IT Service Management (ITSM) tools may soon be able to use AI to write your knowledge articles for you, which will save you time and improve your customer experience, right?
Having the ability to automate the creation of guides, FAQs, and troubleshooting steps, could be an effective way to reduce the workload on your IT staff and enhance customer satisfaction.
But that miraculous, AI written content won’t just be plucked out of thin air, it will likely be generated by scanning your tasks (Incident, Problem, Change) and compiling a list of suggested articles based on repeat effort and ease of deflection.
In the context of ITSM, this data will primarily from the task subject, description, any work notes or interactions with the customer, and any information captured upon resolution.
This means that the quality of these AI generated knowledge articles will be directly proportional to the information captured in your, incidents, and requests.
Before you make any moves towards AI generated knowledge, it’s important to understand some of the challenges that will lie ahead:
Relevance and accuracy in AI generated content
Capturing the right information in ITSM tasks is, and always has been, crucial to the return you get back on your ITSM solution.
If we are going to start using this information to generate guides, FAQs, and troubleshooting steps, then it’s just become even more crucial than ever.
AI relies on patterns and information contained in the data they process. When your agents capture the right information in task descriptions, work notes, and resolutions, the AI algorithms will (hopefully) generate meaningful and genuinely helpful content.
Reduce the quality of the information recorded in your ITSM tasks and you reduce the capability of the AI algorithms.
Understanding context is difficult for AI
AI struggles with context, and simply feeding it more information isn’t the solution.
Just one of the challenges is to understand the relationships in any content. That is, the relationship to different users, other systems and their content, and then classify people and information in the context of the given environment.
Usefulness of AI generated knowledge
The ultimate goal of knowledge (human or AI generated) should be to provide timely solutions that offer useful and effective information that enhances user satisfaction, accelerates problem resolution and reduces downtime and associated costs.
If your AI generated content does not achieve this, then you need to ask yourself: “is this the right path for me/my team/my company?”.
Scalability requires attention to quality
Scalability is possibly the most important of the challenges to understand.
As your team or business grows, so too does the volume of any tasks and information you generate.
AI is a legitimate, and effective path to scalability. But beware, for here be dragons.
Scale up with a focus on capturing quality information, and you scale up the effectiveness of your AI generated knowledge.
Scale up without a focus on quality, and you further compromise the quality of your generated content.
How to avoid the pitfalls
To make the most of AI generated knowledge, consider following best practices for capturing information within your incidents, problems, changes and service requests.
- Comprehensive detail: Encourage agents to provide thorough and informative notes, including steps taken and any communication with the client. The more context your agents provide, the better AI will understand.
- Accurate resolutions: When tasks are resolved, insist on precise resolutions. AI can learn from how problems were solved, so clarity and accuracy in the resolution details are crucial.
- Consistency: Establish a consistent format for data entry. This makes it easier for AI to extract information and create useful knowledge articles.
To ensure that AI generated knowledge provides your desired outcomes, you must prioritise the capture of quality information over the speed of task resolution.
AI generated knowledge has a tremendous potential to enhance efficiency and improve customer satisfaction. It also has a tremendous potential to create confusion and drive customer satisfaction into a downward spiral.
