Path to 7%:
Building the AI Infrastructure
That Makes Transformation Land
A COO-visible, five-pillar enterprise AI transformation program converting fragmented, manual operations into AI-enabled workflows — with measurable impact across billing, forecasting, retention, and customer AI adoption.
Following a significant PE investment, this enterprise SaaS company faced mounting retention pressure across five critical business functions — each operating in complete isolation. AI tooling was being deployed but not adopted. M&A activity was accelerating churn risk in key segments. The MSP partner ecosystem had no formal playbook. Strategic accounts lacked structured coverage. And $1.7M+ in billing errors had accumulated across two quarters with no coordinated remediation.
The COO needed a program that didn't just connect these workstreams — but converted each one from a manual, fragmented operation into an AI-enabled workflow with real-time executive visibility and measurable business impact. That's the program I was brought in to build.
"The problem wasn't that these teams lacked effort. It was that they had no shared infrastructure. No governance. No unified view of what was at risk. The five pillars weren't connected to each other — or to leadership."
Pillar 1 · AI Adoption
Designed and led the enterprise AI adoption program — building the governance framework, adoption metrics, and workflow integration infrastructure that moves the org from ad hoc AI experimentation to structured, AI-first processes. This pillar is currently running ahead of schedule.
Impact
Cross-functional AI enablement deployed across CS, Sales, and RevOps. Organization has measurable AI adoption metrics for the first time. Customer-facing AI adoption program built from scratch including narrative framework, success metrics, and formal adoption plans.
Pillar 2 · M&A Churn Defense
Built the monitoring and intervention infrastructure to identify and address M&A-driven churn risk in real time — creating playbooks, escalation protocols, and executive visibility into accounts at risk due to acquisition activity in customer portfolios.
Impact
Proactive churn defense program in place for M&A-affected accounts, replacing reactive firefighting with structured risk management.
Pillar 3 · MSP Ecosystem
Used AI to identify process gaps across the MSP partner ecosystem — analyzing data consumption patterns, forecasting accuracy, and enablement gaps. Built formalized MSP playbooks and a structured partner adoption program replacing ad hoc partner management.
Impact
Formalized MSP playbooks deployed. Improved forecast accuracy and partner data visibility. Repeatable enablement model replacing heroics.
Pillar 4 · Strategic Accounts
Built structured coverage and intervention framework for strategic accounts — ensuring high-value customers have clear ownership, defined success milestones, and proactive engagement at every stage of the lifecycle.
Impact
Strategic account coverage model in place with defined ownership and escalation paths at each lifecycle stage.
Pillar 5 · Billing & Entitlements
Built an AI-powered billing reconciliation tool that identifies gaps across disparate billing and financial systems in real time — surfacing previously invisible ARR exposure and giving Billing, Finance, and Renewals teams a single source of truth.
Impact
15 hours per week of manual cross-system reconciliation eliminated. $1.7M+ in billing errors surfaced and escalated for remediation. Finance and Renewals leadership now have unified, real-time billing visibility for the first time.
Beyond the five pillars, I built the AI-enabled infrastructure that runs underneath the entire program — converting manual reporting and fragmented data into real-time intelligence for leadership.
AI Revenue Forecasting Model
Replaced fragmented spreadsheet forecasting with a live model pulling data continuously across CRM, Finance, and Support. Revenue leaders now have an always-current view that recovers 40 hours per week previously lost to manual reporting cycles.
AI-Powered CS Risk Dashboards
Implemented automated signal monitoring across expansion, adoption, and renewal metrics. Surfacing risk earlier and replacing 10 hours per week of manual tracking with executive-ready dashboards that update in real time.
Executive Decision-Support Layer
Built E-Staff dashboards connecting health scores, expansion signals, and unit economics to executive planning cycles — giving the COO and SVP real-time portfolio visibility for the first time.
Customer AI Adoption Program
Built the customer-facing AI adoption program from scratch — narrative, success metrics, structured adoption plans. The organization can now formally speak to AI offerings and track customer adoption outcomes.
- ✓COO-visible program with direct executive praise for execution quality
- ✓SVP executive sponsorship with cross-functional accountability authority established
- ✓AI pillar running ahead of schedule — first measurable AI adoption metrics in place
- ✓15 hours per week eliminated in billing reconciliation; $1.7M+ in errors surfaced
- ✓40 hours per week recovered in revenue forecasting cycles
- ✓10 hours per week recovered in CS leadership reporting and meeting prep
- ✓Customer AI adoption program launched — organization can formally speak to and track AI outcomes with customers for the first time
- ✓Formalized MSP playbooks deployed; partner ecosystem moved from heroics to repeatable process
PM Takeaway
"The program infrastructure isn't the overhead. It's what converts an AI initiative from a slide deck into organizational change."
Every hour recovered and every risk surfaced started with building the governance layer first — clear workstream ownership, a unified RAID log, executive operating cadence, and cross-functional accountability. The AI tools work because the program structure gives them somewhere to land. That's the lesson: you don't start with the technology. You start with the infrastructure that makes the technology stick.