Putting AI to work for Customer Service
Summary
TLDRIn 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
📞 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.
🤖 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.
🧠 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.
Mindmap
Keywords
💡Customer Service
💡Generative AI
💡Self-Service
💡Contact Center
💡Agent Assist
💡Omnichannel Experience
💡Robotic Process Automation (RPA)
💡Knowledge Bases
💡After Call Work (ACW)
💡NPS Promoter
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?"
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