How to Fix Knowledge Management for Support [Real Examples]

The job of customer service, whether through self-service or live support, is taking the knowledge a company holds about its products, policies, and processes and getting it to customers when they need it. Every ticket, chat, and call is fundamentally a knowledge delivery problem.

When a customer has to contact support for something they should have been able to handle themselves, they register it as a problem, even if the resolution goes well.

Support knowledge isn't like other company knowledge. It's not a wiki for internal collaboration or a shared drive of reference docs. Support knowledge has correct answers. It serves an external audience. And when it's wrong, the cost is immediate and measurable. 

This guide covers what effective knowledge management looks like for customer support, where most teams go wrong, and practical steps to build a system that works for your agents, customers, and AI system. We also spoke with knowledge managers at companies like Meta, Checkr, Cash App, Bolt, and TravelPerk to hear how they're tackling these challenges firsthand. Their insights are woven throughout.

Why Knowledge Is the Foundation of Customer Service

A customer shows up with a question or a problem, and support's job is to answer it using what the company knows. Everything else, from training to tooling to AI, builds on that foundation.

What Bad Knowledge Costs You

When answers are hard to find or follow, customers often open tickets for issues they could've resolved on their own. Each of those tickets costs money, since an agent now has to handle a request that self-service should have covered.

It can cost customer satisfaction, too. A customer who self-serves successfully will often move on without a second thought. One who has to contact support is more likely to feel like something went wrong, even when the agent handles the request well.

Then there's the failure you never see. Some customers who can't find an answer simply give up. They never reach out, they never get help, and the company never hears about it.

Customer service agents pay a price as well. When the right information is hard to find, they spend less time resolving cases and more time searching. Answers may also start to vary from rep to rep, which makes the experience less consistent for customers.

"I would really like to have an AI coach for agents so they don't have to spend so much time searching for knowledge. The helper would be there for them to type a question and get an answer from the knowledge in seconds."

Jelena Stosic, Head of Knowledge Management - Customer Support, Bolt

All of this negatively affects the metrics support leaders are measured on, like average handle time, first call resolution, and customer satisfaction (CSAT) scores. 

What Makes Support Knowledge Different from Other Company Knowledge

Support knowledge differs from general company knowledge in three ways. 

Support knowledge has right and wrong answers. General company knowledge is often collaborative. Teams develop ideas together in wikis, shared docs, and internal tools, and the content evolves as their thinking does.

Support knowledge doesn't work that way. There's a correct way to handle each customer situation, and the documentation needs to capture it precisely so every agent resolves cases the same way.

The audience differs as well. Other company knowledge exists to help employees do their jobs. Support knowledge serves customers, people who have a stake in the business but no inside context. They won't forgive a wrong answer the way a colleague might, and every wrong answer puts a customer relationship at risk.

Support knowledge also absorbs far more change. An internal process doc might only need a review when a tool or team changes. 

Knowledge for support faces pressure from many directions at once, including product launches, policy updates, pricing changes, and new regulations. Edge cases from real customer conversations add to the pile, so the content can fall out of date quickly without constant maintenance.

The stakes differ as well. Imagine nobody can find the holiday calendar in the company wiki. It's a minor annoyance, and it stays internal.

Now imagine an agent quoting an outdated refund policy to a customer. The company now has to choose between honoring terms it no longer offers or walking back what its own agent said. Either path costs something, whether it's revenue, an escalation, or the customer's trust.

"No KM role is going to be the same. It may have the same title and job description, but the role is heavily impacted by the company's culture, tools, industry, stage, and priorities."

Christianne Beasley, Principal, Knowledge Management, Getty Images

The 5 Knowledge Problems Most Support Teams Face

Every support team runs into knowledge problems eventually. The details vary by company, but the failure modes repeat everywhere, from small support teams to global contact centers. Here are the five we see most often, along with what each one looks like in practice.

1. Knowledge Is a Destination, Not a Part of the Workflow

Companies often treat their customer support knowledge base like a filing cabinet. The documents exist, but anyone who needs one has to walk over and find the right drawer. In practice, agents leave their ticketing system to search a separate tool, and customers leave your app or website to dig through a help center.

