AI SaaS (Series C)
Executive
Summary
Redesigning the commercial operating model of a fast-growing AI company after explosive growth exposed fundamental operational bottlenecks across revenue operations, renewals and commercial data.
Client
Confidential
Industry
AI SaaS
Stage
Series C
Revenue
$50M-$100M
Markets
Global
Team
~500 employees
The
Challenge
After a year of exceptional growth, the company’s operating model could no longer support the pace of the business.
Commercial data was fragmented across multiple systems, with conflicting reports making it impossible to establish a single source of truth. Millions of dollars in renewal revenue lacked visibility, forcing Customer Success teams to work reactively instead of proactively. Finance struggled to produce reliable forecasts, while Sales relied on inconsistent pricing practices that introduced unnecessary operational complexity.
The challenge wasn’t growth—it was an operating model that had failed to evolve alongside it.
My Role
Following a comprehensive assessment of the commercial organization, I stepped into an interim transformation role to redesign the commercial operating model and restore visibility, governance and scalability across the revenue organization.
The engagement combined executive advisory with hands-on execution across commercial processes, systems and data architecture.
What
Changed
Redesigned the commercial data model to establish a reliable single source of truth
Reimplemented the CPQ platform alongside a new pricing model to standardize commercial execution
Rebuilt the end-to-end renewal operating model and introduced a predictable renewal schedule
Improved commercial data quality and governance across core business systems
Enabled AI-ready commercial workflows through a cleaner and more structured data foundation
Increased alignment between Sales, Customer Success and Finance
Business
Outcome
$15M in previously hidden renewal revenue became fully visible and manageable
98% reduction in process-driven churn within the renewal process
Standardized pricing execution through guided CPQ workflows
Significantly improved forecasting accuracy and revenue visibility
Built a scalable commercial data foundation supporting future AI initiatives
Reduced operational friction across commercial teams
Key
Takeaways
Hypergrowth rarely breaks companies. Outdated operating models do. By rebuilding the commercial foundation, the organization regained visibility, predictability and the ability to scale with confidence.
AI SaaS (Series C)
Executive
Summary
Redesigning the commercial operating model of a fast-growing AI company after explosive growth exposed fundamental operational bottlenecks across revenue operations, renewals and commercial data.
Client
Confidential
Industry
AI SaaS
Stage
Series C
Revenue
$50M-$100M
Markets
Global
Team
~500 employees
The
Challenge
After a year of exceptional growth, the company’s operating model could no longer support the pace of the business.
Commercial data was fragmented across multiple systems, with conflicting reports making it impossible to establish a single source of truth. Millions of dollars in renewal revenue lacked visibility, forcing Customer Success teams to work reactively instead of proactively. Finance struggled to produce reliable forecasts, while Sales relied on inconsistent pricing practices that introduced unnecessary operational complexity.
The challenge wasn’t growth—it was an operating model that had failed to evolve alongside it.
My Role
Following a comprehensive assessment of the commercial organization, I stepped into an interim transformation role to redesign the commercial operating model and restore visibility, governance and scalability across the revenue organization.
The engagement combined executive advisory with hands-on execution across commercial processes, systems and data architecture.
What
Changed
Redesigned the commercial data model to establish a reliable single source of truth
Reimplemented the CPQ platform alongside a new pricing model to standardize commercial execution
Rebuilt the end-to-end renewal operating model and introduced a predictable renewal schedule
Improved commercial data quality and governance across core business systems
Enabled AI-ready commercial workflows through a cleaner and more structured data foundation
Increased alignment between Sales, Customer Success and Finance
Business
Outcome
$15M in previously hidden renewal revenue became fully visible and manageable
98% reduction in process-driven churn within the renewal process
Standardized pricing execution through guided CPQ workflows
Significantly improved forecasting accuracy and revenue visibility
Built a scalable commercial data foundation supporting future AI initiatives
Reduced operational friction across commercial teams
Key
Takeaways
Hypergrowth rarely breaks companies. Outdated operating models do. By rebuilding the commercial foundation, the organization regained visibility, predictability and the ability to scale with confidence.
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