Launching a new knowledge-management platform can feel like a major achievement. The content has been migrated. Search is working. Agents have been trained. The new system is live. Then the harder part begins.
Products change. Policies are updated. New customer questions appear. Experienced employees develop workarounds. Old articles remain searchable long after the information they contain has become outdated.
Over time, the knowledge base can become less reliable without anybody deliberately breaking it.
That is why effective contact centre knowledge management needs to be treated as an ongoing operational discipline rather than a one-off technology implementation.
The first article in this series explored what customer-service teams should compare when selecting knowledge-management software. This follow-up focuses on what happens next: how organisations keep knowledge accurate, searchable and useful enough that agents continue to trust it.
Knowledge Management Is a Living System
A knowledge base is never really finished. Every change to:
- Products
- Pricing
- Processes
- Regulation
- Customer journeys
- Technology
- Internal responsibilities
…can potentially change the information agents need.
That creates a continuous lifecycle:
Create → approve → publish → use → review → update → retire
The problem occurs when organisations invest heavily in the first three stages and under-resource everything afterwards.
Capacity’s current guidance on contact centre knowledge management makes a similar point, describing KM as an ongoing function rather than a one-time launch and emphasising ownership, feedback loops and continuous improvement.
Capacity – Knowledge Management Best Practices – https://capacity.com/blog/knowledge-management-best-practices/
Knowledge Principle
The value of a knowledge article declines the moment the business changes but the article does not.
The challenge is therefore making change visible quickly enough to keep content aligned with reality.
Assign an Owner to Every Important Knowledge Area
One of the simplest governance improvements is giving knowledge clear ownership.
An article about refunds might belong to:
Customer Operations
A payment-security procedure might belong to:
Risk & Compliance
Technical troubleshooting might belong to:
Product Support
The exact structure matters less than ensuring somebody is responsible for answering:
- Is this still correct?
- Does it need updating?
- Who approves changes?
- When should it be retired?
Without an owner, outdated content can remain available indefinitely because everybody assumes somebody else is maintaining it.
A mature knowledge-management system should ideally record:
- Content owner
- Last review date
- Next review date
- Version
- Approval status
Governance Insight
Don’t create a generic owner called:
“Knowledge Management Team.“
That team can govern the process, but subject-matter experts should remain accountable for the accuracy of the information itself.
Use Different Review Cycles for Different Content
Not every knowledge article carries the same risk.
A generic explanation of how to reset a user preference may remain accurate for years.
A regulatory process or pricing rule could change several times within a year.
Review schedules should therefore reflect content risk.
For example:
High-risk content
- Regulation
- Payment processes
- Identity verification
- Customer eligibility
- Complaints
- Pricing
Review frequently
Medium-risk content
- Product procedures
- Technical support
- Delivery processes
Review periodically
Low-risk content
- Stable company information
- General navigation
- Long-standing FAQs
Review less frequently
This prevents knowledge teams wasting time checking stable content while higher-risk material becomes outdated.
Keep Articles Short Enough to Use During a Live Contact
A knowledge article can be technically complete and operationally useless.
Imagine an agent dealing with an upset customer while searching a 2,000-word policy document for one sentence.
Contact centre content should usually be written for retrieval and action.
A useful structure might be:
Question
What problem does this solve?
Answer
The core information immediately.
Steps
What should the agent do?
Exceptions
When does the normal process not apply?
Escalation
When should someone else become involved?
Detailed source material can still be linked beneath the operational guidance.
The goal is not to remove complexity from the policy itself.
It is to stop every agent having to reinterpret that complexity during a customer conversation.
Write in the Language Agents and Customers Actually Use
Internal terminology can undermine search.
A customer might say:
“I’ve moved house.”
The internal process may be titled:
“Amendment of Registered Correspondence Address.”
A conventional keyword search may struggle to connect the two.
Good knowledge management therefore requires teams to understand:
- Customer language
- Agent language
- Internal terminology
- Common abbreviations
- Misspellings
- Synonyms
The GOV.UK guidance on chatbots and webchat recommends building knowledge using previous telephone enquiries, emails, chat logs, commonly raised concerns and feedback from advisers and users.
GOV.UK – Using Chatbots and Webchat Tools – https://www.gov.uk/guidance/using-chatbots-and-webchat-tools
This is particularly relevant as semantic search and AI become more common.
