This module teaches professionals to map an enterprise AI capability landscape by durable capability category rather than by product or vendor. Learners build an AI capability inventory, establish what counts as a capability before counting anything, and extend the catalogue past centrally sanctioned deployments to account for ungoverned AI adoption, unsanctioned tool use, and AI tool sprawl. They score each capability on independent axes instead of a single blended figure, distinguish a capability inventory from an AI maturity or readiness assessment, classify every shortfall by its structural gap condition, and rank gaps using multi-input prioritization. The module also builds the vocabulary to separate AI from rules-based automation, to place any system in a durable class — machine learning, deep learning, generative AI, or agentic AI — and to discuss capability trade-offs, build-versus-buy decisions, and platform fit with executives without overpromising. Skills covered include AI capability mapping, capability gap analysis, AI landscape assessment, capability scoring, and AI transformation advisory communication. Suited to consultants, business analysts, AI strategy leads, and digital transformation professionals.

This module builds a tool-agnostic mental model of the deliverables an AI advisory engagement produces, from an early opportunity assessment through a roadmap, a business case, and a trial report to a final executive recommendation, and places each document against the transformation stage it supports. Learners separate an advisory artifact from a report by naming the decision it must enable, who holds that decision, and what happens if the answer is no. They distinguish delivery deliverables from the AI governance documentation an organization must maintain: system inventories, ownership records, oversight specifications, and audit logs. They also see how phase gates, engagement scoping, stakeholder decision rights, and the commercial form of a consulting contract change what completion means. The module teaches the arc of consulting deliverables and the purpose of each, not the method for producing any of them.

AI engagement scoping decides whether an advisory project delivers value or drifts. This module teaches consultants, business analysts, and project leads how to frame and scope an AI project from the start: clarifying business objectives, mapping stakeholders, gathering requirements, and setting a scope boundary of inclusions and exclusions that a later change request can be tested against. Learners practise stakeholder analysis and stakeholder alignment, expectation management with executive sponsors, and the vocabulary work that keeps scope creep out of AI initiatives. The module also covers change control, escalation paths, and the consultant's scope of authority — which questions an advisor answers directly, and which belong to a specialist. Throughout, the emphasis falls on what makes AI projects distinctive: uncertain outcomes, data readiness dependencies, and technical capability that keeps shifting while the engagement runs.

AI vendor due diligence is what separates an advisor who protects a client's plan from one who repeats what the market is saying. This module builds practical due diligence, vendor evaluation, and technology risk assessment skills for consultants, business analysts, and decision makers who assess AI vendors, products, and claims. Learners practise evaluating vendor claims and marketing claims, reading a capability claim back to the measurement conditions that produced it, telling a demonstration or proof of concept apart from a production deployment, and questioning benchmark methodology and headline statistics before a plan is built on them. The module also covers technology vendor selection, third-party and counterparty risk on multi-year platform commitments, supplier viability and financial durability, and evidence-based recommendation writing — turning an honest reading of the evidence into a short advisory note an executive can act on.