20 Use Cases for Generative AI In Customer Service

Less than a year since ChatGPT-3 burst onto the scene, GenAI is opening up contact centers to many new possibilities

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20 Use Cases for Generative AI In Customer Service
Contact CentreInsights

Published: October 2, 2023

Charlie Mitchell

The customer service space is awash with an unprecedented wave of innovations, and there is one culprit: generative AI (GenAI).

In only months, it has expanded contact center agent-assist portfolios, shaken up knowledge management, and transformed conversational AI applications.

Moreover, it has redefined how low-/no-code tools work, with developers creating customer service applications and campaigns through written prompts alone.

Such innovation has changed how many contact centers build bots, self-service applications, and proactive campaigns forever.

Yet, these are just the headlines. To delve deeper into how generative AI has changed customer service – check out the 20 new use cases below.

1. Auto-Generating Customer Replies

Example: Service GPT by Salesforce

Generative AI understands customer intent. That capability sits at the core of many new customer service use cases for the technology – such as auto-generating customer replies.

Indeed, GenAI applications – like Service GPT by Salesforce – can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer.

Such knowledge sources likely include web links, the knowledge base, CRM, and various other customer databases – which may also allow for personalization.

In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers.

That final part is crucial, keeping a human in the loop to lower the risk of responding with incorrect information and protecting service teams from GenAI hallucinations.

2. Assisting Agents as They Type

Example: Expanding Agent Replies by Zendesk

Before LLMs burst onto the scene, many people played with generative AI when using tools like Gmail. Indeed, the email tool predicts how a sentence will likely end, and – if it guesses right – the user can hit the “tab” button, and it’ll complete their message.

Embracing the advent of large language models (LLMs), Zendesk built a customer service version of this – on steroids.

Its “expanding agent replies” solution allows agents to type the bare bones of their response and then fleshes it out for them, saving them time in responding to customers across digital channels.

Again, the contact center must plug the solution into various knowledge sources for this to happen – as is the case across many other use cases – and an agent stays in the loop.

3. Automating Note Taking

Example: Call Note Automation by Sprinklr

Agents often receive a barrage of information, which they must remember. Yet, keeping track of all the critical details is tricky.

As such, many supervisors encourage note-taking. However, even that can impede an agent’s ability to engage in active listening as they multi-task, resulting in increased resolution times.

Sprinklr’s “call note automation” solution aims to overcome this issue by jotting down crucial information as the customer talks.

Agents can refer to these golden nuggets of information when forming their replies – instead of relying on the unstructured, bulky transcript.

Knowing this, they can stay focused on what the customer is saying, not trying to remember what they said previously, which should improve their call handling.

4. Unearthing Customer FAQs

Example: Generative FAQ for CCAI Insights by Google Cloud

As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them.

With this information, contact centers can understand their primary demand drivers.

This enables the service team to prioritize actions to improve contact center journeys. Such actions may include improving agent support content, solving upstream issues, or adding conversational AI.

Google Cloud’s Generative FAQ for CCAI Insights allows contact centers to upload redacted transcripts to unlock this capability. The tool may also generate conversation highlights, summaries, and a customer satisfaction score to store in the CRM.

5. Automating Post-Call Processing

Example: Auto-summarization for Genesys Agent Assist

When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system.

Generative AI solutions can now automate this process, shaving seconds from every contact center conversation and – therefore – saving the service operation significant resources.

CCaaS Magic Quadrant leader Genesys is one vendor to offer such a solution – automating these post-call processes for agents to review, tweak, and publish in the CRM after each conversation.

Moreover, the vendor standardizes the format of each conversation summary. That makes it easier for future agents – handling follow-ups – to get to grips with what happened on the previous call.

6. Simplifying Call Transfers and Escalations

Example: The Verint Interaction Transfer Bot

When a contact escalates, the customer must often repeat their problem and the information they shared with the first agent – which is a common source of customer frustration.

Yet, generative AI can help by summarizing the contact so far and sending that to the second support agent or supervisor.

As a result, they can continue the conversation from where it broke down, saving time and preventing the customer from repeating themselves.

The Verint Interaction Transfer Bot does precisely that. No matter if the first agent is a human or a bot, it sends a quick, informative summary rather than an unwieldy transcript.

7. Detecting Customer Service Automation Opportunities

Example: AI Insights by Five9

By uncovering customer FAQs, generative AI helps contact centers spot opportunities for conversation automation. Yet, AI Insights by Five9 takes this further.

