> ## Documentation Index
> Fetch the complete documentation index at: https://irisdocs.prescientlabs.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Implementation & Expansion

> Turn validated diagnostic findings into a clearly scoped first implementation project and a longer-term account roadmap.

The diagnostic should create a decision, not simply a report.

If the evidence supports action, the natural next question is:

**Which opportunity should the client implement first, and who should deliver it?**

## Build an implementation backlog

For each opportunity record:

* desired business outcome;
* current-state evidence;
* proposed intervention;
* owner;
* scope;
* technical dependencies;
* process dependencies;
* data requirements;
* human oversight;
* implementation effort;
* expected value;
* success KPI.

## Select the first project

Prefer a project with a strong combination of value, evidence, contained scope and a credible path to production.

Avoid choosing a flashy initiative purely because it demonstrates sophisticated AI.

## Decide delivery ownership

You may:

* implement the work yourself;
* use a specialist partner;
* work alongside the client’s internal team;
* recommend a vendor and remain as advisor;
* stop after diagnosis if implementation falls outside your capability.

Credibility increases when you are explicit about your limits.

## Transition talk track

> Of the opportunities we identified, we recommend starting with X because it has the strongest combination of evidence, expected value and manageable implementation complexity. The next step would be to define the production scope, dependencies, success measures and delivery ownership.

## Keep diagnosis and implementation commercially distinct

Unless explicitly included, detailed production design and build work should be a separate scope and fee.