Technical efficiency has become an inherent, modern-day expectation in defining positive experiences with organizations around the globe. Whether or not people understand the nuances of the specific technologies powering technical efficiencies, they do recognize when workflows and interactions are supported well with automation. Brands that have established reputations for delivering a great customer experience are often ones that make digital interactions seamless, simple, and accurate.
Most of these automated exchanges are powered by robotic process automation (RPA) and machine learning. Both technologies excel at replacing tedious, manual processes by automating repetitive tasks to scale digital processes and improve operational workflows. Like all software, both RPA and machine learning systems are prone to errors that must be regularly monitored and addressed to minimize disruption of services and maximize technical efficiency.
While inflation costs are leading companies to cut spending across departments, IT spending is expected to increase to $4.6 trillion USD in 2023 with a clear emphasis on implementing digital automation across all lines of business. These organizations recognize the value that automation brings will lead the way in reshaping how business is conducted in the modern world. The challenges contrasting this growth are production errors in software which can impact productivity, causing delays, crashes, and data loss. Automation errors can lead to wasted time and resources and increase operational costs.
Manually Addressing Automation Errors
The functionality and performance of automated processes are constantly changing, and additional solutions, often called bots, are built, and deployed regularly. A significant amount of human oversight is required to effectively monitor and manage bot performance. This oversight includes routinely gathering the workload information relevant to each process, analyzing work rates and status updates across team members, and extracting all process exceptions, or errors, that have occurred in a given amount of time.
In the event of bot anomalies or failures, operations teams must decide on a specific course of action such as restarting an individual bot or an entire machine, raising the issue with either the bot developer or the infrastructure team, or choosing to ignore the error based on some other guidance or internal decision-making criteria. Unaddressed errors or a high frequency of process interruptions will erode trust in automation across both business users and consumers.
The efforts required to maintain and manage RPA bots have led to significant investments in both time and money that go far beyond the costs to initially create and deploy the bot. The cost to minimize disruption through manual efforts is wrapped up in the following tasks:
- Highly tedious maintenance tasks are completed at an individual level, creating inconsistencies in how issues are addressed and redundancy in efforts across bot management teams.
- Timely response to incidents and management of sudden peaks in workload are tedious to manage due to the complexity of deployment schedules across processes.
- Multiple handovers both within and across teams are required to effectively address and manage errors.
- Agreed upon service level agreements (SLAs) and response times are often missed due to errors, disrupting operations, and delaying subsequent processes from being executed.
Additionally, RPA operations team must keep looking for success/failure emails from the bots or keep checking the status of the bots in the orchestrator or the control panel. Failure alert emails often are absent of any immediate action needed for the exceptions or whether the instance is routine and should be ignored. Deciphering these emails can be an arduous task. Exceptions need to be graded by their impact and impact is often dynamic in nature. Hence infusing AI into error management creates a dynamic, adaptive way to classify and separate what is mission critical vs not and help prioritize error remediation.
In a typical scenario, the operations team decides on which follow-up actions should be taken.
- Restart the bot
- Restart the machine
- Raise the issue with the developer
- Raise the issue with Infra team
- Ignore
These repetitive steps, exaggerated with scale, are the reasons why the RPA maintenance costs are significantly higher than calculated initially. Apart from increased costs, these increased efforts limit the ability of an operational center to scale. It also impacts business’s trust in RPA as the entire process is prone to manual errors.
Automation continues to transform businesses in a significant way, and yet, ironically, the management of errors related to automated processes are still a painstakingly manual process.
Optimizing Automation Management
Intelligence is needed to improve and optimize the management of automated solutions. While RPA teams are responsible for monitoring, identifying, and resolving automation errors, the application of automation and AI to those specific areas of responsibility has been absent.
Turbotic offers an innovative solution to these challenges that are shared among every automation team - an intelligent engine specifically designed to optimize the management of automated processes. Turbotic’s engine not only monitors all RPA bots deployed in an organization, but it also proactively identifies and corrects any issues or errors that arise.
Turbotic optimizes automation management in the following ways:
- Persistent Monitoring - Turbotic provides 24/7 monitoring of all bots and captures the number of errors for all bots. If the number of errors exceeds a certain threshold, Turbotic will flag if the error is important or requires attention. It also detects irregularities that might be contributing to errors such as a sudden influx of bot traffic.
- Anomaly Detection - This feature automatically identifies any irregular behavior across processes by using historical data to understand bot run pattern and error pattern to provide more granular error tracking and troubleshooting. Turbotic classifies anomalies into categories (i.e.: code, user interface, bot deployment, resource allocation, robot service, control panel or business) to ensure the right anomalies reach the right teams for troubleshooting. This saves time in monitoring and identifying issues, as well as minimizing performance interruptions.
- Error Classification - Turbotic uses a supervised text classification model to categorize RPA production errors into pre-defined error categories that are each mapped to specific actions. Classifying errors by type, whether it be infrastructure, code, UI, or something else, minimizes the need for multiple handovers between teams and improves the accuracy of error handling.
Increasing Resilience with Efficiency
In an ever-evolving world of technology, the need for automation has grown, and continues to grow, exponentially. To scale effectively, maintain a competitive edge, and remain resilient through varying degrees of economic certainty, companies must adopt digital automation across business processes.
The more processes are automated, the more complex operations become, often requiring companies to increase the headcount and resources needed to manage digital workloads.
“Without optimizing the technology and processes needed to effectively manage and optimize automation initiatives, companies risk creating negative impacts to their operational efficiency and customer interactions.”
Turbotic addresses these challenges head on, by providing intelligent orchestration of automation initiatives that enables teams to solve everyday automation challenges with speed, agility, and accuracy. This pioneering platform automates the manual efforts associated with RPA and AI management and reduces lead time for error resolution, which ensures trust and efficiency across RPA operations.
Companies who adapt to supporting great experiences through automation are the ones who will provide the best customer experiences and remain resilient through turbulent economic conditions.
