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Whether you are a product-based enterprise or solution provider, an ITSM is a familiar component for companies like yours to empower internal users and provide uninterrupted IT support and delivery for your business's success. The existing challenges for ITSM are also not hidden from anyone.
Generative Artificial Intelligence has set high expectations for enterprise leaders to make ITSM flexible and agile.
There are many different sides to ITSM, where GenAI has brought a potential change. IT support operations, AIOps, DevOps, help desk, and a variety of ITSM components see a major overhaul for Generative AI.
Considering the powerful capabilities of Generative AI, we can say every little detail of ITSM can be improved and reimagined.
That said, the internal Service Level Agreement or SLA for ITSM can also be maintained with such a flair that enterprise leaders can set high expectations for it and attain the business objectives through proper compliance with ITSM SLA using Generative AI.
But why does internal SLA matter so much to ITSM leaders, and how does a Generative AI-powered service desk helps gain internal SLA targets for their internal employees? Let’s learn.
Though what SLA refers to is different from what internal SLA means, the objective of both terms are equal, facilitating service delivery, user performance, and ensuring user experience.
From the keyword ‘Service Level Agreement,’ let’s make a simple sense of the entire thing 一 which is to follow an agreement for what is stated or promised for service delivery to the user.
In a particular scenario of a service level agreement, it involves a vendor and a buyer to whom a certain or some level of service agreement is made, in which it is stated what, how, and when a service will be delivered as per expectations.
For a product-based company, it is obvious to have some stack of applications. Say, your organization uses a screen-recording application from a SaaS-based company. As the SLA goes, the company promises to deliver 240 hours of free recording within 30 days. It can add more clauses to the SLA. If those free hours aren’t utilized within 30 days, they will be removed normally.
But as stated in the SLA, if users cannot access those free hours of recording, they are deprived of what is promised to get their work done.
So, an SLA makes it mandatory to adhere to clauses and parameters that represent violations of those documented clauses.
An internal SLA means offering service level agreement to the internal employees from the employer’s end.
It is easy to understand that an internal SLA is not between the customer and a vendor. Rather, an employer adheres to some policies or a piece of the agreement for its employees so as to deliver the right kind of IT support to them and help them gain operational efficiency in their day-to-day job.
An internal SLA also involves performance metrics and service expectations between two departments of an organization.
Whatever it may concern, an internal SLA ensures performance delivery for the promised services utilizing optimized response and resolution times.
To further reflect upon an internal SLA, it can refer to providing network or application access, password management, account unlock assistance, etc for employees within the right time through proper IT support.
For example, if an internal SLA mentions a system downtime must be addressed within a day, it must occur as stated in SLA. So, a ticket must be created, addressed, and closed within 24 hours.
The key objective of an SLA is to facilitate incident management and improve performance for organizational services, which later translates to user experience.
If an organization does not meet its SLA target, IT support may be compromised, and organizational resilience may be impacted.
For example, an incident is communicated through a service desk for response and resolution just in time.
An SLA defines how a particular incident must be addressed within a specified time frame.
Now imagine an SLA is not documented for downtime in an application. Employees would have longer days of downtime and productivity issue. At the same time, the service desk is not supposed to be careful about a timely resolution of the incident, which impacts operational efficiency.
But, if an internal SLA is there, it is the service desk’s responsibility to resolve the issue as specified in SLA. That’s why an SLA is integral to a service desk.
The fact about the SLA compliance success rate is it depends on what number of tickets are resolved.
In a natural way, a service desk is always involved in resolving a ticket.
To calculate an SLA compliance success rate, it goes like this:
Documenting service-level agreement can articulate steps or commands for users to implement successful performance delivery and meeting end-user experience.
But, SLA compliance is less effective unless there is a proper tool to facilitate employee IT support.
An organization's service desk is everything to meet SLA targets by resolving IT incident tickets and maintaining employee support.
A service desk can facilitate incident resolution, appropriate service delivery, and employee performance management by providing ample tools and features.
With that said, a more powerful service desk means faster and speedier compliance with SLA through more ticket resolution.
A Generative AI service desk may be your answer to meet SLA targets.
Generative Artificial Intelligence's key potential lies in the generation of new content, summarization, classification, and categorization.
The embedded natural language processing capability in Generative AI helps the service desk parse intricate data and provide better suggestions for incident handling, which alternatively helps speed up SLA compliance.
It is an obvious service desk activity to register an incident report whenever there occurs one. But from composing a service incident description to having the right person to look at the message, the communication path does not seem easy.
If the message is factually or contextually correct, a service desk can recommend steps for mitigation plans.
However, providing the service desk with the correct and contextual incident description is not always easy.
For instance, a person responsible for sending a message may not have the knowledge to craft a message. As a result, there might be a delay in receiving a message.
A large language model-powered service desk simplifies the composition of a new service incident description by automating content generation. Using just a few prompts, an incident management requester can create a new message and send it to the service desk.
The content generation capability also helps create a standardized format for everyone at the service desk, which also improves contextual gains and speedier response generation.
When an incident occurs, a service desk receives several types of incident messages for the same incident. A different layer of incident message points may come from product engineers, a monitoring system, or internal employees. The challenge is that the message includes various formats, such as textual statements, images, a string of scripts, etc., making it difficult for the service desk manager to comprehend the message.
The ability to parse human language using NLP, a large language model, or a Generative AI service desk can help remove information overload from the service description using the “extract” prompt.
All it needs for the service desk manager is to copy the ‘incident description,’ paste it into the LLM interface and prompt it to extract key incident information. An LLM-powered service desk easily extracts key information and produces consolidated incident responses for the service desk agent or manager.
The advantage is the service desk does not need to spend extra time and effort to comprehend an incident message to create a ticket and communicate with the right person to address an incident.
A fine-tuned large language model trained with historical data of a specific domain can be instrumental to empowering service desk operations and helping agents to automate incident diagnosis and mitigation plans.
Due to this flexibility, a Generatie AI service desk can use incident summary, title, and description to learn the pattern across its database and produce an appropriate analysis of the incident and its root cause based on retrieval augmentation techniques.
RAG, or Retrieval Augmentation Generation, helps a large language model to retrieve historical data from the database and extract contexts from incident documents or logged reports to generate coherent responses to the service desk inquiries.
By facilitating a chat interface to allow incident discussion, a GenAI-powered service desk can offer unified discussion on incident diagnosis and help accelerate decision-making on mitigation plans and reduce the length of downtime.
It is observed now that a Generative AI-powered service desk offers ample tools and features to handle incidents and resolve tickets with a maximum level of precision and speed. It would dramatically translate to a powerful service desk that helps organizations fully comply with an internal service-level agreement. As a result, a company is empowered to ensure enhanced performance delivery and offer elevated user experience.
On a day-to-day basis, an organization can use a Generative AI service desk to facilitate employee support while maintaining internal SLA without a breach of the document.
When using a Generative AI service desk, a company can unleash unique use cases to improve employee support performance while maintaining an internal SLA.
Workativ uses Generative AI properties in its self-service chatbot to elevate the service desk experience. We combine all the right tools and features to allow organizations to improve support performance, deliver operational efficiency for employees, and maintain user experience.
Our Generative AI-powered service desk complies with the industry standard to deliver employee support and helps you maintain internal service level agreements.
We provide an analytics dashboard to examine chatbot logs ─ what number of tickets are handled successfully and how many remain unattended to analyze the health of an internal SLA. Based on the analytics, it is easier to detect room for improvement and implement the right improvement strategy.
In a nutshell, Workativ helps you leverage the LLM-powered chatbot for service desk operations and helps you meet internal SLA targets.