Service / AI System Advisory & Development

End-to-End AI Project Management

Govern AI delivery from business case and data readiness through build, assurance, deployment, adoption and benefits realization.

Business context

What this capability solves

AI projects combine product, data, engineering, risk, vendor and change-management dependencies. Conventional PM alone may miss model evaluation, data provenance, governance gates and lifecycle risk.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Business Case

Value hypothesis, baseline, scope, stakeholders, constraints and acceptance criteria.

Integrated Plan

Data, platform, model, application, security, governance, testing and change workstreams.

Delivery Governance

RAID, dependencies, decision log, stage gates, scope/change and executive reporting.

AI Quality Gates

Data readiness, model evaluation, safety/security, privacy and business acceptance.

Go-Live Readiness

Support, monitoring, rollback, incident, ownership and handover.

Benefits Realization

Adoption, quality, productivity, risk and financial outcomes after launch.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Business & Governance

Business objectives, accountable owners, risk appetite, policy, use-case portfolio and investment priorities.

AI Engineering Lifecycle

Data, model, prompt/RAG, agent, evaluation, release, monitoring and retirement controls.

Trust & Assurance

Risk/impact assessment, security, privacy, explainability, human oversight, testing and evidence.

Enterprise Operations

Integration, observability, incident handling, change governance, model/agent lifecycle and continuous improvement.

Controls & governance

Named business and technical owner
Use-case risk classification and approval gates
Data provenance, minimization and access control
Human accountability for high-impact outcomes
Security and privacy-by-design controls
Versioned model/prompt/agent configuration
Pre-release evaluation and red-team gates
Continuous monitoring, incident and change control
Audit-ready evidence and management reporting

Priority use cases

Enterprise AI implementation
GenAI assistant
AI analytics platform
Agentic workflow
AI modernization
Multi-vendor AI programme

Key deliverables

Project charter
Integrated plan
RAID/dependency register
Decision log
Gate pack
Go-live checklist
Benefits scorecard

Integration considerations

Enterprise IAM and workload identity
Data lake/warehouse and vector/RAG platforms
Model/API providers and private models
Application/API integration layer
MLOps/LLMOps/AgentOps and observability
SIEM/SOAR and security tooling
GRC, privacy and evidence repositories
ITSM/BPM and business workflow systems

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. AssessInventory use cases, systems, stakeholders, data, risks, maturity and constraints.

2. DesignDefine target architecture, governance, controls, delivery backlog and acceptance criteria.

3. BuildDevelop or configure AI capabilities, integrations, controls, evaluation and operating procedures.

4. AssureTest quality, safety, security, privacy, explainability and business acceptance before release.

5. OperateMonitor outcomes, drift, incidents, changes, cost, risk and control effectiveness through BAU governance.

Outcome and KPI framework

On-time milestoneGate pass rateCritical dependency agingPilot-to-productionAdoptionBenefits realized