The solution takes customer conversations and groups them by various traits, like intent.

From there, it applies GenAI and NLP to search for patterns within these groups of contacts, suggesting process and automation improvement opportunities.

In doing so, the tool indicates how often these opportunities present themselves and the possible cost-savings the contact center can make by acting on them.

8. Adding Context to Automated Quality Scoring

Example: Manager Assist for Amazon Connect

Many CCaaS providers now offer the capability to automate quality scoring, giving insight into all contact center conversations. This provides a more holistic view of agent performance.

Generative AI takes this further by not only automating “what happened” questions – i.e., did the agent say this or do that? – but additional criteria too.

The Manager Assist for Amazon Connect solution does this, harnessing GenAI to auto-fill scorecard criteria such as: Did the customer leave the call satisfied? Or: Did the agent offer any concessions?

Alongside this, the solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it.

9. Pinpointing Agent Coaching Opportunities

Example: Manager Assist for Amazon Connect

Alongside auto-filling more of the quality scorecard (as above), Manager Assist for Amazon Connect provides an automated agent performance summary for every customer conversation. That summary includes coaching and positive recognition opportunities.

Indeed, in an example that AWS shared, the solution generated feedback such as:

  • “The agent could have been more proactive in offering a discount or resolution earlier rather than waiting for the customer to ask. Taking initiative shows commitment to fixing the issue.”
  • “The agent did a good job apologizing and taking accountability for the website issues. Expressing empathy for the customer’s frustration is important.”

The innovation also inspires cooperation between quality assurance and coaching teams, who can create a connected learning strategy to bolster agent performance.

10. Alerting Supervisors to Agent Issues

Example: NICE Enlighten Actions

Generative AI unlocks several chances to turn insight into action – including insights that conversational intelligence tools uncover.

For instance, NICE uses such tools to detect customer sentiment in real-time.

The Forrester Wave CCaaS leader then applies GenAI to monitor the trend in sentiment and alert the supervisor when it drops significantly. They may then swoop in and save the day.

Alongside sentiment, contact centers may harness GenAI to alert supervisors when an agent demonstrates a specific behavior and jot down customer complaints.

11. Spotting Gaps In the Knowledge Base

Example: CustomerAI by Twilio

To automate customer queries, GenAI-based solutions drink from various knowledge sources. Typically, the contact center knowledge base is the most predominant.

Yet, sometimes, there is no knowledge article for the solution to leverage as the basis of its response.

When this happens, it may flag the knowledge base gap to the contact center management, which can then assess the contact reason and create a new knowledge article.

As a result, the GenAI application has something to work from – as do live agents during voice interactions –enhancing the contact center’s knowledge management strategy.

12. Generating Knowledge Articles

Example: CustomerAI by Twilio

Alongside spotting gaps in the knowledge base (as above), some GenAI solutions can create new articles to plug them.

CustomerAI by Twilio is an example of such a solution. It understands customer intent, assesses how agents and supervisors have successfully handled such queries, and uses that information to develop a new knowledge article.

A service team may then have a supervisor or experienced agent assess the knowledge article, edit it, and publish it in the knowledge base to keep a human in the loop.

Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to quickly comprehend and action them.

13. Simplifying Self-Service and Bot-Building Activities

Example: Generative AI App Builder by Google

Generative AI is changing low-/no-code solutions. It’s allowing users to build applications using natural language alone instead of drag-and-drop tooling.

That will impact many aspects of customer service, and chatbot development offers an excellent early example.

Consider the Generative App Builder embedded into Google’s CCaaS solution: The Contact Center AI Platform. It allows contact centers to build bots in minutes.

Indeed, the developer can explain – in natural language – what information the bot should collect, the tasks it must perform, and the APIs it needs to send data. Then, the platform spits out a bot, which the business can adapt and deploy in its contact center.

14. Increasing the Scope of Conversational AI

Example: Nuance Mix

Now part of Microsoft, Nuance was one of the first vendors to add ChatGPT to its conversational AI platform.

It harnessed the LLM in such a way that if a virtual agent receives a question it hasn’t had training to handle, generative AI provides a fallback response.

The weblinks and contact center knowledge sources that the conversational AI platform integrates with inform the response – helping to automate more customer queries.

Alongside the answer, the GenAI-powered bot cites the sources of information it leveraged, which the customer can access if they wish to dig deeper.

