Use Cases by Department
The following sections highlight real-world AI use cases deployed across departments, demonstrating practical applications that other enterprises can leverage.
Product: From 12-Month Cycles to 30 Days
Impact: 80k hours/year saved | 40× faster SDLC cycles | 96% time saved in brand content creation and review
Product became one of the highest-performing AI departments in terms of value delivery.
Key Use Cases:
- SDLC Acceleration: Automated platform that reduced software development cycles from 12 months to 30 days—a 40× acceleration. The platform is now in production and actively used by engineering teams across sprints, testing, and deployment workflows.
- Knowledge Base Intelligence System: Automatically restructured 8,000+ pages of product documentation, improving searchability and accuracy with high quality scores.
- Brand Voice & Content Generation Engine: AI-powered marketing automation system that generates on-brand assets (emails, presentations, videos, images, web copy) while integrating with email platforms for approvals and cloud storage for centralized asset management and distribution. Reduced asset creation time by 96% across all formats while saving executives 20+ hours in weekly content review and approval cycles.
Sales & Partners: Turning GTM into a Machine
Impact: 22k hours/year saved | 14 projects
Sales and Partners represented the largest portfolio with 13 combined initiatives.
Key Use Cases:
- Sales Conversation Intelligence: Replaced manual call reviews with automated analysis and scorecards. Delivers real-time insights and coaching opportunities, saving 5,000 hours/year through Slack-native summaries that sales managers can act on immediately.
- Account Research Automation: Automated prospecting research tool widely adopted by sales representatives. Generates account insights, competitive intelligence, and talking points, saving 6,000 hours/year in manual research time.
- Dynamic Quote Generation: Instant pricing generation during live sales calls, eliminating back-and-forth with deal desk. Targets 5,000 hours/year in time savings while improving quote accuracy and response time.
- Dormant Pipeline Reactivation: Intelligent system for re-engaging stalled opportunities. Addresses 150K dormant opportunities with personalized outreach strategies, targeting 14,780 hours/year in potential savings.
Finance: Direct, Measurable Dollar Impact
Impact:0.12k hours/year saved | $9K in value
Finance focused on hard ROI with initiatives that deliver both time savings and direct cost recovery.
Key Use Case:
- Expense Recovery & Optimization Alerts: Automated system that scans bookings and provides proactive alerts before expiration of credits, discounts, and prepaid services.
Security: Automating Risk, Not Just Speed
Impact: 2k hours/year saved | Proactive risk prevention
Security initiatives targeted risk reduction and compliance automation, not just operational efficiency.
Key Use Cases:
- Contract Intelligence & Compliance Scanner: Automated analysis of contract clauses with compliance validation at scale. Scans legal agreements for security requirements, data protection clauses, and regulatory obligations, saving man-hours while reducing compliance risk.
RevOps: Decision Velocity as a Competitive Advantage
Impact: 8.7k hours/year saved | 91.7% faster decisions
Key Use Cases:
- Sales Attribution Intelligence: Automated attribution system that dramatically improves response time for sales credit inquiries. First response time reduced from 12 hours to 1 hour. Full resolution time decreased from 24 hours to 2 hours—91.7% faster overall, enabling revenue teams to make decisions with minimal friction.
- GTM Content Lifecycle Automation: Automated system for maintaining and updating go-to-market enablement content. Content freshness improved by 80%, with update cycle time reduced from 20 days to ≤5 days, ensuring sales teams always have current materials.
Outcome: Revenue teams moved faster with fewer interruptions and less manual coordination.
People: Scaling Manager Effectiveness
Impact: 95k hours/year projected | 600 managers enabled
The largest single initiative in the entire portfolio, targeting manager productivity across the organization.
Key Use Case:
- Manager Enablement & Coaching Assistant: AI-powered assistant supporting 600+ managers, touching nearly every employee indirectly. Embedded directly in Slack to provide just-in-time support for critical management activities with a target of 189,900 hours/year savings (~3,652 hours per week at full adoption).
- Capabilities include meeting preparation, ongoing performance coaching, new hire onboarding support, 30/60/90 day reviews, policy guidance, and continuous leadership development.
Strategic Impact: This initiative frees People & Culture teams to focus on the 20% of work that requires human judgment—strategic planning, culture development, and complex employee situations—not administrative repetition.
AI Governance: Making AI Safe at Scale
Impact: 4.8k hours/year saved | 100% workflow coverage
Key Use Case:
- Workflow Risk & Compliance Scanner: Automated security and compliance scanning across all AI automation. Achieved 100% scan coverage across 720 workflows, identifying that 84% contained issues, with 29% having critical SEV4-SEV5 risks. Saved 7,200 hours/year in manual triage while dramatically improving security posture.
This shifted governance from reactive to proactive. AI didn't create risk—it surfaced vulnerabilities before they reached production, enabling the team to address issues systematically rather than responding to incidents.
Training & Enablement: Why This Worked
Avalara didn't outsource AI—they learned it.
- India On-Site Training: 40 GTM leaders trained in person
- Company-Wide Platform Training: 65% completion rate (exceptionally high for optional training)
- C-Suite Executive Sessions: Positive feedback from CTO and CPO
- Finance Demonstrations: Converted skeptics into sponsors
This created internal AI fluency, not dependency.
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 for AI initiatives
- Governance frameworks that scale with deployment
- Measurable ROI methodologies
- Trained internal teams with AI fluency
- A 12-month roadmap with clear execution visibility
The Takeaway
This is not a case study about workflows. It is a case study about organizational transformation.
Avalara proves that AI succeeds when:
- Value is measured consistently across the organization
- Execution is federated to department experts
- Governance is built into the process, not bolted on
- Training is prioritized to build internal capability
- Leadership treats AI as infrastructure—not experimentation
This is what enterprise AI looks like when it actually works.
