AI System Advisory & Development • AA-01

AI Strategy & Readiness

Decide where AI is worth doing before deciding how.

Scope

What the engagement covers.

Enthusiasm produces pilots; strategy produces returns. This service assesses readiness across data, skills, platform and governance, then sequences a portfolio the organization can actually deliver.

Included capabilities

AI readiness assessment across data, platform, skills, governance and culture
Use-case discovery, value sizing and feasibility screening
Portfolio sequencing with dependencies and capability build-out
Build, buy or partner analysis per use case
ESG and AI alignment advisory where sustainability commitments apply

Outputs and deliverables

AI readiness and maturity assessment report
Use-case inventory with value and feasibility scoring
Responsible AI strategy and sequenced roadmap
Operating model and investment plan

Workflow

How it is delivered, step by step.

Each step has an owner, an entry condition and an artefact that has to exist before the next step begins.

01AssessReadiness across data, platform, skills, governance and delivery capacity.

02DiscoverUse-case pipeline from business units with value and feasibility scoring.

03PrioritizeSequence by value, dependency, risk and capability required.

04PlanRoadmap with investment profile, capability build and governance gates.

05MobilizeOperating model, funding route and first delivery increment.

Use cases

Where this is typically applied.

Use case 01

Boards asking where AI investment should go first

Use case 02

Organizations with many stalled pilots and no production systems

Use case 03

Groups needing one strategy across several business units

Delivery model

The operating pattern for AI System Advisory & Development.

The same delivery discipline applies across every capability in this line, so combined engagements stay coherent.

Discover

Inventory use cases, models, data, vendors, users and regulatory context.

Classify

Assess materiality by impact, autonomy, data sensitivity and deployment context.

Design

Governance, roles, policy, controls, lifecycle gates and evidence requirements.

Test

Risk assessment, architecture review, red teaming and scenario exercises.

Implement

Controls in delivery workflows, platforms, models and human oversight.

Sustain

Reporting, incidents, change, re-assessment, training and improvement.

Integration

Model registry, feature store and MLOps pipelines for lifecycle gates.
GRC platform for AI control mapping, evidence and issue management.
Data catalogue and lineage tooling for dataset provenance.
Security stack for logging, monitoring and incident handling of AI systems.

Engagement approach

Baseline engagements are a readiness and risk assessment. Build engagements operationalize the framework. Assurance engagements test models, LLM applications, agents and the governance controls around them, then validate remediation.

Other capabilities in AI System Advisory & Development.

View the full AI System Advisory & Development line →

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