Five Specific GenAI Applications That Can Help Customer Service Associates (CSAs) Deliver Better CX

Ever since ChatGPT took the world by storm with its amazing capabilities, AI has been one of the most dominant headlines in the news, albeit many of those headlines have been about the potential dangers of AI.? Even so, AI advocates have sought to illuminate enormous opportunities available to be tapped, but many of the discussions still deal with broad possibilities.? This conversation attempts to distill the discussion down to specific GenAI applications that businesses can readily deploy in their contact centers to elevate CX, and we outline five of them.

1)?Record contact notes in real time and post them. ?One of the key actions that CSAs must accomplish in the process of responding to customers’ inquiries is to accurately capture notes about the customer’s request as well as the steps that were taken to address it. ?Currently, CSAs have to accomplish this note taking in real time while simultaneously problem solving with the customer to identify the best option to resolve their inquiry. ?Then after the contact, CSAs must ensure that the notes are posted and follow-up actions are completed before they can close out the contact to move on to the next customer. ?That places a significant burden on the CSA during the contact, which could impact the quality of solutions provided to the customer. ?

This is where GenAI can help. ?Natural language understanding (NLU) is one of the things that GenAI does well. ?As such, a GenAI?note-taker copilot?can take some of the pressure off CSAs by recording notes in real time during contacts and by posting them afterward as well as executing selected follow-up actions. ?Worst case, the?note-taker copilot?creates draft notes that the CSA can quickly edit and post. ?Best case, it delivers spot on notes that don’t need any further modifications.

Either way, this will enable CSAs to prioritize the interaction with the customer as well as problem solving with them to effectively address their inquiry and deliver an outstanding experience in the process. ?Truth be told, note taking and contact wrap-up aren’t the part of the job CSAs enjoy the most so even relieving them of some of this would also improve the experience for CSAs, thereby creating a better work environment for them. ?That also bodes well for their interactions with customers.

2)?Summarize prior contact history.??One of the most significant differences between GenAI and earlier implementations of AI is that GenAI excels at summarizing, synthesizing and composing content. ?How smart GenAI copilots are to a large extent is determined by what source information they are trained on. ?As such, a GenAI copilot that is trained on the data sources CSAs use to bring themselves up-to-speed during customer contacts could be invaluable for enhancing CX. Instead of CSAs hopping between systems and from screen to screen, the copilot could poll backend systems to bring forward salient information from the customer’s most recent touchpoints, especially ones that did not result in the customer completing his/her objective. ?That would quickly alert CSAs to the context for the customer’s inquiry and help illuminate a path toward a favorable outcome. ?The CSA would be able to help the customer pick up where they left off instead of asking them to tell their story from scratch.

The good news about this is that companies don’t have to wait until all of their ducks are in a row to create a 360” customer record. ?With GenAI, they can create a?contact-history copilot?that has hooks to the various touchpoints customers use to engage with the company. ?In much the same way that ChatGPT can summarize data points from a variety of sources on the web to compose essays, papers, songs, images, etc., the?contact-history copilot?would use data points from touchpoints to summarize the customer’s recent contact history and create an overview of the path that brought them to the current contact. ?The copilot can be trained to overcome such as the same customer being represented in different ways in different systems.

3)?Recommend options for resolving customers’ inquiries. ?Much like the contact-history copilot, the recommended resolutions copilot hooks into backend systems, except they’re the ones that store information about company policies, procedures, and successfully resolved inquiries. ?Armed with this information this copilot presents a CSA with options for the best approach to address the customer’s inquiry. ?This is particularly useful for contacts where the resolution isn’t straightforward.

4)?Decipher customer accents.??One of the challenges that customers frequently encounter when working with live agents is understanding their accents. ?It turns out that this works in both directions because it’s not uncommon for agents to have trouble understanding customers’ accents, and this makes for a difficult interaction. ?This is another area where GenAI can help.?

?One company stumbled on this use case for GenAI while using it to offload straightforward, repetitive contacts.? They trained their voice assistant to recognize a wide variety of accents to take requests for driving directions and were so successful at it that 90% of those contacts are now handled by the voice assistant.? Subsequently, they recognized that the voice assistant was so good at recognizing accents that it was capable of handling more complex contacts, sometimes even better than live agents.? Now imagine a closed captioning copilot for live agents that uses the voice assistant’s accent recognition capabilities to assist agents with understanding customers in real time.? The benefits are obvious because agents cannot effectively address inquiries they can’t fully understand.

5) Offload straightforward, repetitive contacts.? We already alluded to this application for GenAI as we discussed the previous use case.? Every contact center has straightforward, repetitive contacts that don’t necessarily require the intervention of a live agent, e.g., getting driving directions, checking account balances, checking order delivery status, etc.? The good news is that agents are not energized by taking these contacts either.? They’d rather focus their attention, energy, and creativity on contacts that make a more significant difference for customers.? With these contacts off their plates, agents can do exactly that.? As such, many businesses have already prioritized, and some have already made significant progress utilizing GenAI for this application.

The bottom line is that businesses can harness the amazing capabilities of general purpose GenAI systems to improve contact center CX by targeting them at specific applications, connecting them into their backend data sources, training them for the intended purpose, and, of course, walling this information off from the outside world. ??

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