Standardise answers, unlock self-service, and scale AI-driven automation—without losing the human expertise your customers trust.
Most customer service teams already possess the knowledge they need to deliver excellent support. The problem is that this knowledge is often difficult to access, inconsistent, or scattered across employees’ heads, private conversations, outdated documents and disconnected systems.
This makes it harder to provide reliable answers, scale self-service and introduce AI without increasing operational risk.
Building an AI-ready customer experience does not begin with a chatbot or a new automation tool. It begins by transforming fragmented information into a structured, governed knowledge foundation that agents, customers and AI can trust.
A strong knowledge strategy allows organisations to:
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Trodo is a fast-growing ecommerce business specialising in automotive parts. The company serves customers across multiple countries and languages while managing a complex product catalogue and a high volume of daily enquiries.
As the business expanded, Trodo wanted to improve response times and service consistency without simply increasing the size of its support team.
AI offered a clear opportunity to scale, but only if the answers generated remained accurate, useful and aligned with the company’s processes.
Before working with redk, Trodo’s customer service team was dealing with several common operational challenges:
Trodo understood that introducing AI without improving the underlying knowledge would create more uncertainty rather than better customer experiences.
Together, Trodo and redk created a knowledge-first approach to customer service automation.
The project included:
Instead of using AI to compensate for inconsistent information, Trodo created a trusted knowledge layer that could support both automation and human decision-making.
At the time of publishing, Trodo was automatically resolving approximately 40% of its monthly customer requests using AI.
This allowed the company to:
Knowledge became a strategic asset that supported both customer experience and operational growth.
Read the full case study: How Trodo transformed customer service through AI-driven automation
Go behind the scenes: How Trodo automated 40% of customer requests without losing control
AI in customer experience is only as effective as the knowledge and processes behind it.
Before introducing bots, agent-assist tools or automated workflows, organisations need to understand whether their existing information is accurate, accessible and structured around real customer needs.
The following maturity model provides a simple way to assess your current position.
Answers mainly live in employees’ heads, private conversations, email threads and messaging platforms.
There is no reliable source of truth, and new team members depend heavily on asking more experienced colleagues for information.
This creates inconsistent answers, slow onboarding and a high dependency on individual employees.
Some articles and frequently asked questions exist, but the content is incomplete, outdated or disconnected from the customer enquiries agents handle every day.
Information may be available, but employees and customers do not always trust it.
The organisation has a centralised and governed knowledge base.
Content has clearly assigned owners, defined review cycles and sufficient coverage of the main contact reasons. Agents can usually find the information they need without relying on colleagues.
Knowledge is structured around customer intents, scenarios and desired outcomes.
It is embedded directly into Zendesk workflows and actively used by agents, self-service experiences and AI automation.
The organisation measures usage, resolution outcomes and content performance, then continuously improves the knowledge layer based on real customer interactions.
Use the following questions to assess how prepared your organisation is:
If you cannot confidently answer most of these questions, your organisation may not yet be ready to scale AI safely.
A Knowledge and AI CX Audit can help identify immediate improvements, prioritise the highest-value opportunities and define a practical roadmap for future automation.
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AI is not a shortcut around fragmented knowledge or inefficient processes.
The organisations that achieve sustainable results treat knowledge as a central part of their customer experience operating model rather than as a separate documentation project.
The following five-step framework provides a practical approach to preparing customer service operations for AI.
Before choosing technology or creating content, define the role AI should play in your service model.
Consider:
Clear boundaries help avoid disconnected AI experiments that create confusion for customers and employees.
They also make it easier to prioritise the use cases most likely to deliver measurable value.
Explore redk’s approach to AI readiness
Your knowledge strategy should be based on the reasons customers actually contact your organisation.
Start by analysing support tickets, conversations and search data to identify the most common enquiries.
These might include:
Group related enquiries into customer intents and define the ideal outcome for each one.
You should also document any exceptions, required approvals, regulatory restrictions or situations that need escalation.
This intent map becomes the foundation for both your knowledge architecture and your AI use cases.
Discover how to design customer-focused Zendesk workflows
Once the main customer intents are understood, you can create a more useful knowledge structure.
Each article should address a clear question, problem or scenario.
A consistent structure may include:
Avoid creating multiple articles that provide conflicting or duplicated information.
Instead, review existing content and:
The objective is not to create the largest possible knowledge base. It is to create a smaller, clearer and more reliable source of truth.
Learn more about Zendesk knowledge management
Knowledge only delivers value when it appears at the right moment.
Agents should not have to leave their workspace, search across several systems or ask colleagues for basic information.
Within Zendesk, knowledge can be embedded into the flow of work through:
This improves consistency and reduces handling time even before advanced automation is introduced.
It also increases agent adoption because knowledge becomes part of the process rather than an additional task.
See how redk optimises Zendesk environments
Once customer intents are defined and knowledge is governed, AI can be introduced with much greater confidence.
Start with enquiries that are repetitive, predictable and low risk.
Examples may include:
AI can also support agents with more complex cases by:
Performance should be monitored continuously.
Important indicators include:
Use these insights to improve content, adjust workflows and expand automation gradually.
This controlled approach allows organisations to scale AI without compromising customer trust or operational visibility.
Talk to redk about Zendesk AI implementation
An AI-ready knowledge base is a structured and governed collection of information organised around real customer intents and service scenarios.
Its content is accurate, current and written in a way that both employees and AI systems can use effectively.
It also includes clear ownership, review processes and performance measurement so the organisation can trust the information being delivered.
The level of automation depends on the organisation, industry, complexity of enquiries and quality of existing processes.
Many companies can automate a meaningful percentage of repetitive, low-risk requests once their knowledge and workflows are properly structured.
Trodo, for example, achieved automated resolution of approximately 40% of its monthly customer requests by prioritising clearly defined intents and maintaining human control over complex cases.
Read the Trodo AI transformation story
AI initiatives can be explored while knowledge improvements are underway, but organisations should not expect AI to solve inconsistent processes or incomplete documentation.
If the underlying information is inaccurate, AI will reproduce and amplify those problems.
The strongest programmes begin by defining customer intents, improving content and creating reliable workflows before scaling automation.
Success should be measured using a combination of customer, employee and operational indicators.
These may include:
Looking at these metrics together provides a more accurate view of whether AI is improving both experience and productivity.
Many organisations begin seeing improvements within a few months after identifying their main customer intents and restructuring the most important content.
Larger automation gains usually require more time because knowledge must be created, validated, integrated into workflows and tested in real customer situations.
Sustainable transformation generally takes several months or a few quarters rather than a few weeks.
In most successful customer experience models, AI changes the type of work agents perform rather than removing the need for people.
AI handles repetitive and predictable tasks, while employees focus on interactions that require:
The objective is to remove low-value work and give agents more time for the situations where their expertise has the greatest impact.
Zendesk provides the technological foundation for connecting customer conversations, knowledge, automation and agent support.
Its platform can use structured content across:
However, technology alone is not enough.
Organisations still need a clear operating model, a reliable knowledge architecture, defined workflows and effective governance.
redk helps companies develop these foundations so they can use Zendesk AI while maintaining control, accuracy and customer trust.
Learn more about redk’s Zendesk services
AI can only deliver consistent customer experiences when it is supported by reliable knowledge, clear processes and well-defined customer intents.
A Knowledge and AI CX Audit can help you understand your current maturity, identify the highest-value opportunities and create a realistic roadmap for knowledge-driven automation.
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