That extra step discourages use. When people find it hard to reach the information they need, many stop reaching for it. Agents may invent answers, ask a colleague, or muddle through on their own. Customers who can't find an answer quickly may give up or open a ticket instead.

The goal is to put knowledge in the right place, at the right time, for the right person. Delivery tends to improve along a clear progression:

• A knowledge base exists (good)
• The knowledge base is accessible inside the agent workspace or the customer-facing product (better)
• Knowledge surfaces based on ticket context or user behavior (even better)
• Knowledge adapts to the customer's product, plan, or region (best)
• Knowledge automates actions on behalf of the agent or customer (ideal)

We built Stonly with this progression in mind. It plugs directly into ticketing systems like Zendesk, Salesforce, and Freshdesk, and it can surface relevant guides based on ticket context and customer data. Agents get the guidance they need for each ticket without leaving their workflow.

"The best learning always happens contextually. While the knowledge base has always been important, it's better for agents and customers to have something that is accessible directly in their work environments and not somewhere that they have to search for an article."

Katarzyna Wagner, Global Knowledge Management Lead, Jobbatical

2. The Knowledge Format Doesn't Match How It's Used

The typical knowledge base revolves around articles. The format works well for some content, but plenty of what agents and customers need won't fit it. Support knowledge generally comes in two types, and each needs a different format:

• Reference knowledge covers product specs, policy details, and account information that agents look up and relay to customers. Articles handle this well.
• Process knowledge covers the step-by-step procedures agents follow in situations like returns, escalations, and troubleshooting. Long articles handle this poorly, because the agent has to read the whole document and pull out the parts that apply.

Few customer interactions are simple lookups. More often, the customer needs help doing something, and the agent needs to follow a process to do it well.

A new hire, a 10-year veteran, and an outsourced rep should all deliver the same customer experience. They can only do that when processes exist in a format that agents can follow in the moment.

The table below maps common knowledge types to formats. 

3. Knowledge Is Outdated or Incomplete

Support content has a short shelf life. Products evolve, policies change, and new edge cases surface every week. But the documentation rarely keeps up.

Knowledge teams tend to be small relative to the volume of content they manage. Once the library grows past a certain point, keeping everything current becomes harder to manage.

"The product side of the business is moving at lightning speed, while the legal and compliance side usually takes two to three weeks. We've been in survival mode to get new content out there instead of maintaining what we create."

Olivia Overstreet, Content Lead, Cash App

Many teams accept that part of their library is outdated at any given moment. The challenge is finding that content and fixing it before a customer relies on it.

The bigger risk is that agents can't tell when an article has gone stale. An agent working from an outdated knowledge article can follow it carefully and still give the customer the wrong answer. They believe they followed the correct process, so they never flag the issue, and the error can repeat with the next ticket.

"Business travel is continuously evolving, so we need to be able to keep up with content maintenance across over one thousand articles in our library."

Fabrizio Vena, Content and Communications Manager, TravelPerk

4. No Visibility Into How Knowledge Is Used (or Isn't)

Most teams publish content without tracking whether it gets used. Without that data, they fix whatever someone complained about most recently instead of what hurts performance the most.

This lack of visibility gets worse as the library grows. A team managing hundreds of articles and dozens of processes can't review everything by hand. It needs usage data to show which content to fix first.

"The most important element of all metrics we track is the correlation between what we input (the knowledge we create) and what agents output (the actual performance)."

Marjorie Etter, Global Training, Knowledge & Change Management Leader, Meta

Views, thumbs-up, and thumbs-down ratings won't provide that evidence. They show that people opened or rated an article, but not whether the article solved the problem. You want to know whether the knowledge helped the agent resolve the ticket and whether the customer walked away with the right result.

"In my previous organizations, articles would have an upvote and a downvote, which didn't tell me anything about how to improve the content. It just told me that they weren't happy with the answer if they downvoted it. Now, if an agent or customer downvotes an article, we're capturing open text feedback to take action."

