Service / AI System Advisory & Development

ESG & AI Alignment Advisory

Align AI strategy, implementation and reporting with sustainability, responsible technology and broader enterprise ESG objectives.

Business context

What this capability solves

AI creates both sustainability opportunity and impact: energy use, hardware footprint, workforce implications, inclusion, transparency and governance all affect responsible adoption.

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.

Materiality Mapping

Identify AI-related environmental, social and governance impacts relevant to the organization.

Responsible AI Linkage

Connect fairness, transparency, accountability, privacy and security to ESG governance.

Energy / Compute View

Track workload, infrastructure and efficiency considerations for material AI systems.

Social Impact

Assess workforce augmentation/displacement, accessibility, inclusion and customer impact.

Supplier Considerations

Review model/cloud/AI suppliers for relevant governance and sustainability dependencies.

Reporting Integration

Translate AI metrics into sustainability and board reporting where material.

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

AI sustainability strategy
Responsible AI programme
Board ESG/AI reporting
AI infrastructure planning
Supplier governance
High-impact workforce automation

Key deliverables

AI-ESG materiality map
Responsible technology principles
Impact indicators
Supplier questions
Management dashboard design
Improvement roadmap

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

Material AI systems assessedCompute efficiency trendResponsible AI control coverageSupplier evidence coverageSocial-impact actionsGovernance reporting cadence