Service / AI System Advisory & Development

Enterprise AI Integration

Integrate AI safely into enterprise applications, data platforms, APIs and business workflows without creating unmanaged shadow architecture.

Business context

What this capability solves

AI value depends on access to real workflows and data, but integration introduces identity, authorization, data leakage, resilience and operational dependencies.

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.

Integration Architecture

API/event patterns, workflow boundaries, data access, model gateway and failure handling.

Identity / Authorization

User and workload identity, delegated access, least privilege and transaction authorization.

Data / RAG Integration

Approved data sources, retrieval authorization, metadata, lineage and content lifecycle.

Workflow Integration

BPM/ITSM/CRM/ERP actions, human tasks, approvals and compensation logic.

Reliability

Timeout, fallback, retry, queueing, circuit breaker, graceful degradation and observability.

Operational Handover

SLOs, support, incident, change, cost and ownership model.

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

CRM copilots
ERP automation
Knowledge assistants
Service desk AI
Analytics/data assistants
Enterprise agent deployment

Key deliverables

Integration blueprint
API/tool contracts
Identity model
Data/RAG access design
Reliability patterns
Test plan
Support/runbook

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

Integration success rateUnauthorized access blocksWorkflow completionFallback successLatency/SLOOperational incident rate