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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?"