Out of a variety of service desk performance metrics, MTTR or Mean Time to Resolution or Mean Time to Repair is critical.

Service desks generally use this metric to ensure service efficiency and employee experience.

Regardless of your IT responsibilities, the service desk must build effective responses and resolutions to mitigate downtime and ensure productivity.

MTTR provides significant metrics that tell the exact time taken to resolve an issue or employee support request. Hence, service desks always aim to reduce MTTR and boost efficiency.

Unfortunately, reducing MTTR is the biggest challenge for service desk managers today. According to the Observability Pulse Survey by Logz.io, IT and DevOps leaders claimed that their MTTR during production incidents was over an hour.

Companies using AI improve MTTR. When they upgrade their AI tools to Generative AI, they can dramatically reduce MTTR and drive employee experience.

Here’s how Generative AI can help reduce resolution time or MTTR for your service desks.

Generative AI is a subset of AI/ML technologies that uses deep learning and neural networks to demonstrate excellent human language capabilities, synthesize complex or simple queries, and transform service desk performance.

With inherent natural language processing (NLP) and natural language generation (NLG) capabilities, Generative AI can refer to training data patterns to create new content.

Service desks seamlessly use Generative AI to create new responses and automate communications, effectively mitigating risks and lowering MTTR.

Mean Time to Resolution (MTTR) is a metric that helps measure the time from receiving a ticket to resolving it. It encompasses both wait times and resolution times.

MTTR involves four critical steps to determine the average time to resolve a service request for employee support. Generally, it depends on the effectiveness of your incident response systems, which include,

• Report : time taken to report a particular issue

• Response : time taken to respond to the issue

• Resolve : time taken to resolve an issue

• Recovery : time taken to recover from the incident

Reducing MTTR means resolving issues and restoring operations or returning to work faster.

With all this said, Generative AI can help address issues proactively to not just fix them for the time being but ensure they do not happen again. It also helps observe other key things, such as root cause analysis, incident communications, and post-mortem analysis, to ensure tickets are declared resolved correctly.

Lowering MTTR is essential and stays on top of service desk managers’ heads.

Generally, it is essential to observe how long it takes to resolve an issue from when a ticket is reported until it is resolved and service is restored. Service desk managers need to know whether this duration is justifiable.

Higher MTTR has negative impacts on employee experience, productivity, and efficiency.

• User experience

Long MTTR means delayed resolutions. Employees can have hampered productivity, longer wait times, and become frustrated.

• Operational efficiency

Higher MTTR can ruin work consistency. Until a disruption is fixed to bring a system back to work, employees tend to lose a significant number of productive hours.

Service desk managers aim to reduce MTTR, but existing processes challenge them.

A lot of technological innovations has taken place in AI. However, companies should pay more attention to these developments and adopt them. Unfortunately, service desks are not leveraging advanced AI and are struggling to tackle higher MTTR.

Some key causes of higher MTTR include,

IT complexity

IT has become quite complex. The lack of advanced tools makes it challenging for service desks to identify issues and offer resolutions, which increases MTTR.

Limited automation

Manual processes are slow and error-prone. Service desks use automation, but there is still some friction. Detecting, diagnosing, and suggesting actions is difficult.

Lack of knowledge base optimization

Interestingly, knowledge bases are usually only for agents. Employees need more opportunities to optimize their knowledge bases.

Poor self-service

Autonomous problem resolutions are limited to self-service. Though automation is applied to search information, it can create friction when employees seek help for multi-level workflows.

Lack of visibility and monitoring

Having worked with a legacy model for a long time, service desks struggle to gain visibility into organization-wide system performance. They need effective monitoring and visibility systems for real-time alerting.

Generative AI ensures a practical approach to overcoming the present challenges in service desks and improving performance to reduce MTTR.

Utilizing Generative AI to manage MTTR is essential to drive service desk success. Here are ways to do it.

Smart routing of tickets

For service desk agents, it is much easier to optimize MTTR or reduce resolution time for any incident by deciphering an incident message rapidly and accurately in real-time with GenAI.

Identifying actual incidents and triaging them is a critical job for service desk agents to communicate in real time and improve incident response.

However, this is a common scenario across all service desks, where they spend a lot of time exploring the real contexts of tickets about incidents.

This is where a service desk struggles, increasing MTTR.

