RPA Incident Management: 5 IT Metrics RPA Can Improve in '24
Google Trends shows a consistent level of interest in incident management (Figure 1). Incident management is important because it aims to minimize disruptions in IT workflows.
Manual incident management, however, is inefficient.
For instance, an incident report when a data breach happens is a step in incident management. IBM reports that in 2022, it took companies an average of 277 days to identify and contain a breach. By their estimates, lowering that time to 200 days or less can result in $1.12M in savings.
AI tools can be leveraged to improve IT operations. For instance, a tool that can quicken, streamline, and improve incident management is RPA, or robotic process automation.
In this article, we will look at the 5 ways which RPA can improve incident management metrics in a company.
1. Real-time alerts
The first use case of RPA incident management is sending alerts to team members whenever an IT incident takes place.
An important KPI in incident management in the mean time to acknowledge (MTTA). This is the average time it takes an IT staff to acknowledge the existence of an issue. The longer the MTTA, the higher the chances of the issue growing in size and impact.
By sending real-time alerts to team members, companies can increase the likelihood of them being notified of incidents, and thereby, possibly reducing the MTTA. Faster response rate will reduce the down time of the system/applications.
In manufacturing, predictive maintenance enables the technicians to predict and tend to pain points quickly prior to them worsening. A similar idea can be applied to IT incident management, when through repeated interaction with data, RPA bots can use their ML capabilities to predict outages and incidents before they occur and send the alerts in real-time.
2. Faster response time
RPA bots can be programmed to locate the issue, create a ticket for it, and send the ticket to the team member.
This enables the IT staff to reach the bottleneck via one click. The privilege of knowing exactly where the incident has happened, in addition to being able to get there quickly, increases the likelihood of faster response time.
Mean time to resolution (MTTR) is the KPI that measures the time it takes to resolve an issue. Giving your team precise information about the nature of the issue, in addition to the opportunity to get to the issue quickly, can decrease MTTR.
Faster response time to identified issues and outages could increase customer satisfaction and reduce the incurred monetary costs.
In the supply chain, for instance, lack of visibility into the real-time location of shipments can derail manufacturing plans. Once such an issue is detected, a fast response time to restoring the service can aid the company.
3. Accurate issue routing
Intelligent RPA bots can read customer or employees’ ticket issues to understand their meaning and the context to route them to the right agents.
Average incident response time (AIRT) is a metric for tracking the time it takes for a response to an issue. Routing the issue to the correct team member is a critical factor that reduces ARIT. For instance, in expense management automation, employees should submit their expenses to the system on specific dates of the month. If the software does not work for an employee, for some reason, the issue should be directed to the right personnel to resolve it.
The person submitting the ticket might not know who to send it to. But they can fill out the issue ticket and the RPA bots will use their NLP and OCR capability to read the content, understand the category it falls into (i.e., technical, administrative, etc.), and then search through their knowledge person to find the employee whose job description matches the ticket info most closely.
Then they can send it to the correct team member. Accurate issue routing can reduce response time by allowing the most suited person for the task to take up the responsibility.
SLA (service level agreement) is a document between the provider and the customer by which the former’s responsibilities and obligations towards the latter are specified.
Companies can program RPA bots to monitor the SLA terms of each customer in real-time to notify the managers whenever a threshold hasn’t been reached. For instance, a data crowdsourcing provider might have an agreement with a client to provide them 5 relevant datasets each month.
RPA bots can be programmed to monitor and note each time a dataset has been sent to a client. Close to the end of the month, the summary of the KPI can be sent to the manager to review and assess whether it’s SLA-compliant or not.
Manually following up on such performance metrics is time-consuming and error-prone. Delegating it to RPA bots minimizes the possibility of missed performance metrics as they gather and display the info on visible dashboards.
5. Improved troubleshooting
Most RPA solutions today come packed with process mining capabilities. Or they can be leveraged separately. Automated root cause analysis is a useful process mining tool.
Whenever an issue is detected, automated root cause analysis can point out the potential causes behind them.
In finances, RPA can automate reporting by extracting the financial data, putting into pre-made templates, and providing it to the financial analysts for analysis. The same could be done for root cause analysis. The data from this practice can be put into a sheet and be sent to the relevant team members.
This assists the IT staff by reducing the number of possibilities and giving them a place to start investigating right away. Accurate diagnosis and troubleshooting could result in an increase in first touch resolution rate, a metric that tracks the number of times a flagged issue resurfaces.
For more on RPA
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Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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