AIAI Capable

Methodology

Observe the business. Bound the decision. Keep a person accountable.

AI is useful for classification, extraction, drafting, prediction, and exception support. It should not quietly become the source of truth for pricing, availability, refunds, eligibility, or other consequential promises.

  1. 01

    Observe

    Start with real calls, leads, tasks, documents, and outcomes.

  2. 02

    Normalize

    Give inputs consistent fields, states, owners, and definitions.

  3. 03

    Decide

    Use deterministic rules for promises, prices, eligibility, and risk.

  4. 04

    Assist

    Use AI for classification, extraction, drafting, and bounded recommendations.

  5. 05

    Escalate

    Send uncertainty, exceptions, and consequential decisions to a person.

  6. 06

    Measure

    Tie the workflow to time, qualified leads, bookings, revenue, cost, and errors.

Evidence classes

  • Verified fact

    A source, system, document, or owner can support it now.

  • Reasonable inference

    The evidence points this way, but it still needs testing.

  • Unverified assumption

    The brief depends on it and labels it before action.

  • Projection

    A modeled possibility, never booked revenue or collected profit.

Approval boundaries

AI may assist

  • Classification
  • Extraction
  • Drafting
  • Summarization
  • Pattern detection

Human approval stays

  • Prices and discounts
  • Availability and bookings
  • Payments and refunds
  • Compliance-sensitive messages
  • Policy exceptions

Decision brief

Every recommendation must be testable and auditable.

Inputs and system of record
Rules and AI responsibilities
Human approval and escalation
Failure handling and audit trail
Outcome metrics and baseline
Kill criteria and next review