Putting AI to work for Customer Service

Summary

In this transcript, Manish Goyal, Senior Partner at IBM Consulting, discusses how AI and generative AI can revolutionize customer service. He highlights the use of AI to enhance self-service, support human agents, and optimize contact center operations. Goyal explains how AI-driven tools can improve efficiency, reduce response times, and deliver delightful customer experiences. He emphasizes that AI can automate complex tasks, provide real-time insights, and help businesses meet rising customer expectations, ultimately transforming customer interactions into seamless, proactive, and omnichannel experiences.

Takeaways

• 📞 Customer service expectations are higher than ever, with clients demanding quick, satisfying responses across all channels.
• 🤖 Generative AI can significantly enhance customer service, automating complex tasks beyond previous technologies.
• 💬 Self-service tools, like virtual agents and chatbots, can now offer more natural and conversational experiences thanks to generative AI.
• 👩‍💼 Generative AI can improve agent productivity by quickly retrieving and summarizing relevant information, helping to resolve customer queries faster.
• 📈 AI-driven insights from call transcripts can identify operational inefficiencies, classify complaints, and drive proactive problem-solving.
• 📝 Generative AI can automate after-call work by transcribing and summarizing conversations, improving consistency and reducing agent downtime.
• 🔄 AI enables seamless omnichannel experiences, allowing customer requests initiated in one channel to be completed in another without friction.
• ⚙️ Companies should define the customer experience they aim to deliver, understand their audience, and choose the right tools to support the journey.
• 🇺🇸 IBM has applied AI to improve US Veterans Affairs' services, processing millions of documents and significantly speeding up benefits delivery.
• 🚀 The innovation in generative AI is accelerating, and organizations must adopt these capabilities quickly to leverage the vast opportunities they offer.

Q & A

What is the main challenge faced by customer service today?

The main challenge in customer service today is staying ahead of customer expectations and ensuring that every interaction is not only positive but delightful, particularly as customers expect faster and more seamless service.

How can AI and automation improve customer service experiences?

AI and automation, including generative AI, can improve customer service by providing insights that help businesses create smoother self-service experiences, support human agents with relevant tools and information, and optimize operations such as real-time call transcription and email drafting.

What is the significance of self-service in modern customer service?

Self-service allows customers to resolve their own issues, which reduces demand on contact centers. With generative AI, self-service experiences are becoming more natural and conversational, resilient to variations, and quicker to set up by automating the creation of dialogue flows.

How does generative AI assist human customer service agents?

Generative AI assists agents by quickly retrieving information from knowledge bases, providing summarized insights, and helping draft email responses. This reduces customer wait times and improves agent productivity.

What role does AI play in contact center operations?

AI helps contact center operations by analyzing transcripts of customer interactions to identify common issues, streamline processes, and automatically generate summaries of conversations, thereby enhancing agent efficiency and consistency.

What impact can AI have on customer loyalty and lifetime value?

Good customer service supported by AI can turn one-time customers into long-term brand advocates. NPS promoters (loyal customers) are said to have 10 times the lifetime value of NPS detractors, and AI can help businesses deliver more consistently positive customer experiences.

What are some specific AI-powered tools mentioned that improve customer service?

Some AI-powered tools mentioned include virtual agents and chatbots for self-service, generative AI for knowledge retrieval, email drafting, and real-time call transcription, as well as AI insights to improve overall customer service operations.

How does generative AI help field service agents?

Generative AI helps field service agents by providing faster and more accurate solutions to troubleshoot problems in the field, which reduces the time it takes to resolve customer issues and enhances service quality.

What is a real-world example of AI and automation improving customer service?

A real-world example is IBM's work with the US Veterans Affairs, where AI and automation were used to expedite claims processing, including automating document intake and applying AI to medical records. This resulted in faster claim resolution and better service for veterans.

Why is generative AI considered critical for the future of customer service?

Generative AI is critical because it significantly enhances productivity and customer satisfaction. It can automate complex tasks, improve multi-channel coordination, and help resolve customer issues faster, allowing businesses to meet rising customer expectations in a cost-effective way.

Outlines

00:00

📞 Automated Customer Service Challenges

The opening sets the scene by acknowledging how modern customers dislike waiting for service. Manish Goyal, Senior Partner at IBM Consulting, introduces himself and highlights the importance of meeting customer expectations in today's competitive landscape. His team's goal is to help clients apply AI analytics and automation to improve decision-making, particularly in enhancing customer service experiences. AI tools can aid both customers in self-service and human agents to deliver better customer support. Businesses spend billions on customer service annually, and good service can turn customers into long-term brand advocates, while poor service can drive them to competitors.

