Scope
Name the business question, domains, evidence, exclusions and accountable reviewers.
AI + Data Assessment · Method preview
The proposed AI and Data Assessment applies Gibson's evidence-to-action method to an agreed cross-system scope. It maps the current state, grades the evidence, records findings and prioritises action without assuming that AI, automation or a build is the answer.
This page explains the method. It is not yet a fixed public assessment, price or delivery promise.
Proposed assessment flow
Name the business question, domains, evidence, exclusions and accountable reviewers.
Document the current process, systems, handoffs, controls and failure points.
Separate verified records, supported statements, assumptions and open evidence gaps.
Challenge material findings against representative cases and human operating needs.
Prioritise stop, improve, integrate, test or separately scoped production decisions.
Method provenance
Albert Triolo's experience spans enterprise technology at Westpac, transformation work across South32, Ansell and Transport for NSW, and organisation-wide transformation risk advice at Deloitte. That history explains the evidence, control and accountability discipline in the method.
Questions before a workflow discussion
Not as a fixed public product. The assessment remains owner-gated while the representative report, final evidence corpus, examination criteria, secure evidence handling, commercial depth and realistic delivery standard are completed. AI Consulting is the current governed path.
The proposed scope can cover lead flow, CRM and administration, training, complaints, evidence, compliance and an AI or automation profile. A final engagement would examine only the domains and evidence agreed in writing.
The proposed Assessment Pack contains an evidence register, current-state or cross-system map, findings, priorities and an action plan. A sample report and final product truth must be approved before that output is sold as a standard public offer.
No. The method compares process, rule, integration and AI options against the same operating constraint. A valid outcome can be to stop, improve the current process, run a bounded test or separately scope production.