Though there are search engines like Google that help determine what a particular incident means in Jira or other ITSM tools, information overload can be information fatigue, offering no real help.

Using NLP and NLU technologies in large language models, Generative AI helps decipher a message sent by employees to the service desk.

Domain-specific data such as ticket history in Jira or other ITSM platforms, recommendations suggested by agents, or new mitigation actions, etc., can be useful data points to build a predictive Generative AI model and use across ticketing systems to learn across user behavior. All the data points a model is trained with can combine to help offer the right prediction about an incident.

Below is a flow of how incident predictions work:

• Historical data fetched from the ticketing system

• Predictive model built using GenAI

• Further model improvement using current actions

• Prediction delivered for systems impacted by an incident

On top of it, there is a workflow engine embedded in a ticketing system that helps triage an incident without manual help and suggests an improved prediction to route the ticket to the right team to handle the issue.

The predictive model’s responsibility does not end there. It continues to improve its learning capability to understand incident patterns, improve its prediction capability, and help the service desk automate incident triage and route incident tickets to the right team.

Root cause analysis and problem mitigation

Usually, service desk agents try to navigate through history or user interactions to provide exemplary mitigation efforts for an incident.

The approach is less effective and impacts the MTTR.

A generative AI-embedded service desk can be powerful in seeking a root cause analysis, in which a model can define the exact cause of a downtime or performance issue.

At the same time, integrated GenAI technology can offer a mitigation plan. As a result, reducing MTTR for service desk agents is a more time-saving opportunity, as they no longer need to look for similar incident types in the service desk history.

Simultaneously, implementing an automated workflow is an efficient way to effectively escalate mitigation efforts and prevent downtime by implementing the proper fixes on the impacted applications or systems.

For example, if the CPU fails due to overutilization of disk space, a workflow may be automated to clean up the disk space and reduce MTTR without the need to wait for an agent to restore the problem.

Generative AI can also be effective in another scenario to help minimize MTTR by allowing a service desk agent to communicate in the chat interface of the observability tool, enter a prompt, and ask everything about what went wrong, how it went wrong, and what the proper fix for the impact is.

It can answer everything and offer a real-time fix for the problem.

New agent to get remediation help

Knowledge is always a significant resource for new agents handling an issue midway through a mitigation process when an experienced agent is off duty for a couple of hours or on a day off.

A service desk may have SOPs, remediation documents, help guides, and other resources to resolve the problem.

These resources may be helpful but ineffective in mitigating an incident in minimal time and reducing MTTR. If the goal is to reduce MTTR and offer a rapid restoration solution, who has so much time to read through these resources?

Good on-call interaction is always efficient for making real-time decisions and ample tools and resources to mitigate the incident.

Say, a user has a jammed paper issue in his printer, and there are not enough recommendations in the knowledge base to get out of the downtime so that he can ask for an agent's help. The agent then uses an LLM-powered chat interface to surface the right mitigation effort and help reduce the downtime issue for that user.

A GenAI model integrated with IT issues-related resources can offer a suitable recommendation to a new agent when asked questions in a question-and-answer playground. Besides, a group of agents can also participate in incident communication and help surface the correct incident mitigation responses.

Stakeholder communications or status updates

To reduce MTTR, it is always significant to keep stakeholders updated so that they are aware of the mitigation efforts and their constraints, if any, so as to prepare them for future incidents and allocate appropriate resources.

And that’s what it needs to craft a message to update stakeholders. It is not unusual for any enterprise to struggle to craft a message for updates post-incident resolution.

In some instances, enterprises involve as many as two or three people to craft a standard message to send to stakeholders.

Now, imagine the time and resources it takes to create an email or any other form of incident communication.

Generative AI is a savior here. By entering incident-related prompts, one can adeptly draft, review, and create an email or post-resolution incident communications and rapidly build post-incident communications with stakeholders.

Postmortems of incidents using Generative AI

A postmortem of incident resolution is critical to building resilience into operational efficiency, taking preventative measures for any system downtime, and reducing MTTR.

A postmortem report involves working around thousands of data points 一 incident communications in Slack or MS Teams, incident logs, monitoring alerts, and just about everything from the beginning of the incident triage to the end of resolution.

Just imagine having a team that can provide so much detail for a single incident and draft an error-proof postmortem report.

It seems challenging and an open ground for information slipping through the cracks.