05:02

🤖 Enhancing Customer Service with Generative AI

Manish explains how the contact center, the hub of customer service, has evolved with the use of tools like IVR, robotic process automation, and chatbots. However, these technologies often lack seamless integration, and customer expectations have become more demanding. With generative AI, the customer service experience can be enhanced significantly, with AI models performing tasks beyond traditional automation. These models are trained on massive datasets and can create and classify text with precision, revolutionizing self-service and human-agent-assisted experiences.

10:03

🧠 Generative AI in Self-Service

Manish discusses how generative AI can improve self-service experiences by making them more natural and conversational. Historically, creating chatbot or virtual agent dialogue flows was time-consuming, but AI now makes it easier and more resilient to customer variations. The tooling has also improved, allowing domain experts to describe processes in natural language, and the AI generates the necessary flows automatically. This technology leads to more seamless, conversational, and effective customer interactions.

👩‍💼 AI Augmented Human Agents

The second key area of improvement is AI's ability to augment human agents. Customer service agents spend a lot of time searching through knowledge bases for solutions. AI can dramatically speed up information retrieval, summarizing relevant data for agents to use in real-time. This reduces wait times for customers and allows agents to handle more queries efficiently. Field service agents can also benefit from AI support, solving problems faster and more accurately. AI can even help agents draft emails in response to customer queries, improving the quality and satisfaction of responses.

📊 Generative AI in Contact Center Operations

Generative AI can provide valuable insights into contact center operations by analyzing all customer interactions. This data can help identify issues like long call times or product-related complaints, allowing operations leaders to address root causes quickly. Additionally, AI helps reduce the time agents spend on after-call work by transcribing conversations and generating summaries, saving time and ensuring consistency in documentation. This increased efficiency leads to improved productivity and faster service for customers.

🏛 Transforming US Veterans Affairs with AI

Manish shares a heartwarming story about IBM's work with the US Veterans Affairs. AI and automation were applied to streamline and speed up the process of handling veterans' benefit claims, which used to take much longer. The system now processes millions of documents and claims automatically, reducing waiting times for veterans. AI helps claims adjudicators make decisions faster, ensuring that veterans receive the benefits they deserve more quickly.

🚀 The Future of AI in Customer Service

Manish concludes by discussing the rapid advancements in AI technology and its growing impact on customer service. He emphasizes that the faster businesses adopt generative AI, the sooner they can take advantage of the opportunities it presents. With AI evolving so quickly, enterprises need to focus on creating omnichannel experiences that anticipate and address customer needs proactively. AI is not just a tool for responding to problems but can help businesses offer seamless, delightful customer experiences across all channels.

Keywords

💡Customer Service

Customer service refers to the support offered to customers before, during, and after a purchase. In the video, it is emphasized as a key area where AI and automation can drive improvements. The speaker highlights how effective customer service can turn one-time buyers into loyal customers, and bad experiences can drive them away.

💡Generative AI

Generative AI refers to AI systems that can create new content, such as text or speech, based on vast amounts of data. In the context of the video, generative AI is presented as a technology that can revolutionize customer service by automating tasks, improving self-service options, and assisting human agents with complex queries, thus enhancing customer experiences.

💡Self-Service

Self-service in the video refers to the capability of customers to resolve their own issues without needing direct human assistance. It is often enabled through tools like virtual agents and chatbots. Generative AI is said to improve these self-service interactions by making them more conversational and adaptive to customer needs.

💡Contact Center

A contact center is a hub for managing customer communications, typically handling large volumes of inquiries across various channels like voice and digital. The video describes how contact centers have evolved with technologies such as IVR and robotic process automation, and how generative AI can further enhance their efficiency by assisting agents and improving call handling.

💡Agent Assist

Agent assist tools refer to AI-powered technologies that support customer service agents in retrieving information, drafting responses, or handling queries more efficiently. The video highlights how generative AI can provide real-time insights and help agents find answers faster, improving both agent productivity and customer satisfaction.

💡Omnichannel Experience

An omnichannel experience ensures that customers have a seamless interaction with a brand across different channels, such as phone, email, or social media. The video emphasizes the importance of AI in driving consistent customer experiences, allowing interactions that start in one channel to continue smoothly in another.

💡Robotic Process Automation (RPA)

Robotic Process Automation (RPA) refers to software that automates repetitive tasks, often used in customer service operations. In the video, RPA is mentioned as a way to make agents more productive by automating simple tasks like routing calls or retrieving basic information, freeing up human agents for more complex interactions.

💡Knowledge Bases

Knowledge bases are repositories of information that agents or customers can search to find solutions to common problems. The video explains that generative AI can help improve the speed and accuracy of retrieving relevant information from knowledge bases, reducing the time agents spend searching for answers during customer interactions.

💡After Call Work (ACW)

After call work (ACW) refers to the time agents spend documenting the details of a customer interaction after the call ends. The video explains how generative AI can automate the creation of these summaries by transcribing calls in real-time, allowing agents to spend less time on documentation and more time assisting customers.

