AI & ML Solutions

A convincing demonstration does not establish production value. Data access, evaluation, failure handling and operating costs determine whether an AI feature is useful.

Move from a use-case question to an evaluation and integration plan. Compare model or retrieval options, define acceptable failure handling and agree human oversight before a production rollout.

1. 01

   Identify the decision or workflow to improve and a non-AI baseline.

2. 02

   Check data rights, privacy, access controls and evaluation examples.

3. 03

   Compare suitable model and retrieval approaches against quality, latency and cost criteria.

4. 04

   Agree human escalation, release thresholds and ongoing evaluation responsibilities.

AI can assist suitable drafting, exploration or repetitive work. The agreed process must retain engineering review, meaningful validation and a responsible release owner. Tool and data permissions are agreed before using project material.

• What does an acceptable answer or prediction look like?
• Which errors must trigger a human review?
• Who owns the source data and ongoing evaluation?

If requirements are still uncertain, start with discovery and scoping.

How do you decide whether an AI use case is worth building?

Define the workflow and a non-AI baseline, then evaluate representative examples against quality, latency, cost and failure-handling criteria. Check source-data rights and access before choosing an approach. A successful demonstration alone does not establish production value.

What happens when an AI feature gives an unreliable answer?

Define unacceptable errors, human escalation and fallback behaviour during scoping. Agree evaluation thresholds and ongoing monitoring responsibilities before release. Model outputs require validation; an AI feature cannot be assumed accurate in every situation.