Sarah Regner, Senior Manager, Operations & Business Systems, Checkr

5. Knowledge Is Scattered Across Too Many Places

Picture a new agent's first week. The pricing details are in the knowledge base, the workaround for a known bug is in a Slack thread, and the escalation policy is in a doc somebody emailed around last quarter. Before this agent can figure out what the answer is, they have to figure out where to look.

Veteran agents cope by memorizing where everything is stored, but siloed information still slows them down. Every tool they have to open adds time to the resolution.

Scattered knowledge also creates duplication. When teams document the same answer in several places, the copies drift apart over time. Eventually, nobody knows which version is current. Keeping the copies aligned means editing every one of them whenever something changes, and few teams keep up with that.

"We're in the process of overhauling our knowledge right now. We're putting everything into one source of truth, establishing proper categories, and understanding and improving the search based on how our teams use it. We're trying to understand whether the knowledge is 1) relevant, 2) up to date, and 3) from the right source."

Clare Santos, Director, Knowledge & Quality, Customer Experience, Aviso

What Effective Knowledge Management Looks Like

Effective knowledge management looks different at every company, but strong systems share a few traits regardless of size or industry.

A Single Source of Truth

Even when you serve two audiences, keep everything in one platform. Companies often build an external knowledge base for customers and an internal knowledge base for agents. The two fall out of sync over time. Eventually, the answer a customer reads stops matching the answer an agent gives.

Keep one underlying content base instead. Then tailor what each audience sees from it. Customer knowledge stays public and simple. Agent knowledge adds a layer of internal processes.

Knowledge in the Right Format for How It's Used

Match each piece of content to the way people will use it. Reference material, like pricing details or policy terms, works as searchable articles or FAQs. Processes, like handling a return or troubleshooting a setup issue, work better as step-by-step guides that agents and customers follow in real time.

The platform you choose needs to support more than articles. Look for one that also lets you build interactive guides, which show each person only the steps that apply to their situation. 

Embedded in the Agent Workflow

Good knowledge is accessible where people already work. Agents should see relevant guides inside their ticketing system or customer relationship management (CRM) tool, matched to the ticket they're handling. Customers should find help inside the app or website, on the page where the issue came up.

The best systems deliver knowledge proactively. Say a customer gets stuck on a checkout page. A proactive system notices and offers a checkout guide right there, before the customer opens a ticket.

Stonly's customer self-service software handles this delivery with an in-app widget and no-code triggers. You choose which guide appears based on the page someone is on and what they're doing there. You can also use customer data, like their plan or region, to personalize what they see.

Built for Governance and Continuous Improvement

Knowledge management is an ongoing process, so build the upkeep into the system. Start with content reviews. The person who knows a topic best often works outside the knowledge team, like a legal expert for regulated content or a product manager for a new feature. Your review workflow should route each piece of content to that person before it goes live.

The right amount of review varies. One company we spoke with counted 13 departments that could need to weigh in on a single piece of content, while a smaller company might need one reviewer. Both setups work as long as the workflow matches how the company makes decisions.

In addition to reviews, a healthy system supports four ongoing practices:

• Agent feedback loops: Agents should be able to flag wrong or missing content in seconds. The simpler the process, the more agents will use it.
• Usage analytics: Track which content people use, which they ignore, and which queries return nothing.
• Gap identification: Watch for ticket categories with no matching content, and fill those content gaps before they cause escalations.
• Content health monitoring: Flag content that nobody has reviewed in a set period, and check for duplicates, conflicts, and broken links.

"We have a value across our customer-facing teams that we are all responsible for our documentation. If we see something wrong, we should update it immediately if it's an easy fix, or tag it if it's a more complex change."

Anna Udziela-Macha, Technical Support Lead, Stonly

Knowledge-Centered Service

Knowledge-centered service (KCS) puts frontline agents at the center of knowledge creation and maintenance. Instead of treating documentation as a separate task, agents update the knowledge as a byproduct of resolving tickets.

Imagine an agent resolving an unusual ticket. They notice the related article skips a step. Under KCS, they fix the article or flag it before moving on. The knowledge improves a little with every case the team handles.