The technology can improve retrieval, but good underlying content still matters.
Treat Zero-Result Searches as a To-Do List
One of the most useful knowledge-management metrics is also one of the simplest:
What are agents searching for and failing to find?
Suppose agents repeatedly search:
- “change payment date”
- “move direct debit”
- “payment date change”
and receive no useful result.
That is not simply a failed search.
It is evidence of a knowledge gap.
Capacity recommends treating every zero-result search as a content gap that should feed into the knowledge-improvement process.
Capacity – Knowledge Management Best Practices – https://capacity.com/blog/knowledge-management-best-practices/
Knowledge teams should therefore review:
- Zero-result searches
- Low-click searches
- Searches followed by escalation
- Searches generating repeated feedback
Operational Principle
Every unsuccessful search is potentially a piece of customer research.
If agents keep asking the same question, customers probably are too.
Use Frontline Agents as Knowledge Sensors
Agents are among the first people to discover when knowledge is wrong.
They hear directly when:
- A process has changed
- A website is confusing
- Customers use unexpected terminology
- A policy creates recurring questions
- An article omits an important exception
The knowledge system should make it easy for them to flag this.
Potential mechanisms include:
- “Was this useful?” buttons
- Article comments
- Error flags
- Content suggestions
- Missing-answer reports
But collecting feedback is only useful if somebody acts on it.
The UK Government’s current guidance on AI and digital services similarly emphasises establishing feedback mechanisms and continuously monitoring and iterating systems over their lifecycle.
GOV.UK – Data and AI Ethics Framework – https://www.gov.uk/government/publications/data-ethics-framework/data-and-ai-ethics-framework
Feedback Principle
If employees repeatedly report inaccurate content and nothing changes, they eventually stop reporting it.
Then they stop trusting the knowledge base.
Close the Feedback Loop
A stronger process is:
Agent flags issue → owner reviews → content changes → agent sees outcome
The final step matters.
Employees are more likely to contribute when they can see that feedback leads to improvement.
Knowledge teams might publish:
- Weekly content updates
- “You said, we changed” messages
- New article alerts
- Corrected-process notices
This turns agents from passive consumers into active contributors.
Search Analytics Can Reveal Customer Problems
Knowledge analytics can tell contact centre leaders more than whether agents are using the platform.
Repeated searches may reveal problems elsewhere in the customer journey.
For example:
High searches for “where is my order?”
could indicate weak proactive delivery communication.
High searches for “why is my bill higher?”
could indicate confusing billing.
High searches for “cancel subscription”
could reveal friction in self-service.
The GOV.UK Service Manual recommends treating customer enquiries as potential indicators of problems elsewhere in a service.
GOV.UK – Set Up and Manage User Support – https://www.gov.uk/service-manual/operations/managing-user-support.html
That means knowledge analytics can become a form of voice-of-the-customer intelligence.
The contact centre is not simply resolving problems.
It is exposing them.
Identify Articles Agents No Longer Trust
An article existing does not mean agents use it.
Potential warning signs include:
- Low views
- Repeated alternative searches
- Agents asking colleagues instead
- High negative ratings
- Searches followed by escalations
Contact centre leaders should investigate why.
Possible reasons include:
- Article is difficult to find
- Content is too long
- Information is outdated
- Title uses unfamiliar terminology
- Agents know it is unreliable
Trust Principle
Knowledge adoption is ultimately a trust metric.
If agents regularly double-check what the system tells them, the knowledge environment is not doing its job.
Make Knowledge Available Inside the Agent Workflow
Even accurate knowledge can be underused if retrieving it requires too much effort.
Agents may already work across:
CCaaS + CRM + email + case management + messaging
If knowledge requires another application and another search process, employees may default to asking colleagues.
Modern contact centre platforms increasingly embed knowledge directly into the agent desktop.
Puzzel, for example, combines knowledge management with semantic search and wider contact-centre functionality to help agents retrieve information in the context of customer interactions.
Puzzel – Knowledge Management – https://www.puzzel.com/products/knowledge-management
Enghouse Interactive similarly provides knowledge-management capabilities designed to support both agent and customer self-service environments.
