> ## 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.

# Opportunity Identification in Iris

> Use Iris evidence to develop, challenge and prioritise improvement opportunities without treating generated suggestions as final recommendations.

Iris can support opportunity development by connecting process evidence to potential interventions. Treat those opportunities as **inputs to consultant judgment**, not automatic client recommendations.

## Review every opportunity against five questions

1. **Evidence:** what observed problem supports it?
2. **Mechanism:** how would the intervention change the workflow?
3. **Value:** what measurable outcome should improve?
4. **Feasibility:** what systems, data and people are required?
5. **Risk:** what could fail, and where is human oversight needed?

## Challenge the intervention type

Before accepting an AI recommendation, test whether the better answer is:

* eliminate the task;
* simplify the process;
* standardise the input;
* integrate systems;
* use deterministic automation;
* use AI assistance;
* redesign ownership or policy.

## Convert the idea into a recommendation

Weak:

> Use AI for proposals.

Stronger:

> Generate a first-draft proposal from approved CRM, pricing and product information, while keeping commercial review before client release. The objective is to reduce drafting time and rework caused by missing standard information.

## Prioritise with the client

Assess impact, feasibility, risk, time to value, dependencies and evidence confidence. The client should understand why the first recommended project outranks the alternatives.

<Warning>
  Do not present an opportunity as validated simply because Iris surfaced it. Validation requires consultant review and relevant client evidence.
</Warning>