💡NPS Promoter

An NPS Promoter is a customer who, based on the Net Promoter Score (NPS) system, is highly satisfied with a brand and likely to recommend it to others. The video highlights that effective customer service can turn customers into NPS promoters, which increases their lifetime value significantly for the brand.

Highlights

Generative AI can significantly improve customer service through self-service, augmenting human agents, and enhancing contact center operations.

Virtual agents and chatbots now deliver more natural and conversational experiences, resilient to variations and digressions.

Generative AI helps contact center agents by improving knowledge retrieval and summarizing information, reducing hold times and boosting productivity.

Field service agents can troubleshoot problems faster and more accurately with AI-based solutions.

AI-assisted email responses lead to higher customer satisfaction scores.

Generative AI enables analysis of every customer interaction, providing insights into call handling times and granular classification of complaints.

Real-time transcription and summarization reduce after-call work time, increasing agent availability and productivity.

Combining traditional AI with generative AI allows enterprises to deliver proactive outreach, avoiding problems and resolving them faster.

Successful omnichannel experiences are built with a five-step approach: defining the experience, understanding customers, determining service channels, selecting tools, and designing the journey end-to-end.

The U.S. Veterans Affairs has automated 100% of mail intake and processes 220,000 documents per week using AI and automation.

Sophisticated AI analysis of medical records helps claims adjudicators make faster decisions for U.S. veterans.

Since 2023, over 125,000 claims have been processed for U.S. veterans using AI, significantly reducing wait times.

The capabilities of AI have caught up with the hype, and the innovation rate is accelerating rapidly with frequent new developments.

Enterprises need to adopt generative AI quickly to take advantage of the vast opportunities it presents for customer service.

The question for businesses now is no longer 'Why use AI?' but 'When to implement AI?' to enhance customer service.

Transcripts

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Today's customer hates to wait for service.

When I call up a brand or a service provider,

I expect to get my questions answered quickly

and to my satisfaction.

In the world of customer service,

the challenge is to stay a step ahead

of customer expectations

and ensure that every brand interaction

is not just positive, but delightful for the customer.

My name is Manish Goyal

and I'm a Senior Partner in IBM Consulting,

leading our AI analytics business globally.

The goal of my team is to help our clients

apply AI analytics and automation to create insights

that drive better decisions.

And one of the areas I spend a lot of time in

is advising clients on how AI analytics can help deliver

delightful customer experiences.

This could be through self-service experiences

or through aiding human agents

with tools and insights so that they can deliver

a great experience to the customer.

Now, enterprises spend billions of dollars

on customer service every year.

Good customer service

can turn one-time clients into long-term brand champions.

And the lifetime value of an NPS promoter

can be 10 times more than an NPS detractor.

On the other hand, around 80% of consumers say

they would rather do business with a competitor

after more than one bad experience with a brand.

The contact center,

the hub of most customer service operations,

has come a long way in the past couple of decades.

Tools such as interactive voice response (IVR),

agent assist, robotic process automation and chat bots

have already made customer service agents more productive.

However, at most enterprises, the use of these technologies

is fragmented instead of seamless.

At the same time,

customer expectations continue to be more and more demanding

these days, especially coming out of the pandemic.

Customers expect seamless access and speedy resolution

to their queries across digital and voice channels.

This in turn puts pressure on businesses

to deliver a frictionless customer experience

at every stage, from discovery to purchase,

through service and retention.

Now with generative AI,

this experience can be taken to the next level.

Large language models have the power to significantly expand

what can be automated,

performing critical customer service tasks

that are far beyond the capacities of earlier technologies.

These models are trained on vast amounts of data

and can recognize, classify, and create

sophisticated text and speech with speed and precision.

To see how

generative AI can significantly improve customer service,

let's look at three key areas.

The first is self-service,

wherein you give the tools to customers to serve themselves.

Virtual agents or chatbots serve the purpose here.

And over the years, they have become very good

at being able to direct customers

down a predetermined journey.

To make these journeys,

you first analyze what people are asking about,

you understand their intent

and then handcraft the dialogue flows

to direct them down the right journeys.

In the past, creating these flows took time,

but now with generative AI,

you can deliver much richer self-service experiences.

These experiences are more natural and conversational.

They're more resilient to variations and digressions,

and the tooling to create these flows

is also now being augmented with generative AI,

so that the process area and domain area experts

can describe the journeys in natural language

and AI generates the necessary underlying flows.

The second area,

where generative AI can significantly improve

customer experience is by augmenting the human agent,

whether in the contact center or in the field.

A lot of time is spent by agents in the contact center

searching knowledge bases

to resolve the queries for customers.

