Support
Augmented Product Assistant
Anyone familiar with the software development lifecycle will tell you that the product launch date doesn’t draw a line under the job. Whether it’s to improve performance, advise users, or fix bugs, any software product that goes out into the world will need support.
The mechanisms that organizations put in place to deal with post-launch software support are pretty much the same as for any other customer services issue. The user contacts the support team and flags the issue, and the support engineer creates a ticket for resolution.
All too often, engineers find support issues can be a slog. There can be numerous manual steps in issue resolution, tickets can proliferate, and they can’t easily see or search semantically through historical tickets to see whether they can save time by applying previous approaches to new cases. Caseloads begin to mount, and there are so many decisions to make that it becomes difficult to know where to start.
In such circumstances, organizations have several requirements. They need to accelerate their ticket resolution time. They need to find ways to resolve complex ticket issues more efficiently. They need to train their product support teams more quickly and comprehensively. And they need to explore GenAI’s potential to resolve these issues, in order first, to make life easier for software development team members, and second, to deliver a superior customer experience.
Capgemini Engineering’s Augmented Product Assistant solution (see graphic) is designed with these challenging scenarios in mind. Driven by natural language processing (NLP) and the power of large language models (LLM), it makes life easier for team members, and helps to resolve issues faster and more satisfactorily for users.
For example, Augmented Product Assistant accelerates ticket resolution by exploring ticket information, by effectively categorizing them, and by noting similarities between them and also with previous instances. It improves product quality by identifying code modules or product components that are most prone to bugs, assigning them with error scores for prioritization. It also supports managers by conducting predictive analytics on ticket traffic, anticipating surges and drops in ticket flows, and so making it easier to plan resource allocation. In addition, managers can gain deeper insights into customer sentiments and satisfaction levels.
All these features have been developed using Capgemini-generated machine learning (ML) models. Other Augmented Product Assistant features take advantage of Generative AI. For instance, a GenAI-enabled bot can be contextualized to software products and their ticket history, guiding support engineers and augmenting both their knowledge and their ability to see issues through to their resolution. What’s more, the solution is platform-agnostic, and so can be integrated simply with whatever ticket management software the user organization has adopted.
Delivering business value…
Augmented Product Assistant has been delivering positive results. Defect management has improved: the triaging of errors has been accelerated, and manual effort and the need for expertise in specific domains have been drastically reduced, thereby reducing the time spent managing issues and improving the accuracy of automated tasks.
The contextual support facility means that organizations have simpler and more rapid access to documentation and historical resolutions, with faster ramping up and greater productivity of new resources. As a result, client organizations have seen a substantial proportion of tickets resolved by GenAI support assistance, and a significant increase in issue resolution efficiency.
The real-time organizational planning and sentiment analytics functions have enabled users to gain a deeper understanding of customer feedback, and to use real-time data forecasting to think ahead on resource allocation. Organizations have experienced notable increases in customer satisfaction scores, and increases too in conversion rates.
This AI-enabled streamlining of support hasn’t just increased efficiency and lowered operational costs – it’s also liberated product support agents to spend more time on the most important parts of their roles.
… and making life easier
In a world in the midst of digital transformation, the software products on which organizations and their customers depend will continue to proliferate and to grow in importance. At the same time, this sheer growth in product volume will mean that potential support issues continue to multiply. It’s difficult when trends pull in opposite directions.
Solutions such as Augmented Product Assistant can not only learn and respond swiftly and naturally – they can also scale. Which means that, like all good technologies, Augmented Product Assistant meets a basic human need – which is to make life easier.
Augmented Product Assistant – what’s involved:
Faster defect management – intelligent triaging helps to prioritize and focus effort
Contextual support – with historical issue analysis and natural language communication
Real-time resource planning and sentiment analytics – better for managers, better for customers
