The Non-Intelligent Knowledge Base

Across most organizations, 20–30% of IT and HR tickets are knowledge base related. These aren’t complex edge cases - they’re repeat questions, known issues, and policy clarifications that should already be documented.

And yet, they keep generating tickets.

This isn’t because companies don’t invest in knowledge bases. Most do. The problem is that traditional knowledge management doesn’t scale, even with modern tools.

The core challenges of maintaining an IT & HR knowledge base

On paper, knowledge bases make sense. In practice, maintaining them is one of the hardest operational problems inside IT and HR - and usually the first thing to fall off the priority list.

1) Knowledge goes stale quickly

IT environments change constantly: tools, configurations, permissions, security requirements. HR content evolves constantly with policies, regulations, benefits, and organizational changes.

But KB articles are often written once and rarely revisited.

The result:

  • Outdated setup guides
  • Incorrect policy explanations
  • Answers that no longer reflect reality

Once employees encounter wrong information, trust erodes and the KB stops being used.

2) Ownership is unclear - or centralized and overloaded

In most organizations, KB ownership falls into one of two broken models:

  • No clear owner: Articles are written by SMEs who later move on.
  • A dedicated KB owner or team: A person (or small team) is responsible for keeping everything up to date.

The second model sounds better, but it doesn’t scale.

A centralized KB team:

  • Can’t keep up with the pace of change across IT and HR
  • Relies on others to report updates, without institutional knowledge
  • Becomes a bottleneck for accuracy and freshness

Even with the best intentions, the KB slowly drifts away from reality.

3) Discoverability is poor

Even when the right content exists, employees often can’t find it:

  • Inconsistent terminology and tagging
  • Long, unstructured articles
  • Employees searching in natural language rather than internal jargon

When answers aren’t found in seconds, users default to opening tickets.

4) Duplication and inconsistency

Over time, KBs accumulate:

  • Multiple articles answering the same question
  • Conflicting instructions
  • Different tones, formats, and levels of detail

This creates confusion and reduces confidence in the KB as a source of truth - prompting users to ignore it altogether.

5) Manual maintenance simply doesn’t scale

Review cycles, audits, approvals, pruning old content - all require sustained human effort.

As the company grows, the KB grows faster than the team maintaining it.

Eventually, maintenance loses.

Why enterprise search platforms aren’t enough

Over the last few years, a new category of tools has emerged: enterprise search and AI-powered knowledge discovery platforms.

Platforms like Glean excel at:

  • Unified search across many internal systems
  • Quickly surfacing existing documents and conversations
  • Helping employees discover information they didn’t know where to look for

For information discovery in large, fragmented organizations, this category is a major step forward.

Where the category falls short for knowledge bases

The challenge is that enterprise search is not knowledge creation or maintenance.

Search tools:

  • Surface what already exists
  • Index both good and bad content equally
  • Depend entirely on the quality of the underlying data

They don’t:

  • Identify missing knowledge
  • Generate new KB articles
  • Keep content up to date
  • Resolve duplicates or contradictions
  • Manage the knowledge lifecycle

If the KB is outdated, incomplete, or inconsistent, search simply exposes those problems faster.

AI answers still rely on broken inputs

Even AI-powered Q&A features depend on existing content.

If the KB:

  • Doesn’t cover a topic
  • Contains outdated policy
  • Has conflicting answers

Then AI responses risk being inaccurate - which is especially dangerous in HR and compliance-related scenarios.

Discovery ≠ ticket deflection

Most importantly, these tools operate after tickets already exist.

They don’t:

  • Analyze why tickets are being created
  • Learn from how issues are resolved
  • Feed real-world resolutions back into the KB

They help people find information, but they don’t fix the system that keeps producing repetitive tickets.

How AI can actually solve the knowledge base problem

Knowledge base learnings

AI becomes transformative when it’s used not just to search knowledge - but to create and maintain it automatically.

Instead of treating the KB as a static library maintained by humans, AI turns it into a living system that learns from real operational data.

1) Knowledge gaps analysis

AI can continuously analyze:

  • The existing knowledge base
  • Thousands of historical IT and HR tickets
  • How those tickets were actually resolved

From this, it can identify:

  • Questions that repeatedly generate tickets
  • Topics with no KB coverage
  • Articles that exist but don’t resolve issues

This replaces manual audits with continuous, data-driven insight into what knowledge is missing or broken.

2) Automated knowledge base generation

Once gaps are identified, AI can:

  • Generate new KB articles based on real ticket resolutions
  • Improve existing articles using proven fixes
  • Standardize structure, tone, and clarity
  • Keep articles updated as systems and policies change

The KB evolves automatically - and stays grounded in reality, not assumptions.

3) Fully autonomous, zero-configuration impact

This approach can run fully autonomously:

  • No manual tagging
  • No hand-written articles
  • No dedicated KB maintenance team
  • No long setup or configuration

By continuously learning from new tickets and resolutions, AI can deflect 20–30% of total IT and HR ticket volume, often in less than 24 hours.

The bottom line

Traditional KBs fail because they rely on people to:

  • Predict what knowledge will be needed
  • Manually document it
  • Keep it accurate forever

Search and discovery tools are excellent at helping employees find information - but they are not designed to create, maintain, or fix knowledge bases.

AI changes the model entirely:

  • Knowledge is derived from real work, not guesswork
  • Maintenance is continuous, not manual
  • Ticket resolution feeds directly back into documentation

For the first time, a high-quality IT and HR knowledge base doesn’t require heroic effort. It can run itself.