Getting Customers Off the Ground with Knowledge Management: What Actually Works

MaryG · 2 hours ago

Getting Customers Off the Ground with Knowledge Management: What Actually Works

After working with customers across industries and maturity levels, one thing is consistent:
Knowledge Management doesn’t fail because of missing features. It fails because teams focus on the wrong outcomes.

Customers often believe they need more content, more cleanup, or more advanced capabilities. What they actually need is clarity—about purpose, sequencing, and what “success” looks like, especially as AI becomes part of the KM equation.

Whether an organization is launching a brand-new knowledge base or trying to stabilize an existing one, the path forward is the same.

New Knowledge Base or Cleanup? The Work Is the Same

A common misconception is that starting fresh is fundamentally different from cleaning up an existing knowledge base.

It isn’t.

From a KM and AI-readiness standpoint, the tools, decisions, and requirements are identical. The difference is emotional—greenfield feels hopeful; cleanup feels overwhelming.

In both scenarios, customers must answer the same questions:

Who owns knowledge and quality?
What makes an article useful—not just complete?
How will users find what they need?
What signals will AI rely on to surface the right answers?

You can’t shortcut these decisions by starting new, and you can’t avoid them by rewriting old content.

Usage Is the Only Metric That Matters

Another common trap is equating KM success with volume.

A knowledge base with 2,000 cleaned-up articles is not better than one with 200 that are actually used. In many cases, it’s worse.

An effective knowledge base is defined by whether content is:

Found
Understood
Used
Trusted

If content can’t be found—or isn’t helpful once it is—it isn’t relevant. Polished language and perfect formatting don’t matter if users bypass knowledge entirely.

Knowledge Center: Visibility, Not a Silver Bullet

Knowledge Center is often treated as the KM experience, but in practice it’s the starting point, not the finish line.

Used well, it:

Centralizes visibility across knowledge
Reinforces lifecycle behaviors
Makes gaps and duplication visible

What it doesn’t do is fix unclear ownership, inconsistent structure, or unhelpful content. It exposes reality—and that’s exactly its value.

Setup: Where KM Maturity Is Established

In the field, setup is where KM efforts either stabilize—or quietly fail.

Effective setup forces alignment on things teams tend to skip:

Ownership models
Content standards
Metadata that actually matters
How success will be measured

Setup isn’t about configuration checklists. It’s about establishing expectations and behaviors that search, AI, and adoption depend on. Customers who rush through this step often come back later asking why AI Search or Virtual Agent isn’t working as expected. The answer is almost always the same: the foundation wasn’t set.

Knowledge Graph: An Accelerator, Not a Starting Point

Knowledge Graph is powerful—and frequently misunderstood.

It doesn’t create meaning. It amplifies the signals already present in your content and data.

It delivers value when:

Content is consistently structured
Metadata is intentional and reliable
Relationships already exist or can be inferred accurately

Introduced too early, Knowledge Graph adds complexity without clarity. Introduced at the right time, it dramatically improves relevance, discoverability, and AI-driven recommendations.

Clean Content No One Uses Is Still Noise

One of the hardest mindset shifts for customers is realizing that cleanup alone doesn’t create value.

I’ve seen organizations spend months rewriting content only to find:

Search behavior didn’t change
AI experiences remained inconsistent
Users continued to avoid knowledge

Why? Because the focus was on hygiene, not usefulness.

AI won’t fix content that can’t be found or doesn’t help. It will amplify the problem.

Where to Start: Proven Accelerators That Build the Right Foundation

For customers looking for a practical, low-risk way to get moving, the most effective place to start is with focused accelerators designed to establish strong KM and AI foundations:

Jumpstart Your Knowledge Management

Establishes ownership, standards, structure, and expectations—the core foundation every KM program needs.

TuneUp Your AI Search

Improves relevance and predictability by aligning content structure, metadata, and search behavior.

TuneUp Your Virtual Agent

Ensures knowledge is usable in conversational experiences by focusing on clarity, intent alignment, and answer quality.

These accelerators work whether a customer is starting fresh or cleaning up years of existing content. They focus on outcomes, not volume—and they create the conditions AI depends on to work effectively.

Knowledge AI Readiness Is the Real Goal

AI doesn’t care whether your knowledge base is new or ten years old.
It cares whether the signals are clear.

Knowledge AI readiness is built on:

Predictable structure
Meaningful metadata
Clear ownership
Content written to answer real questions

When those elements are in place, AI-powered experiences work—regardless of how or when content was created.

The Real Measure of Success

The goal of Knowledge Management isn’t to create more content.
It’s to reduce friction.

An effective knowledge base is one people rely on because it helps them—consistently. Everything else is noise.

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