Computer Systems Design and Related Services2024Machine Learning (classification)NLPPredictive AnalyticsB2B
Leapwork

Leapwork consolidates Chorus, Gong, and People.ai into Clari for unified revenue forecasting and simplified sales adoption

AI test automation company Leapwork replaced a fragmented stack of three revenue intelligence tools with Clari's unified platform, simplifying forecasting and improving adoption across the sales organization. Clari's broader enterprise customer research shows 398% ROI over three years.

Platform ROI398 % (Clari enterprise composite)
Payback<6 months (enterprise composite)
4 min read

Background

As Leapwork scaled its sales operation, maintaining separate tools for conversation intelligence, activity capture, and revenue forecasting created fragmentation. Sales managers had to reconcile data across multiple systems, and rep adoption suffered because each additional tool added friction to daily workflows.

What Was Implemented

  • Replaced Chorus, Gong, and People.ai with Clari's unified revenue orchestration platform
  • Consolidated forecasting, deal inspection, conversation intelligence, and pipeline management into a single dashboard
  • Single sign-on, unified data model, and one source of truth for sales leadership
  • AI-driven forecast signals and deal risk scoring embedded within the same workflow

Results

Leapwork achieved operational simplification by consolidating three separate tools into one platform, improving sales team adoption through reduced workflow friction. Clari's enterprise composite research (not Leapwork-specific) reports a 398% ROI over three years, payback under six months, and doubled win rates across its enterprise customer base. Leapwork-specific quantified outcomes are not confirmed in publicly available sources.

Lessons

  • Platform consolidation can be as strategically valuable as individual capability improvement: reduced tool switching improves adoption and data consistency
  • A unified source of truth matters most for forecast accuracy — inconsistent data inputs are a primary driver of forecast error
  • Win/loss analysis requires integrating conversation intelligence (what was said), activity data (what happened), and CRM signals (what's in the pipe) — single-platform approaches reduce reconciliation burden
  • AI-driven forecasting value compounds over time as models learn organizational deal patterns

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