AI-generated. Human-edited. Grounded in the industry and engineering expertise built into AionCX.
A refund policy can determine what an agent tells a customer, what an automated assistant is allowed to offer, how a quality review evaluates the conversation and what a supervisor coaches afterward. All four depend on the same business rules, but each needs those rules in a different form.
The agent needs a clear explanation and the steps to follow. Quality assurance needs criteria it can evaluate against the conversation. Coaching needs examples of the expected behavior and material someone can practice. A virtual agent needs instructions it can apply, including when to hand the customer to a person.
In many contact centers, the source information still sits in SharePoint documents, spreadsheets, presentations and team messages. People copy it into scorecards, knowledge articles, training courses and assistant configurations. Every copy then needs to be maintained.
That is why we call this Knowledge Operations. The responsibility extends from the original business information to every operational use of it. As AI takes on more of the work in a contact center, that connection becomes critical.
One policy affects several parts of the operation
Consider a change to the refund policy. The business introduces an additional review for certain purchases and requires customers to receive an explanation of the next steps.
Someone updates the policy document. The agent guidance needs the new procedure. The quality scorecard needs to reflect what agents are now expected to explain. Coaching material needs examples of the revised conversation. The virtual agent needs to recognize the affected purchases and give the appropriate instructions.
If only some of those updates happen, the operation starts working against itself. An agent follows the new procedure and receives a poor quality assessment against the old criteria. A supervisor coaches an explanation that has been superseded. An assistant tells customers they qualify for an immediate refund when their purchase now requires review.
These problems can look like failures in quality, training or automation. Their common cause is that the systems are working from different versions of the business’s knowledge.
Updating the document is one task in a larger operational change. Someone needs to understand what depends on it and carry the change through.
Each system needs a different version of the information
Giving every system access to the same folder helps with access, but leaves the work of interpretation.
A policy is usually written to establish a rule. A quality evaluation needs to establish what evidence would demonstrate that the rule was followed. A coaching exercise needs to help an employee recognize the situation and respond appropriately. A customer-facing assistant needs to apply the rule to the information available in that interaction.
Those uses require preparation. The wording, level of detail and structure will differ, while the underlying meaning needs to remain consistent.
For example, a policy might require the customer to be informed of a review period. The agent article should explain that period in customer language. The quality criterion should check whether the explanation was given accurately. The coaching exercise should include a customer asking when their money will arrive. The assistant should use the approved timing and account for any relevant exceptions.
Knowledge Operations manages those relationships. It connects the source rule to the guidance, criteria and instructions derived from it, so a change can be reviewed across its different uses.
SharePoint and spreadsheets still leave people doing the translation
Shared folders and spreadsheets are familiar places to store and collaborate on information. The difficulty comes when the operation depends on people remembering every place that information has been copied.
A spreadsheet may hold the quality criteria while a document contains the policy they came from. A training presentation explains the procedure, but its author has moved to another team. An assistant uses a separate knowledge collection with its own update process.
The people maintaining these materials may be doing careful work. They still need a way to identify dependencies, resolve conflicting versions and coordinate updates across systems.
As more AI applications are introduced, the number of places consuming that knowledge can grow. Each application may perform well against the information it has been given while producing results that conflict with another part of the operation.
The business needs to be able to establish which source governs a particular answer or assessment, who owns it and whether the consuming system has received the current approved version.
AI should handle more of this work
AI gives us a way to reduce the effort involved in preparing and maintaining these different uses of knowledge.
It can help examine source material, identify related guidance and locate contradictions. It can prepare an agent article, propose an evaluation criterion or draft a practice scenario from approved information. When a source changes, it can help identify which materials need review and assemble the proposed revisions.
The owner can assess the changes with the source and affected materials alongside them. Questions about the policy’s meaning can go to the person authorized to answer them. Once approved, the revisions can be distributed to the connected destinations, with outstanding updates visible.
That is a more useful role for AI than expecting each department to repeat the same investigation and drafting work independently. It also makes consistent knowledge practices more achievable for operations with limited specialist staff.
The source remains important throughout. An evaluation criterion should have a basis in the service standard. A coaching exercise should teach the approved behavior. An assistant’s instructions should reflect the policy that applies to its customers.
The operation also produces new knowledge
Customer conversations, quality reviews and coaching sessions reveal questions the existing material has yet to address.
Agents may encounter an exception that the procedure leaves unclear. Quality reviewers may disagree because a criterion allows several interpretations. A virtual agent may repeatedly transfer customers asking the same question. A coaching session may reveal that the expected behavior is difficult to follow in practice.
These findings should feed back into the knowledge work. They can prompt clarification, a revised criterion, a better example or an additional answer.
This also helps the business avoid treating every problem as an employee performance issue. When several people struggle with the same instruction, examining the instruction is a sensible place to start. When an assistant gives an incomplete answer, the investigation should include the information it received.
Knowledge Operations connects those observations with the people responsible for improving the source and its operational uses.
What this publication will cover
We will examine how contact centers organize business knowledge and put it to use across quality, coaching, customer intelligence, workforce planning and human and automated service. That includes the practical work of preparing information for different systems, maintaining approvals and versions, and learning from what happens when the information is used.
Our point of view is that knowledge deserves the same operational attention as staffing or quality. It influences what the contact center does, how that work is evaluated and what employees learn to do next.
AI increases both our dependence on reliable knowledge and our ability to maintain it. Knowledge Operations brings those responsibilities together so the business can change a rule with confidence that its people and systems will follow it.