Full adoption takes real commitment, and plenty of teams stop short of it. The core habits pay off either way. Encourage agents to check the knowledge base before answering, flag problems when they find them, and contribute updates rather than just consuming content.

"We have a KCS-influenced authoring model, which puts the ability to edit content directly in the hands of the frontline customer support folks. They have the most context, so it translates to the most usable content. KCS is a way to drive internal engagement with the knowledge base and get our support team excited to use it. It's also a way to scale our ability to manage content without hiring more people."

Evan Pitonzo, Manager, Knowledge Management, Compass

Why Knowledge Matters Even More With AI

Knowledge management used to serve two audiences: customers and agents. AI tools make a third. And they consume knowledge differently than people do.

Your AI Is Only as Good as Your Knowledge

AI agents, chatbots, agent assist tools, and copilots all pull their answers from your knowledge base. They inherit whatever they find there, including the outdated articles and the conflicting answers.

An experienced agent can work around imperfect documentation because they sense when an article looks wrong and check with a colleague before using it. 

AI has no such filter. It reads what's there and answers with full confidence.

This changes the math on knowledge investment. Cleaning up the knowledge base used to make agents somewhat faster. Now it can determine whether your AI resolves tickets or invents answers.

"AI has the tremendous ability to help customers and support staff by giving them answers instead of articles. Ensuring the source of information it's pulling from is accurate is my team's top priority and one that makes a good knowledge management program a must for any business looking to use this groundbreaking technology."

Evan Pitonzo, Manager, Knowledge Management, Compass

Making Knowledge AI-Ready

Not all knowledge is equally useful to AI. Natural language processing lets AI read any article you give it. A long, unstructured article still forces the AI to guess which parts apply to the question at hand.

Structured content removes that guesswork. Say your refund process changes based on the payment method.

An article describes every variation in prose. The AI has to infer which one applies. A step-by-step guide with explicit decision logic walks it down the correct branch instead.

Preparing knowledge for AI is good content hygiene. Consolidate scattered content, remove duplicates, and then break processes into clear steps. A new agent benefits from that work for the same reason the AI does. Neither one knows your company's unwritten context.

For a deeper look at structuring and governing knowledge for AI, see our guide to AI knowledge management.

Choosing a Knowledge Management Platform

At a certain scale, support teams outgrow informal documentation. The content volume becomes too large to maintain by hand. And the business starts needing knowledge to resolve issues on its own, before customers open tickets, which is when a dedicated knowledge management system earns its cost. 

Management Capabilities vs. Knowledge Impact

Platform evaluations are split into two qualities: management and impact.

Management capabilities cover the daily work of running a knowledge program, from content creation to review workflows to usage analytics. Knowledge impact covers the results, like higher self-service rates, stronger agent performance, and more accurate AI.

Plenty of platforms handle the management side well, but far fewer make the knowledge itself more effective. Prioritize the second kind. Improvements there show up in numbers you already track, like ticket volume, resolution time, and self-service rate.

What to Look For

When you evaluate a platform, check for the capabilities that produce those measurable results.

Build Knowledge That Drives Better Support

Stonly is an AI knowledge management software platform built for customer service teams. It combines articles and interactive guides, delivers them where agents and customers work, and keeps the whole library healthy as it grows.

Support teams use Stonly to:

• Create knowledge in the right format: Write reference content as articles and process content as step-by-step guides that adapt to each customer's situation.
• Deliver knowledge in context: Surface the right guide inside Zendesk, Salesforce, or Freshdesk based on the ticket type and customer data, so agents get answers without searching.
• Power effective self-service: Help customers resolve issues on their own through an in-app widget, a help center, and AI-powered search.
• Maintain the library with Knowledge Agents: AI monitors your knowledge base, spots gaps and inconsistencies, and drafts updates for human review.
• Serve every audience from one content base: Customers, agents, and AI work from the same governed knowledge, so answers stay consistent across channels.
• Connect knowledge to outcomes: Track how knowledge gets used and correlate it with agent metrics like handle time and first-contact resolution.

Request a demo to see how Stonly builds knowledge that gets used and makes a measurable impact.