Why does Phase 1 take two to four weeks rather than running it in parallel with build work?

Because deciding what to build before you have assessed readiness is the single most expensive mistake in AI adoption. Phase 1 produces the prioritised use case register, the governance gap analysis and the board-approved roadmap that make the next six months efficient. Skipping it usually means rebuilding work in month four because a governance issue or a data dependency was missed. Two to four weeks of disciplined assessment routinely saves three to six months of rework, and it gives the board the evidence they need to fund Phase 2 properly.

Can we skip the governance setup and go straight to a pilot?

No. The framework treats governance as a precondition for deployment, not an afterthought. AI in production without policy, DPIAs and a tool approval process creates real liability — under UK GDPR Article 22, ICO guidance and the EU AI Act as it applies to UK businesses. Governance establishment runs in the first month of Phase 2 specifically so the pilot in month two is defensible. The setup work is not heavy if started early; it becomes painful only when retrofitted to a system that is already live.

What does 'fractional AI director' actually mean in practice?

It means Simon Steggles operates as your AI Director on a fractional basis — typically one to two days a month after Phase 2 — covering board reporting, investment planning, governance review and capability direction. You get a senior AI leader without the cost of a full-time hire. The role is delivered for organisations that need strategic AI leadership but do not have the volume to justify a permanent appointment. It pairs naturally with an internal champion network so the day-to-day work continues between the director's sessions.

How does the framework handle change resistance from staff?

Change management is a workstream in Phase 2, not a side activity. It includes the communication plan, leadership messaging, resistance management and success story sharing. Specifically, staff training combines AI literacy with fear reduction workshops and a champion programme — three to five trained champions per fifty staff is the working ratio. The target outcome is organisation-wide AI adoption above 70% within six months. Resistance is treated as information about the rollout, not an obstacle to overcome by force.

What does an engagement cost?

Cost varies with organisation size and the scope of Phase 2. Phase 1 is a fixed-scope assessment delivered within two to four weeks; pricing is shared at the initial discussion. Phase 2 is scoped against the approved roadmap and the number of use cases in the first wave. Phase 3 is a fractional director engagement billed monthly. Every engagement begins with a free 30-minute strategic discussion to understand context and objectives. Call Simon on 07973 210 895 for a 30-minute consultation if you would like to scope what an engagement might look like for you.

Executive Resources · for UK SME leaders

AI Project Engagement Framework

The AI Project Engagement Framework is the structured methodology AI-Si.com applies to every implementation project. It runs in three phases: a two to four week strategic assessment, a three to six month implementation, and an ongoing optimisation phase that builds internal capability. Each phase names the deliverables, the responsible parties, the governance forums and the success metrics, so boards and procurement teams know exactly what an engagement looks like before any commitment is made.

Framework overview

An AI project engagement is a structured, time-bound collaboration between AI-Si.com and a client organisation to plan, deploy and embed AI capabilities that deliver measurable business value and sustainable internal capability. The framework prevents random AI activity. It replaces guesswork with a clear sequence: assess first, implement second, optimise third.

The three phases below summarise the full model. Phase 1 lasts two to four weeks. Phase 2 runs for three to six months. Phase 3 is ongoing, with the explicit goal of reducing reliance on external consultancy over time.

Phase 1: Assessment. Two to four weeks. Discovery, AI readiness audit, prioritised use cases, strategic roadmap.
Phase 2: Implementation. Three to six months. Pilot deployment, governance setup, staff training, iterative improvement cycles.
Phase 3: Optimisation. Ongoing. Performance monitoring, capability development, advanced use cases, strategic AI leadership.

Phase 1: Strategic Assessment (weeks 1–4)

Phase 1 establishes the foundation for all subsequent work. No AI implementation begins without completing it. 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.

The phase completes when 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.

Activity	Deliverable	Responsible Party
AI Readiness Audit	AI Readiness Report with 5-dimension scoring	AI-Si.com
Stakeholder interviews	Stakeholder map, pain points, AI opportunity register	AI-Si.com + client leadership
Use Case Prioritisation Workshop	Ranked use case register with ROI projections	AI-Si.com + client operations
Governance Gap Assessment	Governance requirements checklist + policy gap analysis	AI-Si.com
Technical Infrastructure Review	Integration requirements document	AI-Si.com + client IT
Strategic Roadmap	30 / 90 / 180-day AI implementation roadmap	AI-Si.com
Board presentation	Executive briefing deck	AI-Si.com

Phase 2: Implementation (months 2–7)

Phase 2 translates the approved roadmap into operational reality. Work proceeds in four-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.

The defining feature of this phase is that governance is established before any AI deployment, not afterwards. Policies, DPIAs and the tool approval process are operational by the end of the first month so that pilot deployment in month two has a defensible framework around it.

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

Phase 3: Optimisation and Independence (month 7+)

Phase 3 deepens capability, expands to new use cases, and builds the internal independence that reduces reliance on external consultancy. The goal is an organisation that can lead its own AI evolution with the right governance and strategic discipline still in place.

Four workstreams run in parallel: advanced AI capabilities (next-generation use cases identified in Phase 2, with custom development where standard tools are insufficient); internal capability building (champion network, internal leadership confidence, knowledge transfer); governance evolution (annual governance review as the minimum standard, with policy updates as tools and regulation change); and strategic AI leadership through the fractional AI director model — board reporting, investment planning, sector benchmarking and long-range capability direction.

What makes the framework work

The framework is not designed to generate activity for activity's sake. It is designed to create defensible value, visible progress and controlled adoption. Five principles run through every engagement.

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.

Engagement governance structure

Every engagement operates under a clear governance structure designed to maintain transparency, ensure accountability and resolve issues quickly. 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.com lead	Strategic direction, escalations, roadmap decisions
Implementation Review	Fortnightly	Project sponsor, department leads, AI-Si.com	Sprint progress, blockers, upcoming activities
Champion Network	Monthly	AI Champions, AI-Si.com director	Skills development, issue resolution, best practice sharing
Board Report	Quarterly	Board + CEO	KPI performance, ROI evidence, strategic AI update

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, so AI value is evidenced rather than assumed.

Productivity metrics cover time saved per automated process, output volume increase, error rate reduction, and staff hours reallocated from manual work to higher-value work. Financial metrics cover direct cost savings, revenue attributed to AI-improved processes, ROI against engagement investment, and payback period. Capability metrics cover staff AI confidence scores before and after training, tool adoption rates, the number of operational AI use cases, and internal champion competence.

Take the next step

Want help applying this to your organisation? Use the resource below or book a 30 minute strategy call with Simon — no pitch, just practical advice.

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