Proof, before promises.

01 / AI Opportunity Audit

The audit is designed to answer a practical question: where can AI reduce friction without weakening customer trust?

Decision brief

Start with the bottleneck, not the technology. The sample assessment begins with a constrained discovery sprint rather than approving a full implementation on assumptions.

Opportunity → evidence → decision

The sample portfolio examines service operations, catalog enrichment, voice-of-customer, marketing adaptation, and demand forecasting. The illustrative rubric is explicitly not a client result. The recommendation remains conditional on verified evidence.

“Remove one measurable bottleneck without weakening customer trust.”

Controls and measurement

Measurement centers on handling time, first-contact resolution, grounded-answer rate, escalation precision, agent adoption, and customer-trust signals. Controls include an approved-source registry, access minimization, a human approval gate, an evaluation set, rollback, and privacy/vendor review.

02 / Customer Service Copilot Implementation Plan

A six-week human-in-the-loop pilot turns a validated opportunity into a working, governable system.

The operating principle

Build the smallest system that can earn its keep. The sample copilot drafts grounded answers from approved policy, product, and order sources while keeping the agent responsible for the final decision and customer relationship.

Bounded implementation

The pilot is deliberately narrow: retrieve approved information, summarize order context, draft answers for selected intents, suggest escalation reasons, and capture feedback. Autonomous customer messaging, refunds, credits, cancellations, policy overrides, and unapproved sources remain out of scope.

Acceptance framework

The sample framework evaluates quality, safety, escalation, speed, adoption, and trust. Example thresholds include grounded-answer accuracy ≥90%, zero critical policy/privacy failures, escalation ≥95%, median handling-time improvement ≥15%, useful draft rate ≥70%, and agent confidence ≥4/5.

Handoff

The implementation ends with documentation, source ownership, configuration notes, test cases, operating instructions, an escalation matrix, an admin runbook, training, and an evidence-backed scale/revise/stop decision.

“Make the evidence easier to see, not accountability harder to locate.”

What these artifacts demonstrate

Discovery replaces assumptions with evidence. Implementation converts that evidence into a bounded system, explicit controls, acceptance criteria, and an ownership path.

The deliverable is not a glossy strategy document. It is a decision system someone can actually operate.