15. Keeping Self-Service Interactions On Track

Example: Conversation Booster by Nuance

Automated customer service interactions sometimes break down when customers change their intent halfway through a conversation – confusing the virtual agent.

The Conversation Booster by Nuance uses generative AI to combat this issue as users carry out self-service tasks within the bot. These may include making payments, scheduling appointments, or updating their personal information.

Indeed, the bot detects the intent change and presents a message to refocus the customer, pull the conversation back on track, and improve containment rates.

16. Simulating Conversations for Bot Testing

Example: Conversation Simulation by Cognigy

Like Nuance and Google, Cognigy has pushed the boundaries of generative AI innovation in customer service, as its “Conversation Simulation” tool exemplifies.

The tool bombards virtual agent applications with mock customer conversations to test how well the bot stands up to various inputs.

By pairing this with the Cognigy Playbooks reporting platform, service teams can verify bot flows, validate outputs, and add assertions.

The Customers’ Choice conversational AI vendor – as per a 2023 Gartner report – defines an “assertion” as the conditions a bot must meet to pass a test.

17. Extracting Insights from Customer Feedback

Example: Smart Summary Generator by InMoment

LLMs can consume large amounts of information, strip away trends, and convert those into concise, structured takeaways.

InMoment’s Smart Summary Generator does this for customer feedback. Indeed, the GenAI-powered solution first ingests various sources of such feedback – including surveys, conversation transcripts, and online reviews. From there, it generates a trends overview.

If a contact center can continuously feed such a solution with knowledge sources, contact centers can continually monitor customer complaints and act fast to foil emerging issues.

18. Defining Troubleshooting Steps

Example: Flow Modelling by Cresta

By assessing successful conversation transcripts – across a particular customer intent – generative AI can assimilate the resolution ideal path.

Flow Modelling by Cresta offers such a solution, determining this path based on its impact on various customer experience and business outcomes.

Meanwhile, the capability uncovers the characteristics that lead to successful resolutions. These may inform agent coaching and scorecard creation initiatives.

19. Augmenting Search Functions

Example: Industry Benchmarks by NICE

Search engines can auto-generate answers to written questions with generative AI. That functionality may impact several customer service applications.

For instance, consider the knowledge base. When an agent types in a question, it can pop up the answer, so the agent doesn’t have to trawl through articles and documents to find it.

Such a capability may also bring new customer service possibilities to life. The Industry Benchmarks by NICE is an excellent example of this.

At its heart, the solution contains a wealth of anonymized contact center conversation data that NICE has pulled together and used to develop sector-specific benchmarks for many metrics.

Such metrics include customer sentiment, call reasons, automation maturity, and more.

Upfront, the vendor installed a GenAI-infused search engine so service teams can see how they stack up against the competition by simply entering a few written prompts.

20. Expanding Service Operations

Example: Field Service Recommendations for Oracle Fusion Service

Nowadays, contact center agents and store associates are not the only employees to offer customer service. Thanks to the proliferation of cloud communication tools, all employees can chip in – and generative AI can support them.

Consider a field service worker. They often engage with customers to snuff out any potentially simple fixes before making a site visit. Doing so frequently saves them a time-wasting visit.

To increase the success rates of these upfront conversations, Oracle has added a GenAI-powered Field Service Recommendations feature to its customer service CRM.

The tool offers these employees real-time AI-powered recommendations from troubleshooting source material – including product manuals – to support them in solving issues remotely.

Expect Much More Generative AI Innovation In Customer Service

As LLMs become more sophisticated, expect further waves of customer service use cases for generative AI to rise up.

For instance, the latest iteration of ChatGPT – GPT-4 – can analyze and classify images. Such a capability may allow contact centers to automate more customer conversations.

Consider a scenario where a customer takes a photo of a faulty product and posts it on social media. The new image recognition capabilities can verify if it belongs to the business and use this information to automate an appropriate response to the problem.

CCaaS vendors may already be trying to execute such a possibility. They could also attempt to action another new capability: mimicking a user’s writing style.

That new LLM feature may further enhance automated customer replies by ensuring they align with the brand’s tone of voice.

Yet, even with some of the capabilities vendors leverage today, arenas such as reporting, routing, and workforce management seem ripe for GenAI augmentation.

As such, expect generative AI to stay in the CX headlines for many years to come, turning contact center insights into actions.

Is there an excellent generative AI use case for customer service that I’ve missed? If so, let me know by commenting on my LinkedIn post.

 

 

Artificial IntelligenceCCaaSGenerative AI
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