Rollout Plan 2026 - AI Advisory Integration Journey

Welcome

Thank you for choosing AI Advisory. This document walks you through how we will activate the service in your environment together. It covers the five phases of the rollout, what each side is responsible for at each step, and how we make sure the experience is right for your customers before going live.

The rollout is collaborative by design. We bring the AI configuration, deployment, and validation expertise; you bring the environment, data, and use-case knowledge. Each phase ends with a clear checkpoint so we both know we're ready to move forward.

The five phases at a glance

Indicative timeline. Each phase nominally runs for approximately one week — putting a standard rollout at around five weeks end to end. When your data is already prepared, stakeholders are aligned, and no blockers surface in any phase, the full rollout can be completed in as little as two weeks. Pace depends on your data readiness, internal review turnaround, and the depth of customisation we agree together.

How to read this document

For each phase, you will find: a short description of the goal, an indicative duration, a side- by-side view of what your team and ours will do, the checkpoint that marks the phase complete, and the items we proactively monitor together. The final page summarises next steps.

P H A S E 0 1

Staging preparation

We confirm that your environment is ready for AI Advisory and that your product or content data can support meaningful, accurate answers. This is the foundation everything else builds on.

WHAT EACH SIDE DOES

PHASE COMPLETE WHEN
Staging access is verified, technical compatibility is documented, and a sample of your data passes our quality check.

WHAT WE WATCH FOR TOGETHER

Staging that drifts from production. We agree a parity confirmation upfront so what we test reflects what your customers will see.

Data not yet AI-ready. If the data audit highlights gaps, we hold the gate and address them — better to fix here than after launch.

Late legal or privacy review. We surface this in Phase 01, never as a Phase 04 surprise.

P H A S E 0 2

Proposal & initial integration

We design the AI Advisory specifically for your context — its scope, conversation patterns, sample queries, placement, and tone — and then we deploy a first working version into your staging environment.

WHAT EACH SIDE DOES

PHASE COMPLETE WHEN
The first version of AI Advisory is visibly active in your staging environment and baseline tests pass.

WHAT WE WATCH FOR TOGETHER

Misalignment between proposal and use case. We hold a 30-minute walk-through before deployment so you can shape the proposal early.

Embed conflicts with your security policies or analytics. The Phase 01 fit check minimises this; we additionally test on a sandbox subdomain when needed.

Scope expanding before v1 is even live. We lock the proposal scope here and route additional ideas into Phase 03, where we have a structured way to evaluate them.

P H A S E 0 3

Iteration & feedback

This is where we tune AI Advisory to your real use cases. Your team tests with internal users, we collect structured feedback, and we deploy the adjustments to staging within a single focused iteration round.

WHAT EACH SIDE DOES

PHASE COMPLETE WHEN
You confirm that AI Advisory addresses the agreed use cases and there are no blocking change requests open.

WHAT WE WATCH FOR TOGETHER

An open-ended feedback loop. We time-box this phase to a single iteration round. Anything beyond is re-scoped explicitly together rather than drifting.

Edge cases drowning out core use cases. We weight feedback by the use-case priorities we agreed in Phase 01.

Tester profile too narrow. We agree upfront on minimum tester coverage so the feedback represents your real audience.

P H A S E 0 4

Validation & readiness

Before going live, we validate AI Advisory against the success criteria we set together at the start. You run user acceptance testing with your stakeholders. We provide a readiness report and prepare the production deployment.

WHAT EACH SIDE DOES

PHASE COMPLETE WHEN
Sign-off document is signed, the readiness report is shared, and the rollback plan is acknowledged on both sides.

WHAT WE WATCH FOR TOGETHER

Late-arriving approvers. We agreed the stakeholder map in Phase 01 specifically to avoid
surprises here.

"Works in staging, behaves differently in production." We run an environment parity check before sign-off.

Subjective validation criteria. We rely on the numerical thresholds defined in Phase 01 — relevance, latency, fallback rate — rather than impressions.

P H A S E 0 5

Production rollout

We go live. After deployment, we run a short heightened-support period — known as hypercare — during which we monitor closely and respond quickly. Once the service is stable, we transition into standard support.

WHAT EACH SIDE DOES

WHAT WE WATCH FOR TOGETHER

Deployment timing risk. We agree a deployment window with a change-freeze around launch — no last-minute Friday surprises.

Production load behaving differently. We run a pre-launch load test in staging at production-equivalent traffic.

Unexpected post-launch issues. The rollback procedure prepared in Phase 04 means we have a tested fallback if anything needs immediate attention.

What happens next

If you are reading this in preparation for the rollout, here is how we will get started.

Immediate next steps

• Confirm the technical and business owners on your side who will be the day-to-day contacts.
• Agree the staging environment we will work in and the access method.
• Schedule the kickoff session — typically 60 minutes — to align on success criteria, the stakeholder map, and the data preparation plan.
• Review and confirm the success metrics for Phase 04 validation (response relevance, latency, fallback rate, UX targets).

Through the rollout

Given the compressed one-week-per-phase pace, we hold short check-ins at the start, midpoint, and end of each phase, with daily touchpoints during the hypercare period in Phase 05. All decisions and changes are documented and shared with you in real time.

Beyond go-live

After the hypercare period, AI Advisory transitions into standard support. We agree a cadence for future updates — most customers prefer a lightweight refresh path for content and configuration changes, with the full staging-to-production cycle reserved for larger updates.