Infosys TechCompass

AI Operations and Automation

Trend 7: Shift to a sentient digital workforce from simple runbook automation

Organizations now realize that digital workforce performs cognitive functions that help them move toward self-learning autonomous systems. A repository of bots accelerates this adoption, led by varied inbuilt functions — from sensing an anomaly to resolving the failure and learning from the episode to improve the prediction. The key to this acceleration is the ability of bots to interact across multiple RPA and non-RPA technologies.

Consolidated automation workflows drive a successful digital workforce under a unique identity to create a digital twin of a human worker. This aggregation with the identity is also critical for auditing and tracing the actions of the digital workforce. The depth of the evolving cognitive system capabilities will define the acceptance and transition to these digital workers.

A leading European consumer goods manufacturer built a sentient enterprise by leveraging three critical solutions from Infosys: Digital Brain, Live Enterprise Application Platform (LEAP), and Infosys Cognitive Automation Studio. Digital Brain builds knowledge graphs that validate enterprisewide data, LEAP provides a cognitive-first dashboard to detect anomalies and predict failures, and Infosys Cognitive Automation Studio builds a cluster of cognitive bots to leverage Digital Brain and LEAP.

AI Operations and Automation

Trend 8: Ticket triaging, solution prediction, and auto resolution become eminent

A critical task in IT operations is to service tickets that either report failures or user requests. However, improper routing and wrong categorization of tickets are typical challenges, resulting in delayed mean time to resolve (MTTR). Previously, deterministic automation routed the tickets to the correct assignee based on defined rules. Today's AI solutions learn from historical data and identify the right category of the ticket based on the problem/request details. These solutions aid in faster response and resolution, greatly reducing MTTR.

Now, systems rely on deterministic rules to identify the resolution path and corresponding automation, if available. While this is a step forward from the manual triggering of a relevant automation solution, the technology is still limited by defining the rule upfront for identification. New AI developments predict solutions based on historical trends and knowledge artifacts. Once the resolution is identified, performing that action becomes just a matter of triggering the right bots. This helps create, in essence, self-healing systems.

An Asian tax regulatory body used Infosys' in-house solutions, including an intelligent automation tool, to classify, enrich, and route their tickets to the right support engineer efficiently. It reduced the entity's MTTR by 20%.

FINACLE automated its ticket classification and analysis through Infosys NIA to identify top solutions. It a self-learning solution that improves accuracy and relevance based on user feedback.

AI Operations and Automation

Trend 9: Rapid advancements in computer vision and AI ease field service operations

Rapid advancements in AI and its applications in fields like computer vision through object detection, image classification, speech processing like voice to text, sentiment detection, and autonomous driving create intelligent robots that automate and simplify several tedious tasks in field service operations. AI in field service operations includes better information sharing, real-time technician updates, automated workflows, digital form data collection, and improved data analysis. This gives technicians more freedom and flexibility to perform higher value work, leading to improved customer experience and employee experience outcomes. Scheduling and managing tasks become easier, matching the right technician to the right job.

An overhead conductors manufacturer wanted to apply specialized coating on its installed power conductors to extend their lives and evade expensive replacements. Intelligent robots controlled remotely via cellular/RF used advanced vision and control systems to automate and perform the coating process.