Enghouse Interactive – Knowledge Management – https://www.enghouseinteractive.com/en-gb/products/customer-self-service/knowledge-management/
The operational goal should be to minimise the distance between:
customer question
and
approved answer.
Agent Assist Changes the Search Model
Traditional knowledge management assumes:
Agent recognises question → agent searches → agent chooses article
AI-powered agent assistance can change this.
A system can potentially recognise the context of the conversation and surface information automatically.
For example:
Customer mentions changing address
→ address-change process appears.
Customer disputes a payment
→ verification and dispute guidance appears.
This reduces manual searching.
But it also raises the importance of content governance.
If the system proactively surfaces an outdated article, agents may be more likely to trust it because the technology presented it automatically.
AI Insight
Agent assist increases the speed at which good knowledge reaches employees, but it can also increase the speed at which bad knowledge spreads.
That makes content quality more important, not less.
AI Needs a Trusted Knowledge Layer
Generative AI can summarise, retrieve and combine knowledge in ways traditional search cannot.
Capacity describes its architecture as a knowledge layer that can support agent assist, AI agents, self-service and other customer workflows from the same governed information source.
Capacity – AI Knowledge Orchestration – https://capacity.com/ai-knowledge-orchestration/
The attraction is obvious.
Update knowledge once and the change can potentially propagate across:
Human agents + chatbot + AI agent + self-service
But that creates a corresponding governance responsibility.
If one incorrect article feeds all four channels, inconsistency has been replaced by consistent inaccuracy.
Organisations therefore need clearly approved source material beneath AI-generated answers.
Decide Which Sources AI Is Allowed to Use
Not every document inside an organisation should automatically become AI knowledge.
Possible sources might include:
- Approved knowledge articles
- Policies
- Product documentation
- Historical files
- Internal emails
- Shared drives
These carry different levels of authority.
An email written by a manager three years ago should not necessarily carry the same status as the current approved complaints policy.
AI systems therefore need defined source boundaries.
Capacity’s current access-control documentation, for example, allows AI agents to be configured around defined data sources and interfaces rather than simply having unrestricted access to everything in the knowledge environment.
Capacity – Knowledge Base Access Controls – https://support.capacity.com/article/559642/managing-knowledge-base-access-controls
AI Governance Principle
Don’t ask:
“What information can the AI access?“
Ask:
“What information are we prepared to let the AI treat as authoritative?“
Human Oversight Still Matters
AI can help:
- Find duplicate content
- Summarise long articles
- Suggest tags
- Identify outdated material
- Draft new articles
- Detect knowledge gaps
But automation should operate within appropriate human control.
Current UK Government guidance on using AI in services stresses accuracy, ongoing monitoring and clear accountability for how AI is used.
GOV.UK – Using Artificial Intelligence in Services – https://www.gov.uk/service-manual/technology/using-artificial-intelligence-ai-in-services
Likewise, the Government’s 2026 guidance on using AI agents states that businesses remain responsible for the actions their AI agents take when interacting with customers.
CMA – Using AI Agents: Complying with Consumer Law – https://www.gov.uk/government/publications/complying-with-consumer-law-when-using-ai-agents
For contact centres, that means automation should not remove:
- Content ownership
- Quality assurance
- Escalation
- Monitoring
Start AI Knowledge Deployment with Lower-Risk Use Cases
Organisations do not need to expose every customer process to AI immediately.
A sensible approach is to start with:
- High-volume
- Well-documented
- Low-complexity
- Lower-risk
…interactions.
Examples might include:
- Opening hours
- Order status
- Basic troubleshooting
- Standard product questions
More sensitive processes might require additional controls.
Capacity’s current implementation guidance recommends beginning AI-agent deployments with a limited number of high-volume, low-complexity interaction types and expanding once performance is understood.
Capacity – Implementing AI Agents in a Contact Centre – https://capacity.com/blog/how-to-implement-ai-agents-in-a-contact-center/
This mirrors a useful wider principle:
Start narrow → measure → improve → scale
Keep Human Escalation Available
Knowledge management should help resolve straightforward issues efficiently without trapping customers or agents when the available information is insufficient.
AI and self-service processes therefore need a clear escalation path.
Examples include:
- Knowledge not found
- Conflicting answers
- Customer dispute
- Complex exception
- Sensitive situation
The GOV.UK guidance on AI-enabled services recommends making customers aware of how AI is being used where relevant and ensuring they know how to reach a human.