Generative AI can dramatically improve the retrieval

of this information from the knowledge bases

and present it back to agents in a summarized way,

to help resolve the customer query quickly.

This cuts down the time that the customer is on hold,

improving their experience,

while also allowing the agent to handle more calls

during their shift.

Similarly, field service agents can be armed

with generative AI based solutions

that help them troubleshoot problems in the field

faster and more accurately.

Another good example

is helping agents draft email responses automatically,

based on the context or query,

allowing them to review and edit

before responding to the customer.

AI augmented emails

have shown to have a higher satisfaction score by customers.

And the third area is in contact center operations.

Let's say you have a call center of a thousand

or maybe ten thousand agents.

Gaining insights into what's happening

across all the conversations

taking place between agents and customers

was difficult or expensive before.

With generative AI, you can go through the transcripts

of every call made and continuously gather insights

on how and why agents are taking a long time

to handle certain types of calls,

or understanding granular classification of complaints

on products or services.

This insight that a application of generative AI provides

can allow your operations leaders to find the root cause

of a problem faster and resolve them,

if in the servicing function,

or alert the product or marketing teams,

if they need to take remedial action.

There's also a lot of time spent by agents,

after each call with a customer,

documenting a summary of their conversations

and actions taken.

During that after call work time,

they're unavailable to attend a new call.

Again, with generative AI,

you can transcribe in real time

the conversation that they're having

and generate a draft of the summary

that agents can then edit and feed back into the CRM system.

Not only does this drive consistency

in capturing details of each conversation with customers,

but it also saves time and drives productivity

for the agents.

Today, as the cost of building these solution comes down

with foundation models and the ROI becomes justifiable,

there's a renewed focus

on the ways generative AI can be used for customer service.

And it's not difficult to imagine why!

Customer service has always been complex.

Just think of the last time you called in

for customer service.

Chances are you wanted to address a problem you were facing

with a purchase or a product or a service you have;

and if you have a problem, you are generally unhappy,

which is why enabling service journeys that anticipate

and deliver delightful omnichannel experiences,

whether as a self-service function

or a human assisted one is critical.

Secondly, in some companies, there are a lot of employees

who can influence the experience customers have

with the brand or service.

If you can augment their skills

and drive productivity across this large population

who front your brand,

it'll be a huge win for your enterprise.

And given the capabilities of AI

and the rate at which it is evolving,

you can expect significant gains in productivity

with the right deployment.

And there's more!

As businesses focus on building omnichannel experiences

for their customers,

AI can power interactions or conversations

irrespective of the channel customers come in from.

Which means a customer request

that originated in one channel

can be completed in another channel, seamlessly.

Combining traditional AI with generative AI

enterprises can drive proactive outreach,

helping avoid problems or help resolve them faster.

If you were to look closely at how these companies achieve

high levels of coordination amongst their channels,

you will discover a five step approach driving the execution.

The first step, as you kick off, is to have a clear idea

of the experience you want to deliver.

Next is to understand your customers well.

What is the demographic, preferences, digital or voice?

And now that you know your audience, you need to determine

how you want to serve them,

decide what channels you want to direct them to.

And then, look at the best tools

that can support those channels.

So what is your platform?

Do you go with the cloud-based contact center solution?

Will it be on-premises for other reasons, or something else?

Once you have your tool chain sorted,

the final step is to design the journey end-to-end,

so it delivers on the service strategy,

the experience you had defined when you started.

My favorite story, and it really warms my heart

whenever I speak of this,

is the work that we have been doing

with the US Veterans Affairs since 2019.

Before we came in, it used to take a really long time

for veterans to get their benefits.

We applied analytics and automation

to help support faster claim creation

and response to veterans.

Just a few numbers: 3 million packets processed end-to-end,

and these packets have lots of documents in them.

280 different document types,

24 distinct mail type processes,

100% automation of all mail intake.

We are processing 220,000 documents per week.

We then took that process further,

by applying sophisticated AI to analyze the medical records.

We are now helping the claims adjudicator

make decisions faster,

so that the veterans who fought for our country

get the help they need without the long wait.

And since 2023, we have processed over 125,000 claims.

We have been expecting AI to make a big difference

for quite some time now.

And I think the capabilities

have finally caught up with the hype.

What's more, the innovation rate has also accelerated

dramatically, with a new announcement almost every week.

It is becoming increasingly evident now

that the faster you add the capabilities

of generative AI to your organization,

the sooner you can make use of the unlimited opportunities

it opens up.

Imagine the next time your customer runs into a problem

and needs help: You'll have all the capabilities

to turn a possibly adverse situation

into a positive experience, without making them wait,

and perhaps, even before they pick up the phone.

So the question for enterprises

looking to fold AI into their customer service

is no longer, "Why?" But "When?"