Machine-Learning is Leading the Self-Improving Help Desk: Case-Based Reasoning (CBR) Systems

In the AI-driven era, customer service has evolved to be more efficient and self-learning. AI systems help companies in a variety of ways including improving customer satisfaction ratings, reducing operational costs, and increasing revenue. AI has many other advantages for customer service that human agents cannot compete with — it is always available, 24/7 and never gets tired or distracted. One of the leading AI systems in this area is Case-Based Reasoning, or CBR, systems' machine learning help desk system.

What is Case-Based Reasoning?

Case-Based Reasoning (CBR) is an AI problem-solving technique that works similarly to how humans learn by recalling similar past experiences. Instead of solving problems from scratch, CBR retrieves a "case" (a past problem and its solution), adapts it to the new situation, and reuses it.

For example, if a help desk ticket comes in about a printer error, the system looks up similar past cases, finds the fixe that resolved the issue, and presents it.

Case-Based Reasoning (CBR) is increasingly used by customer service departments to improve their performance and help desk software providers to offer even more intelligent solutions for their customers.

What is the Case-Based Reasoning Cycle?

The Case-Based Reasoning cycle includes five steps:

1. Case representation and storage (RETRIEVE)

   Using a retrieval algorithm, the system searches its case memory to match a help desk ticket.

2. Precedent matching and retrieval (REUSE)

   The help desk agent can then quickly solve the customer's problem by applying the knowledge from previous calls.

3. Adaptation of the retrieved solution (REVISE)

   After compiling new data from customer calls, the help desk agent can update the case indices for future retrieval.

4. Validation of the solution

   Instead of working out the similar customer issues every time a help desk ticket is logged, agents can learn about the effectiveness of a troubleshooting technique from previous cases.

5. Casebase update (RETAIN)

   As the cycle of updating information and indexing the cases continues, the AI continues to improve its ability to retrieve information in order to support help desk agents better.

What are the Advantages of Case-Based Reasoning AI in Help Desk Systems and Its Limitations?

• Advantages of CBR

  • The AI help desk system is constantly updating its knowledge base with up-to-date customer service issues and questions, making it more efficient in solving problems for customers than human agents can be. Further, the entire model does not need to be updated continuously.
  • The AI also learns with every interaction so that it will become even more intelligent over time — something humans cannot do as quickly or efficiently.
  • The CBR AI machine learning solution provides a cost-effective customer service option to businesses of any size while improving their bottom line by increasing revenues.
  • Machine-Learning software also uses CBR to further the potential of Self-Improving help desk solutions.
  • They are even capable of prioritizing cases.
  • Since they have historical data on how complicated or lengthy a problem can be, they can quickly manage customer requests. Using the input specifications, they match the problems to their casebase. The AI then uses a combination of machine learning and human input to determine the best course for handling customer issues.

• Limitations of CBR

  • Most AI help desk chatbots use filtered-chat options to understand customer problems.
  • In cases of unique concerns, a customer can become quickly frustrated if their need or concern is not an available option. In those cases, it becomes important to merge AI solutions and human agents to ensure that the IT agent is providing more intelligence to the system that it may be missing.
  • It is only good as the information it has. If the quality of the data it is using lacks, it can provide irrelevant information. And if the information doesn't exist, it is unable to provide answers.

Case-Based Reasoning Applications Beyond the Help Desk

While CBR is popular in help desks, it is also often used in other industries, such as:

• Healthcare

  Example: Diagnosing rare diseases by comparing patient symptoms with historical cases

• Legal Services

  Example: Supporting lawyers with precedent-based case research

• Education

  Example: E-learning platforms that suggest solutions based on similar student mistakes

These examples illustrate the versatility of CBR across various functional business areas.

Case-Based Reasoning vs. Other AI Approaches

There do exist other similar AI systems, so let's briefly compare a few of them to the CBR approach:

• Rule-Based Systems Vs. CBR

  These work on static "if-then" rules, while CBR adapts dynamically from past cases.

• Neural Networks vs. CBR

  These are powerful for pattern recognition but less transparent in tracing how and why a solution was found or presented. CBR on the other hand uses the cases themselves, which will typically have an audit trail of the resolution process

• Supervised Learning vs. CBR

  These require large labeled datasets, which pair a question with a label and an answer. This ends up requiring huge amounts of pairing to be able to provide comprehensive solutions to problems. However, CBR can work well with smaller, real-world-example case libraries.

The Future of Case-Based Reasoning

Modern AI systems are combining CBR with deep learning and even generative AI. For example, a help desk solution might retrieve a past case (CBR) and then use a generative model to personalize a natural-language response for the customer. This hybrid approach merges the efficiency of past knowledge with the adaptability of modern AI.

In the end, AI systems are redefining customer service and the way we interact with our customers. CBR Systems' machine learning help desks are a great example of how these technologies can be a valuable asset to your company's customer satisfaction ratings, revenue streams, operational costs — or all of the above.

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