GOV.UK – Using AI in Services – https://www.gov.uk/service-manual/technology/using-artificial-intelligence-ai-in-services
For knowledge teams, this means designing explicitly for:
“We do not know.“
A system that confidently gives an incorrect answer is more dangerous than one that recognises uncertainty and escalates.
Knowledge Should Work Across Agent and Self-Service Channels
A strong knowledge environment can support:
- Voice agents
- Email teams
- Webchat
- Chatbots
- Help centres
- AI agents
But the presentation may need to change.
Internal agent version
Includes:
- Detailed procedure
- Verification steps
- Internal system actions
- Escalation rules
Customer version
Includes:
- Clear explanation
- Simple instructions
- No internal terminology
Both can still originate from the same approved underlying policy.
This helps prevent one of the most frustrating customer experiences:
Website says one thing. Contact centre says another.
Review Knowledge After Major Business Changes
Certain events should automatically trigger knowledge review.
Examples include:
- Product launch
- Pricing change
- Regulation change
- New website
- System migration
- Policy change
- Merger or acquisition
Rather than waiting for scheduled review dates, teams should ask:
“Which customer-service knowledge does this change affect?”
Knowledge management should therefore have links into wider change-management processes.
If the business launches a new returns policy on Monday, the contact centre should not discover it on Tuesday from customers.
Use Version Control
When an article changes, organisations may need to understand:
- What changed?
- Who changed it?
- Who approved it?
- When did it take effect?
Version control is particularly important for:
- Regulatory information
- Complaints
- Financial processes
- Customer eligibility
It can also support incident investigation.
If a customer received incorrect advice six months ago, teams may need to know which version of the article agents could see at that time.
Good knowledge systems should therefore preserve a reliable audit history.
Retire Content Properly
Old content should not simply remain available because somebody might need it one day.
Obsolete articles create:
- Search clutter
- Conflicting results
- AI confusion
- Agent uncertainty
A mature lifecycle should therefore include:
Active → review → superseded → archived
Archived content may still be retained for legal or historical reasons, but it should not necessarily appear in routine agent search.
This becomes especially important with AI.
If outdated and current versions remain equally retrievable, the system may return information from the wrong one.
Measure Knowledge Quality, Not Just Article Volume
Having 10,000 knowledge articles is not necessarily better than having 2,000.
More content can make search harder if:
- Articles overlap
- Duplicates exist
- Old versions remain
- Titles are inconsistent
Useful metrics include:
Search success
Did the agent find an answer?
Zero-result searches
What information is missing?
Article usefulness
Do agents rate the content positively?
First Contact Resolution
Does better knowledge help solve more interactions first time?
Average Handle Time
Does retrieval become faster?
Escalation
Are fewer enquiries being passed elsewhere unnecessarily?
Adoption
Are agents actually using the system?
Capacity identifies search success, FCR, AHT and adoption as useful contact-centre KM measures.
Capacity – https://capacity.com/blog/knowledge-management-best-practices/
Don’t Optimise Average Handle Time in Isolation
Faster knowledge retrieval can reduce Average Handle Time.
That is useful.
But an agent giving the wrong answer very quickly is not an improvement.
Contact centres should therefore balance:
Speed + accuracy + resolution + customer outcome
For example:
AHT falls 20 seconds
but
repeat contact increases
may indicate the knowledge experience has become faster without becoming better.
Measurement Principle
The goal is not the fastest answer.
It is the fastest reliable answer that resolves the customer’s need.
Create a Practical Knowledge Improvement Cycle
A sustainable operating model might look like this:
1. Monitor searches
Identify what agents are looking for.
2. Identify gaps
Review zero-result and unsuccessful searches.
3. Gather frontline feedback
Allow agents to flag errors and missing content.
4. Prioritise changes
Focus on high-volume and high-risk knowledge.
5. Update and approve
Ensure subject-matter owners validate changes.
6. Publish
Make the latest approved content available.
7. Measure
Track search success, usage and customer outcomes.
8. Repeat
Knowledge management becomes continuous improvement rather than periodic housekeeping.
Build Knowledge Management into Team Routines
The process becomes much easier when it forms part of normal contact centre operations.
