How Well Does Your Support Team Use AI?
A scored profile across the dimensions that decide whether AI in support reduces ticket volume and improves CSAT — or just adds another tool to the stack.
- A scored profile across 6 dimensions — see exactly where you're strong and where the gaps are.
- Your biggest opportunities, mapped to specific next moves.
- A personalized video walkthrough from Shawn (optional) — a real read on your results.
Most support orgs don't lose ground on AI because the tools are bad — they lose ground because the leverage never compounds. The chatbot at the front of the queue is scripted, the knowledge base AI would have to ground in is stale, agents draft replies from scratch, and AI QA is running on a sample instead of every ticket. An honest read on support AI leverage looks past the demos at what actually moves ticket volume and CSAT: how much of the easy stuff customers resolve themselves, how much of each ticket the agent doesn't have to write, how accurately tickets reach the right person, and whether common, well-understood issues resolve without a human in the loop.
This free assessment scores your support org across six dimensions and returns a clear leverage profile in about six minutes. It's built from 27 years of technology leadership across Fortune 500 and growth-stage companies — the same lens a fractional Chief AI Officer would bring to your first conversation about deflection, agent assist, routing, quality assurance, and autonomous resolution.
What support AI leverage actually measures
Leverage is a profile, not a single number. The assessment scores six dimensions independently so you can see where AI is already paying off and where the gaps are: Ticket Deflection & Self-Service (is the front door resolving instead of routing), Agent Productivity & Augmentation (is AI drafting, summarizing, and looking things up for agents on every ticket), Routing & Prioritization (do intent, sentiment, and urgency get tickets to the right person fast), Quality Assurance & Voice-of-Customer (is every ticket scored and are themes reaching product), Self-Healing Workflows (do common issues resolve without an agent), and Tooling & Integration (are the AI features in your helpdesk turned on and does your stack share context). The final question maps which support workflows to automate first.
Why most support AI investments underperform
The pattern is consistent across mid-market support orgs: a chatbot that deflects single-digit percentages, an agent assist tool no one uses, AI QA on a small sample, and a helpdesk full of native AI features no one has configured. Each piece captures partial value; none of them roll up to a measurable change in ticket volume, handle time, or CSAT. Agents tab between systems, the knowledge base ages out, and product never sees the friction signal sitting in support data. The orgs that capture real value treat support AI leverage as an integration problem first — get the knowledge base healthy, connect two or three high-leverage workflows, prove the lift, and earn the right to roll out more. A leverage profile turns a vague AI ambition into a sequenced plan, and it tells you whether your constraint is content, integration, agent adoption, or decision speed.
What you get at the end
You'll see an overall Customer Support AI Leverage Score, a band that describes where you stand (from Pre-Foundation through Execution-Ready), a per-dimension breakdown, and a map of your highest-value automation opportunities across deflection, agent augmentation, routing, QA, self-healing, and tooling. From there you can request a personalized video walkthrough — a short, recorded read on your specific results and what a fractional Chief AI Officer engagement would do for your support motion. No generic sales deck.
Frequently asked questions
What is a customer support AI leverage assessment?
It's a structured evaluation of how much of your support motion is being amplified by AI today and where the biggest unreclaimed leverage is. Rather than measuring AI knowledge, it measures the practical workflows — deflection, agent assist, routing, QA, autonomous resolution, and tooling integration — that determine whether AI is moving ticket volume and CSAT or quietly sitting as shelfware.
How is this different from an AI readiness assessment?
A general AI readiness assessment looks at the whole company's preconditions. This one is scoped to the things a head of support or VP of customer experience actually controls and is measured on: deflection rate, agent productivity, routing accuracy, QA coverage, autonomous resolution, and the AI features inside the support stack. The questions, scoring, and opportunity map are framed in the language of support — tickets, queues, handle time, CSAT — not generic AI strategy.
How long does it take?
About six minutes. It's 18 scored questions across six dimensions, two short financial-context questions, and a final workflow-mapping question across deflection, augmentation, routing, QA, self-healing, and tooling. Your progress auto-saves, so you can leave and resume without losing answers.
Who should take this?
VPs of customer support, heads of customer experience, support operations leads, and senior support managers weighing AI investment who want a clear-eyed read on where the leverage actually is — and what to fix first if the AI you've already bought isn't producing results. It's also useful for fractional support leaders and PE operating partners scoping support AI work for a portfolio company.
What do most support orgs score?
Most support orgs land in the Emerging Leverage or Pre-Foundation bands on the first run — usually because the knowledge base isn't healthy enough for AI to ground in, AI QA is running on a sample, or the helpdesk's native AI features aren't configured. That's not a failing grade; it's a useful diagnosis, and it tells you where the next dollar of support AI investment should go.