AI Project Engagement Framework
The structured framework governing all AI implementation engagements delivered by AI-Si Consultancy.
It sets out the phases, deliverables, responsibilities, governance model, and success criteria used to move organisations from AI curiosity to measurable operational value. This page is designed to help board leaders, senior managers, and procurement teams understand exactly how an AI-Si engagement works before any commitment is made.
This framework is built for clarity. It shows what happens first, what gets delivered, who owns each part, how risk is controlled, and what success looks like at each stage. It is designed to be board-friendly, implementation-ready, and easy to audit.
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 is delivered at each stage, how success is measured, and the governance structures that maintain 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.
In simple terms: the framework prevents random AI activity. It replaces guesswork with a clear sequence. Assess first. Implement second. Optimise third.
The Three-Phase Model
- Phase 1: Assessment. 2–4 weeks. Discovery, AI readiness audit, prioritised use cases, strategic roadmap.
- Phase 2: Implementation. 3–6 months. Pilot deployment, governance setup, staff training, 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, policy, data, 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 | 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 are documented and addressed before Phase 2 begins.
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 a 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 reduces reliance on external consultancy support. The goal is an organisation that can lead its own AI evolution with the right governance and strategic discipline still in place.
Advanced AI Capabilities
Explore next-generation use cases identified in Phase 2. Move into custom AI development where standard tools are insufficient.
Internal Capability Building
Develop the champion network, internal leadership confidence, and knowledge transfer required to reduce external dependency.
Governance Evolution
Update policies and controls as tools, risk exposure, and regulation evolve. Annual governance review is the minimum standard.
Strategic AI Leadership
Board-level reporting, AI investment planning, sector benchmarking, and long-range capability direction through the fractional AI director model.
What Makes the Framework Work
- Clear sequencing. No jumping into tools before governance and use-case prioritisation are done.
- Board-ready reporting. Progress is visible, explainable, and commercially grounded.
- Change management built in. Staff adoption is treated as a workstream, not an afterthought.
- Measured outcomes. AI activity is tied to KPIs, ROI, and operational impact.
- Sustainable capability. The framework is designed to leave the client stronger, not dependent.
5. Engagement Governance Structure
Every engagement operates under a clear governance structure designed to maintain transparency, ensure accountability, and resolve issues quickly. This matters for execution, but it also matters for trust. Stakeholders need to know where decisions are made, who attends each forum, and how escalation works.
| 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. This ensures AI value is evidenced rather than assumed.
Productivity Metrics
Time saved per automated process. Output volume increase. Error rate reduction. Staff hours reallocated from manual work to higher-value work.
Financial Metrics
Direct cost savings. Revenue attributed to AI-improved processes. ROI against engagement investment. Payback period.
Capability Metrics
Staff AI confidence scores before and after training. Tool adoption rates. Number of operational AI use cases. Internal champion competence.
Commercial point: the framework is not designed to generate activity for activity’s sake. It is designed to create defensible value, visible progress, and controlled adoption.
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