Possible routines include:
Daily
Urgent policy and operational updates.
Weekly
Review top failed searches and frontline feedback.
Monthly
Examine knowledge analytics and highest-volume content.
Quarterly
Review high-risk articles, owners and governance.
This prevents the knowledge base drifting for months before somebody organises a large-scale cleanup project.
Operational Insight
Small, continuous knowledge maintenance is usually easier than periodic knowledge rescue.
Knowledge Management Best Practices
For contact centre leaders, the operational priorities can be summarised relatively simply.
Give content an owner.
Somebody needs to remain accountable for accuracy.
Review according to risk.
High-change, high-risk information needs greater scrutiny.
Write for retrieval.
Agents need answers, not policy documents.
Use customer language.
Search should reflect how questions are actually asked.
Track zero-result searches.
They reveal missing knowledge.
Listen to frontline agents.
They discover problems early.
Integrate knowledge into workflows.
Don’t make agents hunt through another application.
Control AI sources.
Only approved information should underpin automated answers.
Measure outcomes.
Search success and resolution matter more than article count.
Keep improving.
Knowledge management is never finished.
Frequently Asked Questions
What are contact centre knowledge management best practices?
They include assigning content ownership, establishing review cycles, writing searchable content, capturing frontline feedback, analysing failed searches, integrating knowledge into agent workflows and measuring effectiveness continuously.
How often should knowledge-base articles be reviewed?
There is no single appropriate interval. Review frequency should reflect factors such as regulatory risk, how frequently information changes and how heavily the article is used.
What is a zero-result search?
A zero-result search occurs when an agent searches the knowledge environment and receives no useful answer. Repeated zero-result searches can identify important content gaps.
How can agents help improve the knowledge base?
Agents can flag incorrect information, suggest updates, identify missing answers and provide feedback on whether existing content is useful.
How can AI improve knowledge management?
AI can support semantic search, summarisation, agent assistance, content analysis and knowledge-gap identification, but it depends on accurate and properly governed source information.
How do you prevent AI from giving outdated answers?
Organisations should maintain authoritative source content, control which information AI systems can access, retire outdated material and continuously monitor AI performance.
How should contact centres measure knowledge management?
Useful measures include search success, zero-result searches, agent adoption, First Contact Resolution, Average Handle Time, escalation rates and content feedback.
Product Guide
Effective knowledge management combines technology with content governance, agent workflows, AI, customer-service operations and continuous optimisation. The following providers will be at the Contact Centre Summit and can support different elements of that environment.
Featured Suppliers
Capacity
AI-powered customer-service technology provider offering knowledge orchestration across agent assist, AI agents, self-service and quality workflows. Its approach places particular emphasis on maintaining a governed knowledge layer, analysing knowledge gaps and making updated information available across multiple customer-service touchpoints.
Website: https://capacity.com/
Capita Customer Management Ltd
Customer-experience and business-process specialist delivering large-scale customer-service operations and transformation. Capita’s operational experience can support organisations looking to embed knowledge, digital tooling and automation within complex service environments.
Website: https://www.capita.com/
Enghouse Interactive
Contact centre technology provider offering customer interaction, employee-experience and self-service capabilities. Its knowledge-management technology is designed to help agents and customers access relevant information across assisted and digital channels.
Website: https://www.enghouseinteractive.com/
HGS
Global customer-experience and business-process provider combining customer-service operations with digital transformation, automation, analytics and AI, supporting organisations seeking to improve how knowledge and technology are applied across service delivery.
Website: https://hgs.com/
IP Integration Ltd
UK customer-experience specialist providing cloud contact centre, communications, workforce optimisation, AI and managed services. Its integration expertise can help organisations bring knowledge and agent-support capabilities into wider contact-centre workflows.
Website: https://ipintegration.com/
Kerv
Technology provider delivering cloud communications, contact centre and customer-experience solutions, including AI and automation capabilities supporting more connected agent environments and digital service delivery.
Website: https://kerv.com/
MAINTEL / MITEL
Maintel provides managed communications and customer-experience solutions, while Mitel supplies communications and contact-centre technology. Their capabilities can support organisations integrating knowledge, communications and wider agent tools within modern customer-service environments.
