Lower cost per contact
Deflect repeat work without sacrificing service quality
Customer Operations
Your support team shouldn’t need to answer the same question five times in five channels.
High ticket volume, repetitive calls, and fragmented knowledge bases drain support capacity. I help customer operations leaders identify where AI and automation reduce volume, improve response times, and raise quality without damaging service.
Where customer automation usually fails
The problem is usually not the chatbot. It is bad triage, stale knowledge, and no owner for the exception path.
Ticket triage fails when every request gets the same priority and the queue never gets smarter
Knowledge systems disappoint when the content is stale, duplicated, or owned by no one
Contact deflection backfires when it lowers volume without actually resolving the issue
QA gaps appear when manual review cannot keep up with channel volume
Escalation logic breaks when customer context is split across systems and teams
Support teams adopt the tool only if the workflow fits how they already work
What I look for first
- Repeat contact reasons that are stable enough to route and deflect
- Ticket volume growing faster than the support budget
- Knowledge bases that exist, but agents do not trust or use them
- Manual QA that only covers a tiny slice of conversations
- Escalations that depend on humans remembering the right handoff every time
- No clear triage logic, so every ticket is treated like a priority case
- Leadership adding headcount before redesigning the workflow
Where AI can help
Start where the contact reasons are repeatable, the rules are visible, and the knowledge can be owned.
- Start with AI-assisted intake when inbound calls and tickets follow repeatable patterns
- Automate triage when urgency, topic, and customer context can be categorized consistently
- Surface relevant knowledge during live interactions so agents are not hunting across tools
- Automate repetitive support workflows and status updates before tackling deeper orchestration
- Expand QA coverage with conversation review and scoring after the workflow is stable
- Reduce unnecessary contacts through better self-service and smarter routing
- Build knowledge systems only when content ownership and freshness are already clear
Typical outcomes
Executives buy outcomes, not channel automation.
Faster first response
Route and prioritize the queue before customers wait too long
Better QA coverage
Review more interactions without adding headcount
Lower repeat contacts
Fix the routing and knowledge gaps that cause rework
What the assessment includes
The AI Opportunity Assessment for customer operations maps the highest-ROI opportunities across intake, triage, knowledge, QA, and escalation workflows.
- Current workflow review across phone, email, chat, and ticket channels
- Contact reason analysis and triage logic review
- AI opportunity map prioritized by volume impact and implementation effort
- ROI estimates for labor savings, capacity gains, and deflection quality
- Prioritized implementation roadmap
- Build-vs-buy recommendations for support automation and knowledge tools
- Vendor and tooling recommendations
- Data and integration requirements with CRM and ticketing systems
- 90-day implementation plan
Why I can speak to customer operations
I have shipped the support systems, not just written about them.
Turo Support Platform Transformation
Led migration of 1,900 users, 12 million customer cases, and 5 critical integrations while redesigning support workflows and the platform underneath them. The hard part was preserving service quality while the operating model changed.
- 12M cases migrated
- 1,900 users
- $3M+ savings
- Platform transformation
Customer Support Workflow Redesign
Redesigned support workflows and implemented automation, routing logic, and AI-assisted processes to reduce unnecessary customer contacts, summarize conversations, and make the queue easier to manage.
- 30% fewer contacts
- Routing automation
- Workflow redesign
Support Capacity at Scale
Built operational systems that maintained service quality while controlling costs during rapid growth — combining process redesign with technology implementation.
- Capacity increase
- Cost control
- Service quality maintained
Find where AI reduces support volume without breaking service quality.
Start with a practical roadmap before committing to a full implementation.