For heads of support and CX

AI for Customer Support and CX Leaders

Fewer avoidable contacts, faster routine resolutions, and agents focused on the cases that need a person. Measured, not promised.

Book a 30-minute callTake the 2-minute scorecard

No pitch. If I can’t find a workflow worth automating, I’ll tell you on the call.

Where AI actually helps in support

The biggest wins in support are rarely a chatbot on the front door. They come from classifying contacts so you can eliminate the ones that should never happen, automating the routine ones with a human check, and routing the rest to the right person the first time.

I use the Eliminate, Automate, Too Late framework to sort every contact type. It keeps the team focused on root causes first, automation second, and makes sure the hard cases stay with people.

  • Contact classification with an LLM so you can see what is driving volume
  • Self-service and automation for routine, well-defined contacts
  • Agent-assist drafting with review, not auto-send, until quality earns trust
  • Routing and triage that cut handoffs and rework
  • QA sampling that scales with volume

Why I am the right person for this

I spent seven years running Lyft's global customer experience platforms: 500,000 monthly contacts, 12 sites, a 20-person team across product, engineering, and operations. I led the Zendesk, 8x8 IVR, and Twilio Flex migrations and ran the self-service programs that reduced avoidable contacts. Since then I have brought AI into support workflows at a membership platform and classified customer pain at scale with LLMs.

What a first project usually looks like

Pick one contact type with high volume and a clear right answer. Set the baseline. Build the automation with review. Roll it out to a pod first, then the floor. Measure deflection, handle time, error rate, and CSAT against the baseline. Most teams have their first number inside a month.

Questions

What people ask first.

Will this replace agents?

It changes what they spend time on. The goal is to remove work that should not exist, automate the routine, and give people the cases that need judgment. Headcount decisions are yours, and they are better decisions with real numbers.

Can you work inside Zendesk, Intercom, or Salesforce Service Cloud?

Yes. I have implemented Zendesk and Twilio Flex at scale and work with the native AI features in the major platforms as well as external models where they fit better.

Which AI tools and vendors do you work with?

I am vendor neutral. I have bought and implemented platforms such as Zendesk, Twilio Flex, and 8x8 at Lyft scale, and I build with Claude, OpenAI, and current agent tooling every week. The recommendation follows the workflow, not the other way around.

Do we need engineers for this to work?

Usually not for the first wins. Most operations automation comes from well-defined workflows and off-the-shelf tooling configured carefully. When a build is needed, I write the first version or a spec your engineers can act on.

We are in a regulated industry. Is that a problem?

It is the normal case for me. I run operations under FCRA today, and at Lyft I worked through regulatory programs that required careful tradeoffs. Compliance is designed into the workflow with audit trails and human review, not bolted on afterward.

Further reading
AI & Operations

Eliminate, Automate, Too Late: How to Sort Support Work Before You Automate It

The EAT framework (Eliminate, Automate, Too Late) for deciding which customer support work to remove, which to automate with AI, and which to leave to people, plus how to classify customer pain at scale with an LLM.

Product Strategy

Turning Customer Feedback Into Product Strategy

How to use customer feedback without letting the loudest voice win: decision filters over backlogs.

Let’s find the first workflow.

Thirty minutes. Bring the process that eats the most time. You leave with a view on whether AI can help and what it would take.

Book a 30-minute call