Service / AI System Advisory & Development

AI Readiness Assessment

Assess organizational, technical, data, regulatory, security and operating readiness before scaling AI investment.

Business context

What this capability solves

AI programmes fail when strategy assumes capabilities that are not present: poor data quality, weak integration, unclear ownership, limited skills, no evaluation discipline or unmanaged compliance 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.

Strategy Readiness

Business priorities, value hypotheses, sponsorship and portfolio governance.

Data Readiness

Quality, ownership, access, lineage, privacy and retrieval/training suitability.

Technology Readiness

Cloud/compute, model access, integration, MLOps/LLMOps, observability and security.

Governance Readiness

Policies, inventory, risk classification, impact assessment and human oversight.

People Readiness

Product, engineering, risk, legal, operations and change-management capability.

Regulatory Readiness

Applicable AI, privacy, sector and cross-border requirements and evidence gaps.

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

Pre-AI transformation assessment
GenAI enterprise adoption
AI platform investment
Regulated AI programme
Agentic AI readiness
Board investment decision

Key deliverables

Readiness maturity score
Domain heatmap
Risk and dependency register
Target-state capability map
Quick wins
12–24 month roadmap
Investment priorities

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

Readiness maturityCritical dependency closureData readiness coverageAI skill coverageGovernance readinessPilot-to-scale conversion