This is the deployment framework we use for every Skkyee engagement. It's been refined across 6+ industries and dozens of deployments. It's not theoretical — it's the actual process behind systems like HOW Waterproofing's AI transformation and Flamingo Aviation's lead automation.

The playbook is divided into three phases, each with clear deliverables, decision points, and red flags.

Phase 1 — Days 1–30

Scope & Audit

The goal of Phase 1 is to answer one question: What should we build, and what will success look like? Most failed AI projects skip this phase or rush through it.

Step 1: Operations Mapping

We map every process that touches the problem space — not just the obvious ones. At our own company HOW, this meant mapping the journey from "customer calls with a leak" to "10-year warranty tracking." We found 23 manual touchpoints where AI could intervene.

Step 2: Data Audit

We evaluate data availability, quality, and format against production requirements. This isn't a data science exercise — it's an operations exercise. We ask: "Can we get this data, in this format, at this frequency, in production?"

Step 3: Quick Win Identification

We identify 2–3 high-impact, low-complexity deployments that can ship within Phase 2. These quick wins build organisational confidence and generate early ROI to fund broader deployment.

Step 4: Success Metrics Definition

We define 3–5 business metrics (not model metrics) that will determine whether the deployment is successful. These are agreed upon with stakeholders before any building begins.

Red flags in Phase 1: No executive sponsor. Data lives in spreadsheets with no API access. Success metrics are vague ("improve efficiency"). The team can't articulate what happens when the AI is wrong.

Phase 2 — Days 31–60

Build & Test

Phase 2 is where the system takes shape. The goal is to have a production-ready system tested against real-world conditions — not a demo that works on curated data.

Step 1: Architecture Design

We design the system architecture with three non-negotiable constraints: manual override at every AI decision point, graceful degradation when any component fails, and zero vendor lock-in on core infrastructure.

Step 2: Agent Development

We build and train the AI agents — whether they're diagnostic chatbots, lead scoring engines, scheduling systems, or knowledge retrieval tools. Each agent is tested against the ugliest data we found in Phase 1.

Step 3: Integration Testing

We integrate with existing systems (CRM, ERP, communication channels) and test end-to-end flows. This is where most pilots break — the AI works in isolation but fails when connected to real infrastructure.

Step 4: Rollback Protocol Design

For every AI touchpoint, we define: What happens when it fails? How does the interaction route to a human? How quickly can we disable the AI component without disrupting the customer experience?

Red flags in Phase 2: Testing only with clean data. No rollback path defined. Integration treated as an afterthought. Building features nobody asked for during Phase 1.

Phase 3 — Days 61–90

Deploy & Optimise

Phase 3 is controlled rollout followed by rapid iteration. The goal is to go from "it works in staging" to "it's handling real customers and we have the data to prove it."

Step 1: Staged Rollout

We deploy to a controlled subset — typically 10–20% of traffic or a single team. This limits blast radius while generating production data for optimisation.

Step 2: Monitoring & Alerting

We set up real-time dashboards tracking the success metrics defined in Phase 1. Alerts fire when metrics deviate beyond acceptable thresholds. The team sees the same data we see — full transparency.

Step 3: Rapid Iteration

Based on production data, we run 2–3 optimisation cycles. This typically involves prompt refinement, edge case handling, and performance tuning. Each cycle is measured against our baseline metrics.

Step 4: Knowledge Transfer

We transfer operational ownership to your team with complete documentation: runbooks, monitoring guides, escalation paths, and training sessions. Your team should be able to operate and iterate on the system without us.

Red flags in Phase 3: Deploying to 100% of traffic on day one. No monitoring dashboard. No documented rollback procedure. "We'll figure out training later."

Decision Framework

At the end of each phase, there's a go/no-go decision. This isn't a formality — we've killed projects at Phase 1 when the data wasn't there, and we've paused at Phase 2 when integration revealed deeper infrastructure problems.

The discipline to stop is what separates a deployment framework from a slide deck.

Phase Gate Criteria

  • Phase 1 → Phase 2: Executive sponsor confirmed, data access validated, success metrics signed off, quick wins identified.
  • Phase 2 → Phase 3: All agents passing integration tests, rollback protocols documented, staging environment matching production conditions.
  • Phase 3 → Handoff: Success metrics met or exceeded, team trained, monitoring operational, runbooks complete.

Why 90 Days?

Shorter timelines (2–4 weeks) produce demos, not deployments. Longer timelines (6–12 months) lose organisational momentum and executive attention. 90 days is long enough to build something real and short enough to maintain urgency.

Every day past 90 days without a production system is a day where the organisation starts questioning whether AI works. We don't let that happen.

Ready to scope your deployment? Book a discovery call and we'll walk through Phase 1 together.