Edith Heroux

Revenue leaders across enterprise software organizations face a persistent challenge: despite investing heavily in sales technology and headcount, forecast accuracy remains stubbornly below 75%, and pipeline coverage ratios fluctuate wildly quarter to quarter. The root cause isn't lack of effort—it's the exponential complexity of managing hundreds or thousands of opportunities simultaneously while trying to extract meaningful signals from CRM data that's often incomplete, inconsistent, or outdated by the time it's reviewed. Traditional opportunity management approaches rely on manual qualification frameworks and periodic deal reviews, but these methods can't scale to the velocity and volume demands of modern enterprise sales cycles. The emergence of AI in Opportunity Management represents a fundamental shift from reactive to predictive pipeline governance. By analyzing historical win/loss patterns, buyer engagement signals, and deal progression velocities across th...