across the GTM engine.
This context distinguishes beneficial AI from noisy AI because it enables models to generate recommendations based on actual actions rather than just on what teams happen to log. It allows AI
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