The hidden problem with AI discovery calls

After years of working with SaaS and automation projects, I noticed a recurring pattern.

A consultant holds a discovery call with a client.

The conversation reveals important insights:

• manual processes
• workarounds
• wasted hours
• bottlenecks
• systems that don’t communicate

By the end of the call, everyone sees the potential for improvement. But then something odd happens.

The consultant finishes with a page of notes and a vague feeling that there is a lot to automate.

However, the real opportunities are not clearly defined.

Later, when the proposal is written, the consultant has to piece together the project logic:
What should be automated?
How big is the opportunity?
How valuable is it?
What is the pricing?

Key insights from the call often get overlooked, and proposals frequently depend on gut feelings instead of solid evidence.

I began to question why the discovery phase, which should be the most valuable part, is still handled this way. This led me to create something to organize that moment.

Auditic transforms discovery conversations into:
• structured opportunities
• impact scoring
• ROI modeling
• proposal foundations

The aim is not to replace consultants. It is to help them capture and organize the insights they already gather.

I would love to hear from those in AI consulting or automation:
How do you currently turn discovery conversations into specific projects?