Netcall Adds AI Tools to Liberty Spark for Process Discovery

What happened

Netcall has introduced three new AI-powered capabilities within Liberty Spark, its business process improvement platform. The additions are designed to help organisations capture institutional knowledge, search existing documented processes, and convert approved requirements directly into working applications.

The update targets a specific point in the digital transformation lifecycle: before organisations build new automation or AI-driven workflows, they first need an accurate picture of how processes actually run today. Netcall positions these capabilities as a way to surface that knowledge more efficiently than manual documentation and review methods allow.

Why it matters

As more organisations move to adopt AI across operations, a recurring barrier is that process knowledge is often undocumented, outdated or scattered across teams. Liberty Spark's new capabilities address this gap directly, aiming to make it faster to identify what processes exist, how they work, and where they can be translated into requirements and, ultimately, deployable applications.

This reflects a broader shift in enterprise AI tooling: rather than focusing purely on generative output, vendors are increasingly building capability around the groundwork organisations need — knowledge capture, searchability and requirements-to-build pipelines — before automation or AI can be reliably layered on top.

The Renascence take

The interesting part of this announcement isn't the AI layer itself — it's the admission, implicit in the product design, that most organisations don't actually know how their own processes work.

Every transformation programme stalls at the same point: nobody can agree what the current process actually is, because it lives in people's heads, old wikis and tribal memory rather than anywhere searchable. Tools that capture and surface that knowledge aren't glamorous, but they fix the real bottleneck — not a lack of AI, but a lack of accurate ground truth to point it at. Operators chasing AI-driven service improvement should treat process documentation as the prerequisite work, not an afterthought; skipping it just automates confusion faster.