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Knowledge Manager

Implement and manage enterprise search

Enhances✓ Available Now

What You Do Today

Configure search across knowledge systems, optimize for relevance, manage synonyms and best bets, track search effectiveness

AI That Applies

AI optimizes search ranking, understands natural language queries, surfaces answers from unstructured content, learns from behavior

Technologies

Enterprise search AINLP query understandingRelevance optimization

How It Works

The system ingests unstructured content as its primary data source. NLP models process the text input by identifying entities, classifying intent, and extracting the structured information needed for downstream decisions. The output — answers from unstructured content — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Search becomes dramatically better. AI understands questions and finds answers across all knowledge repositories

What Stays

Search strategy, deciding what content should be authoritative, managing information governance across search

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for implement and manage enterprise search, understand your current state.

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Map your current process: Document how implement and manage enterprise search works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Search strategy, deciding what content should be authoritative, managing information governance across search. These are the boundaries AI won't cross.
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Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Enterprise search AI tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long implement and manage enterprise search takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

“What data do we already have that could improve how we handle implement and manage enterprise search?”

They're prioritizing which operational processes to automate

your process improvement or lean lead

“Who on our team has the deepest experience with implement and manage enterprise search, and what tools are they already using?”

They understand the workflow dependencies that AI tools need to respect

a frontline supervisor

“If we brought in AI tools for implement and manage enterprise search, what would we measure before and after to know it actually helped?”

They see the daily reality that AI tools need to fit into

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.

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