AI Project Engagement Framework
The structured framework governing all AI implementation engagements delivered by AI-Si Consultancy — setting out phases, deliverables, responsibilities, and success criteria.
1. Framework Overview
The AI Project Engagement Framework is the structured methodology applied to all AI implementation projects delivered by AI-Si Consultancy. It defines how we engage with client organisations, what we deliver at each stage, how success is measured, and the governance structures that ensure accountability throughout.
An AI project engagement is a structured, time-bound collaboration between AI-Si Consultancy and a client organisation to plan, deploy, and embed artificial intelligence capabilities in a way that delivers measurable business value and sustainable internal capability.
Phase 1: Assessment
2–4 weeks. Deep discovery, AI readiness audit, use case prioritisation, and strategic roadmap development.
Phase 2: Implementation
3–6 months. Pilot deployment, governance setup, staff training, and iterative improvement cycles.
Phase 3: Optimisation
Ongoing. Performance monitoring, capability development, advanced use cases, and strategic AI leadership.
2. Phase 1 — Strategic Assessment (Weeks 1–4)
Phase 1 establishes the foundation for all subsequent work. No AI implementation begins without completing Phase 1. This phase identifies exactly where AI can deliver value in your specific context, and what governance and infrastructure changes are required before deployment.
| Activity | Deliverable | Responsible Party |
|---|---|---|
| AI Readiness Audit | AI Readiness Report with 5-dimension scoring | AI-Si |
| Stakeholder Interviews | Stakeholder map, pain points, AI opportunity register | AI-Si + Client leadership |
| Use Case Prioritisation Workshop | Ranked use case register with ROI projections | AI-Si + Client operations |
| Governance Gap Assessment | Governance requirements checklist + policy gap analysis | AI-Si |
| Technical Infrastructure Review | Integration requirements document | AI-Si + Client IT |
| Strategic Roadmap | 30/90/180-day AI implementation roadmap | AI-Si |
| Board Presentation | Executive briefing deck (PowerPoint) | AI-Si |
Phase 1 completion criterion: The client board or senior leadership team has reviewed, understood, and approved the AI Strategic Roadmap. Any significant concerns have been documented and addressed before Phase 2 commences.
3. Phase 2 — Implementation (Months 2–7)
Phase 2 translates the approved roadmap into operational reality. Work proceeds in 4-week implementation sprints, with each sprint focused on one or two specific AI use cases or governance workstreams. Monthly steering committee reviews track progress and resolve blockers.
| Workstream | Key Activities | Target Outcome |
|---|---|---|
| Governance Establishment | AI policy drafting, DPIAs, oversight structure setup, tool approval process | Governance framework operational before any AI deployment |
| Pilot AI Deployment | Select and deploy first AI use case in controlled environment. Measure baseline vs outcome. | First AI use case delivering measurable value within 90 days |
| Staff Training | AI literacy programme, fear reduction workshops, champion identification and certification | 90%+ staff confidence. 3–5 trained champions per 50 staff. |
| Change Management | Communication plan, leadership messaging, resistance management, success story sharing | Organisation-wide AI adoption above 70% within 6 months |
| Second Use Case Rollout | Apply learnings from pilot. Deploy second prioritised use case with refined approach. | Two operational AI use cases by end of Phase 2 |
| Performance Review | Quarterly KPI review, ROI measurement, lessons learned documentation | Documented ROI evidence for board reporting |
4. Phase 3 — Optimisation & Independence (Month 7+)
Phase 3 focuses on deepening capability, expanding to new use cases, and building the internal independence that removes reliance on external consultancy support. The goal is an organisation that can lead its own AI evolution.
5. Engagement Governance Structure
Every engagement operates under a clear governance structure designed to maintain transparency, ensure accountability, and resolve issues quickly.
| Forum | Frequency | Participants | Purpose |
|---|---|---|---|
| Steering Committee | Monthly | CEO/MD, Operations Lead, AI-Si lead | Strategic direction, escalations, roadmap decisions |
| Implementation Review | Fortnightly | Project sponsor, department leads, AI-Si | Sprint progress, blockers, upcoming activities |
| Champion Network | Monthly | AI Champions, AI-Si director | Skills development, issue resolution, best practice sharing |
| Board Report | Quarterly | Board + CEO | KPI performance, ROI evidence, strategic AI update |
6. How We Measure Success
Success metrics are agreed at the start of each engagement and reviewed at monthly steering committee meetings. All metrics are quantified against a documented baseline established in Phase 1.
Productivity Metrics
Time saved per process automated. Output volume increase. Error rate reduction. Staff hours reallocated from manual to higher-value work.
Financial Metrics
Direct cost savings. Revenue attributed to AI-improved processes. ROI calculation against engagement investment. Payback period.
Capability Metrics
Staff AI confidence scores (before/after training). AI tool adoption rates. Number of operational AI use cases. Internal champion competency.
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