However, having Generative AI to fetch every data point from the incident resolution journey makes the job easy while helping you automate the generation of an accurate postmortem draft for stakeholders.

Similarly, this automated postmortem report is a comprehensive guide for service desk agents to look into what went wrong, what the frequency of downtime for that particular application, what the areas of improvement, and above the cut, what can be done to prevent the same incident from recurring.

By drafting a postmortem report ahead of time, the service desk can help DevOps reduce MTTR and have complete peace of mind.

AI copilot to reduce frequent incidents

Other than using GenAI for the service desk to reduce MTTR, enterprises can choose to build AI Copilot using Generative AI and leverage domain-specific data such as employee training materials, internet resources, knowledge bases, software documentation, etc. to train the model and embed it in the enterprise application suite.

What you gain from the Copilot is a real-time alert that keeps you updated on the upcoming incident related to that application and provides the right resolution steps ahead of time for mitigation.

For example, your enterprise uses an Oracle database. If there is an attempt to infringe on the internal database, Copilot can flag the threat ahead of time and provide possible measures to prevent security breaches.

Active alerting to reduce downtime

A Generative AI-powered AI copilot is your AI assistant to help you end-to-end with routine and everyday tasks. If your service desk creates a workflow for upcoming threats, your employees can easily handle them without raising a ticket for the service desk agents, which will help reduce MTTR.

An alerting workflow works best if your employees are about to face password expiry. A timely alert can help avoid downtime and improve MTTR.

Reducing MTTR brings a lot of business gains for enterprise leaders. Here are what they include,

Long-term incident resolution - Yes, Generative AI offers an easy iteration for drafting comprehensive postmortem reports. This helps gain a data-driven understanding of future incidents and be incident-ready to mitigate them as per the MTTR strategy. Since the same type of incident is frequent, service desks can help IT teams take advantage of process efficiency and effectively reduce the tendency of future incidents.

More free time for critical incidents - GenAI automates incident triage and helps escalate incident tickets to the right team. It saves the service desk team ample time and helps DevOps get back to operations by resolving real-time incidents and allocating time to address more critical issues.

Low dependency on agents - GenAI and conversational AI are the perfect combination to facilitate service desk communications via a self-service chat interface. Using NLP and NLU technologies, Generative AI can help surface the correct information to mitigate incidents via a self-service capability.

Workativ provides the flexibility to build KnowledgeAI for the service desk chatbot and allows DevOps to resolve incidents at scale by engaging in self-service problem resolution or getting help from service desk agents.

Continuous improvement of incident management - GenAI has the unique ability to self-learn and build a predictive model on an ongoing basis, making it easy for the service desk to make real-time predictions of incidents, communicate effectively with the right team, and resolve the issues. As a result, it helps improve MTTR and enhances DevOps uptime and system performance.

Bottom line cost savings - GenAI does not need massive resources to monitor systems and ensure system performance continuously. Automated workflows can easily detect an anomaly in systems, alert the right team, and help reduce the impact of incidents. This is a considerable cost saving on the bottom line for average-scale enterprises that own a comprehensive IT infrastructure.

MTTR is a metric that service desks use to help DevOps keep their systems performant and running constantly without facing long-term incidents.

Mean Time to Resolution is always a challenging metric. Sometimes, mitigating a simple incident may be prolonged, or a critical incident may take less time than expected. Hence, the objective should be to allocate resources to manage time and have a strategy that helps optimize time and prevent recurrence.

This is where Generative AI helps build healthy service desks by effectively and efficiently aiding service desks in reducing MTTR.

By leveraging the power of Generative AI, Workativ can help service desks use its LLM-powered chatbot and apply self-service capability via Slack or MS Teams. This is a great, convenient tool for communicating incidents to service desk agents or implying self-service capability to solve low-priority ticket issues.

For example, Workativ offers KnowledgeAI technology with the power of hybrid NLU that helps enterprises build their custom knowledge bases with IT operations across industries and domain-specific IT issues. This competency allows users to leverage problem-solving techniques to autonomously solve common IT issues effectively and gain real-time MTTR benefits.

There is more to Workativ that can help you enhance MTTR and build an elevated user experience for your employees.

Want to learn more about Workativ conversational AI solutions and their MTTR reduction capability?

Get in touch with us or schedule a personalized demo today.