Websites: https://maintel.co.uk/ | https://www.mitel.com/
Odigo
Cloud contact-centre provider offering CCaaS capabilities across voice, digital interactions, routing, AI and agent support, helping organisations bring customer-service information and workflows into a more integrated operating environment.
Website: https://odigo.com/
Opus Technology
Managed technology and communications provider offering contact-centre, cloud, connectivity and wider IT services, supporting organisations seeking to modernise the underlying technology used by customer-service teams.
Website: https://www.opustech.co.uk/
Puzzel
Customer-experience technology provider offering cloud contact centre, workforce engagement and dedicated knowledge-management capabilities. Its platform combines structured knowledge with AI-powered semantic search designed to help agents retrieve relevant information more effectively.
Website: https://www.puzzel.com/
Sabio Group
Customer-experience transformation specialist combining contact-centre technology, AI, analytics, automation and managed services, helping organisations optimise customer-service operations and agent experience.
Website: https://sabiogroup.com/
Verint
Customer-experience automation specialist providing knowledge management, workforce engagement, analytics and AI technology. Its capabilities support organisations seeking to automate and improve both human-agent and digital customer interactions.
Website: https://www.verint.com/
Wavenet
UK managed technology provider spanning communications, cloud, connectivity, cybersecurity and customer-experience technology, supporting the wider infrastructure required for modern contact-centre operations.
Website: https://www.wavenet.co.uk/
Zoho Corporation Limited
Business-software provider offering CRM, helpdesk, knowledge-base, customer self-service and automation capabilities through its wider application portfolio, enabling organisations to connect customer information with service knowledge and support workflows.
Website: https://www.zoho.com/
Keep Contact Centre Knowledge Useful
A contact centre knowledge base rarely fails overnight.
It fails one small piece at a time.
An article becomes outdated.
A customer starts using terminology the system does not recognise.
A new process launches without the knowledge team being told.
Agents discover a workaround and begin asking colleagues instead of searching.
Eventually, the technology may still be working perfectly while confidence in the information has declined.
That is why the strongest knowledge-management programmes treat content as a living operational asset.
Ownership + review + frontline feedback + search analytics + governance + continuous improvement
are what keep the system useful after implementation.
And as AI becomes more deeply embedded into agent assistance and customer self-service, the quality of that foundation becomes even more important.
AI can make good knowledge dramatically easier to use.
It can also make bad knowledge dramatically easier to distribute.
The Contact Centre & Customer Services Summit connects senior contact centre and customer-experience professionals with carefully selected providers of knowledge management, AI, CCaaS and wider customer-service technology through a programme of pre-arranged one-to-one meetings.
Explore contact centre knowledge-management solutions and specialist partners to discover how better governance, frontline feedback and intelligent technology can help keep organisational knowledge accurate, searchable and trusted.
Related Reading
This article follows our earlier guide to Contact Centre Knowledge Management Software: What Customer Service Teams Should Compare, covering search, AI, agent assist, governance, self-service, integration and supplier selection.
Sources
- GOV.UK – Using Chatbots and Webchat Tools – https://www.gov.uk/guidance/using-chatbots-and-webchat-tools
- GOV.UK Service Manual – Set Up and Manage User Support – https://www.gov.uk/service-manual/operations/managing-user-support.html
- GOV.UK – Using Artificial Intelligence in Services – https://www.gov.uk/service-manual/technology/using-artificial-intelligence-ai-in-services
- GOV.UK – Data and AI Ethics Framework – https://www.gov.uk/government/publications/data-ethics-framework/data-and-ai-ethics-framework
- Competition and Markets Authority – Complying with Consumer Law When Using AI Agents – https://www.gov.uk/government/publications/complying-with-consumer-law-when-using-ai-agents
- Capacity – Knowledge Management Best Practices – https://capacity.com/blog/knowledge-management-best-practices/
- Capacity – AI Knowledge Orchestration – https://capacity.com/ai-knowledge-orchestration/
- Capacity – Implementing AI Agents in a Contact Centre – https://capacity.com/blog/how-to-implement-ai-agents-in-a-contact-center/
- Puzzel – Knowledge Management – https://www.puzzel.com/products/knowledge-management
- Enghouse Interactive – Knowledge Management – https://www.enghouseinteractive.com/en-gb/products/customer-self-service/knowledge-management/
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