AI System Advisory & Development • AA-06
AI Solution Implementation
Take responsible AI requirements all the way into working systems.
Scope
What the engagement covers.
Governance that never reaches production is theatre. This service delivers the system itself, with lifecycle management, security and enterprise integration built in from the start.
Included capabilities
- End-to-end AI project delivery with defined stage gates
- Model lifecycle management: registry, versioning, promotion and retirement
- AI security implementation across training and inference environments
- Explainable AI implementation where decisions affect people
- Enterprise integration and agentic AI implementation on AAIOS
Outputs and deliverables
- Working system in production against agreed acceptance criteria
- Model registry with lineage, versioning and promotion records
- Security testing and remediation evidence
- Operating runbooks and retraining 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.
01FrameBusiness outcome, success metrics, constraints and acceptance criteria.
02BuildData pipeline, model development and evaluation against the metric.
03SecureControls, testing and red teaming before any production exposure.
04IntegrateConnection into business systems, workflows and human processes.
05Hand overRunbooks, monitoring, retraining plan and capability transfer.
Use cases
Where this is typically applied.
Replacing a manual process with a governed automated one
Integrating AI into an existing enterprise application estate
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.
Related
Other capabilities in AI System Advisory & Development.
AA-01
AI Strategy & Readiness
Decide where AI is worth doing before deciding how.
AA-02AI Risk, Compliance & Assurance
Identify, evaluate, treat and evidence AI risk in a form auditors accept.
AA-03Secure AI Lifecycle Development
Build the controls into the pipeline rather than reviewing at the end.
AA-04Explainability & Human Oversight
Make automated decisions explainable to the person they affect.
AA-05AI Capability Development
Give decision-makers and builders the skills the strategy assumes they have.