Building an enterprise-scale AI operating system

Product: From 12-Month Cycles to 30 Days

Product became the highest-performing AI department at Avalara. Their AIM30 (SDLC Platform) reduced software development lifecycle time from 12 months to just 30 days—a 40× acceleration that's now in production and actively used by engineering teams. Meanwhile, AI Docs automatically restructured over 8,000 pages of product documentation, scoring above 80 by Acrolinks and delivering 72,580 hours per year in value. This high-visibility project captured attention from the CPO, CEO, and board, shipping before year-end. The result: Product delivered 97% of target value, the highest performance across the entire organization.

Sales & Partnerships: Turning GTM into a Machine

Sales represented the largest portfolio with 14 initiatives focused on embedding AI directly into rep workflows. Live today are Discovery Scorecards, which replaced manual Gong reviews and deliver 5,000 hours per year through Slack-native summaries, and List Research Automation, widely adopted by reps and generating 6,000 hours per year in savings. In QA and near-term deployment are the Pricing Generator, which provides instant CPQ pricing during live calls with a 5,000 hours per year target, and CL Opportunity Re-Engagement, addressing 150,000 opportunities with 14,780 hours per year in potential value. The key insight: sales automation only works when embedded inside the rep's flow—Slack, calls, CRM—not as separate tools they have to remember to use.

Finance: Direct, Measurable Dollar Impact

Finance focused on hard ROI, not novelty. Their Unused Travel Credits Recovery initiative automated scanning of bookings with proactive alerts before expiration, recovering $247,000 while saving 120 hours per year. However, several additional finance workflows sit ready but blocked, highlighting a critical insight: most AI value is lost to access, not capability. Unlocking platform permissions would immediately release 75% of finance's projected value, demonstrating that infrastructure decisions often matter more than algorithm sophistication.

Security: Automating Risk, Not Just Speed

Security projects targeted risk reduction, not just efficiency gains. AvaSecClause, a collaboration between Security and Legal, automated contract clause analysis and compliance validation at scale, delivering 5,363 hours per year with production deployment in progress. Data Room Ticket Monitoring automated triage and prioritization, reducing response time and risk exposure across the organization. The key insight: AI's highest leverage in security is preventing incidents—value that never shows up in incident reports but protects the business from exposure that could cost millions.

RevOps: Decision Velocity as a Competitive Advantage

RevOps transformed decision-making speed across the organization. Sales Attribution Automation reduced first response time from 12 hours to just 1 hour, and resolution time from 24 hours to 2 hours—a 91.7% faster decision cycle. GTM Enablement Content Maintenance improved content freshness by 80% while reducing cycle time from 20 days to 5 days or less. The outcome: revenue teams moved faster with fewer interruptions and less manual coordination, turning operational efficiency into competitive advantage.

People: Avi for Avalarians (The Flagship Within the Flagship)

Avi represents the largest single initiative in the entire portfolio—an AI companion for Avalara's 600 managers that touches nearly every employee indirectly. Embedded directly in Slack, Avi targets 189,900 hours per year in value, approximately 3,652 hours saved per week at full adoption. Avi covers event prep, ongoing coaching, onboarding, 30/60/90 reviews, and continuous guidance. The strategic impact: Avi frees People & Culture teams to focus on the 20% of work that requires human judgment, rather than drowning in administrative repetition.

Governance & Triage: Making AI Safe at Scale

The n8n X3 Workflow Security Scanner achieved 100% scan coverage across 720 workflows, revealing that 84% contained issues and 29% had SEV4–SEV5 critical risks. This initiative saves 7,200 hours per year in manual triage while shifting governance from reactive to proactive. AI didn't create risk—it surfaced it before production, giving teams the visibility to address vulnerabilities systematically rather than discovering them through incidents.

Training & Enablement: Why This Worked

Avalara didn't outsource AI—they learned it. The company conducted on-site training in India for 40 GTM leaders, rolled out company-wide n8n training that achieved a 65% completion rate (exceptionally high for optional training), and held C-suite executive sessions that earned positive feedback from the CTO and CPO. Finance demos converted skeptics into sponsors. This approach created internal AI fluency, not dependency, ensuring the organization could sustain and expand its AI capabilities without relying on external resources.

ROI Math (Avalara's Own)

The numbers tell a compelling story:

• 84,176 hours/year live in production
• $75/hour conservative rate
• ~$6.3M annualized value
• ~$1M investment
• 6–7× ROI before pipeline projects go live

And this excludes risk reduction, revenue acceleration, cultural shift, and platform leverage—benefits that are real but harder to quantify. The financial case alone justifies the investment multiple times over.

The Real Outcome: An AI Operating System

Avalara didn't buy AI. They built the ability to deploy AI continuously. They now have a repeatable intake model, governance that scales, measurable ROI, trained internal teams, a 12-month roadmap with clarity, and a playbook most enterprises don't have. This isn't a collection of point solutions—it's an operating system for sustained AI value creation.

The Takeaway

This is not a case study about workflows. It's a case study about organizational transformation. Avalara proves that AI succeeds when value is measured, execution is federated, governance is built in, training is prioritized, and leadership treats AI as infrastructure—not experimentation. This is what enterprise AI looks like when it actually works.