Making knowledge findable with AI

In progress. Target: deflect 15–20% of incidents.

Role: Architect · Platform: ServiceNow · Status: in testing

Context

Answers lived in three places: the knowledge base, file servers and shared document sites. There was no single place to look.

Problem

When people couldn't find an answer, they called the help desk. That meant extra tickets, extra agent time, and answers that weren't always right.

Decision

We tested a lot before settling on an approach: large hosted models against local ones, different file types, ways to re-rank search results, and out-of-the-box connectors. No single strategy won. A mix worked best for our use case, and we're still refining it.

The first fix wasn't AI at all. It was the same thinking as our developer guidelines: fix the bucket before adding water.

What we're building

  • Governance first: rules for how knowledge is written, and fewer places where it's allowed to live.
  • A vector database built from those approved sources.
  • Hybrid search that combines meaning-based and keyword matching.
  • ServiceNow connected to that search, so answers reach people where they already ask for help.

Status

In testing. We're aiming to deflect 15–20% of incidents. Real numbers aren't in yet, and I'll update this page when they are.

What I've learned so far

There's no single right way to do RAG; the best approach depends on the use case. The more time you spend planning the setup and defining the goal, the better the AI works for you.