Service / AI System Advisory & Development

AI Awareness & Ethics Campaign

Build practical organizational understanding of responsible AI, acceptable use, risk, privacy, security and human accountability.

Business context

What this capability solves

Policies are ineffective if users cannot recognize risky AI behavior or understand when to escalate. Awareness must be role-based, recurrent and connected to real use cases.

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.

Audience Segmentation

Executives, business users, engineers, risk/legal, HR, operations and procurement.

Responsible AI Fundamentals

Fairness, transparency, accountability, privacy, security and limitations.

Acceptable Use

Safe public-GenAI behavior, data handling, IP, confidential information and approved tools.

Scenario Learning

Role-specific examples, dilemmas, incidents and decision exercises.

Campaign Cadence

Microlearning, communications, events, quizzes and reinforcement.

Measurement

Knowledge, behavior, policy acknowledgment and issue-reporting indicators.

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 adoption
Policy launch
Board/executive awareness
Developer responsible-AI training
GenAI user education
New regulatory requirement

Key deliverables

Awareness plan
Role curriculum
Campaign assets
Scenario library
Assessment/quiz
Metrics dashboard
Improvement actions

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

ParticipationKnowledge upliftUnsafe-use reductionPolicy acknowledgementIssue reportingRepeat error reduction