With advanced technologies such as Artificial Intelligence(AI) and Machine Learning(ML) widely adopted by businesses, there is a large push towards building solutions with modern tools and technical frameworks.. However, most of the time, technical advancements are perceived as growth in the technical maturity ladder for individuals, teams, IT or business organizations. Such situations often downplay the value proposition of embracing new transformations.

As a result, businesses tend to deboard such solutions which can cost precious dollars to the business. In several occasions, businesses have not understood the AI engine making decision on behalf of an experienced personnel who does it manually or overrides AI engine outcomes eventually pushing the developed solution to obsolete path. The trust factor of such AI algorithms is at stake with explainability being the centre of a hidden rationale.

This blog series is an attempt to discover the potential real-word use cases for AI infusion into our IBM Sterling Order Management suite of applications from a business perspective involved in our customer organizations.

Edition 1

Persona : Call Center Agent

Problem statement

• Enterprises offer multiple channels of communication, feedback, issue resolution through website, call centres, mobile app, surveys, etc. Among these modes, the call centre is a unique form of engagement for businesses to foster a human connection with their customers
• During these conversations, customer care representatives (CSRs) spend lot of time looking up customer details, recent transactions about a customer order, payment, and other inquiries. This requires CSRs put a customer on hold in order to search key attributes, navigate to the appropriate screen to further drill down into multiple data sources to completely understand a situation and answer the customer
• There is normally a waiting window for CSR who is trying to fetch details – causing customer frustration
• Such circumstances create a lot of negative experiences for customers which can result in brand damage, cancellations, lost sales to competition and unnecessary escalations to CSR manager and so forth

Solution outline for call center with a Voice Assistant (CCVA)

• Integrate voice assistant with an enterprise CRM application to enable CSRs to do voice-based search and actions in the language or dialect of his/her choice to more pro-actively converse and engage with customers. This helps to reduce call hold times, resolves queries faster and save labor hours
• The voice assistant leverages Natural Language Processing (NLP) algorithms of AI services along with contextual understanding of complex customer queries which require multiple data elements to be combined without need of keying on several forms and attributes for searches. Post search results, data points required about policies, rules, protocols or any meta data information to perform next best actions is also available to CSR by voice based look up. Further, actions are also performed by voice trigger by CSR to finish the transaction and close the call

Sample Use Case

Stay tuned for the second edition of the blog next week!