Self-service is on the rise. More and more customers are looking to solve their own issues without human intervention.
Enter a conversational AI solution for your contact center.
AI virtual agents can help resolve a lot of routine and repetitive interactions, but not all are created equal. Some customer service interactions require a human —but just because some calls must be transferred to a live agent doesn’t mean they can’t all begin with AI.
In a 6-minute customer service call, 75% of that time goes to live agents doing manual research. There’s authentication, information gathering, trying to route the call to the appropriate location, and so on. Only 25% of the call is valued customer interaction. How can you render this process more efficient and give more space to the valued interaction to shine?
By starting every conversation with AI.
In this webinar, you’ll learn:
The importance of an intelligent front door at the beginning of every interaction
What calls are best handled by a collaboration between AI and live agents
How information gathering and authentication in natural language through an AI agent can cut down on AHT and save money on operating costs
What the intelligent front door experience looks like in a live demonstrationSelf-service is on the rise. More and more customers are looking to solve their own issues without human intervention.
Enter a conversational AI solution for your contact center.
AI virtual agents can help resolve a lot of routine and repetitive interactions, but not all are created equal. Some customer service interactions require a human —but just because some calls must be transferred to a live agent doesn’t mean they can’t all begin with AI.
In a 6-minute customer service call, 75% of that time goes to live agents doing manual research. There’s authentication, information gathering, trying to route the call to the appropriate location, and so on. Only 25% of the call is valued customer interaction. How can you render this process more efficient and give more space to the valued interaction to shine?
By starting every conversation with AI.
In this webinar, you’ll learn:
• The importance of an intelligent front door at the beginning of every interaction
• What calls are best handled by a collaboration between AI and live agents
• How information gathering and authentication in natural language through an AI agent can cut down on AHT and save money on operating costs
• What the intelligent front door experience looks like in a live demonstration
Brian
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Abstract:
Self-service is on the rise. More and more customers are looking to solve their own issues without human intervention.
Enter a conversational AI solution for your contact center.
AI virtual agents can help resolve a lot of routine and repetitive interactions, but not all are created equal. Some customer service interactions require a human —but just because some calls must be transferred to a live agent doesn’t mean they can’t all begin with AI.
In a 6-minute customer service call, 75% of that time goes to live agents doing manual research. There’s information gathering, authentication, capturing the intent of the call, and then a live agent trying to solve the issue. The other 25% goes to customer interaction of value. How can you render this process more efficient and give more space to the valued interaction to shine?
By starting every conversation with AI.
In this webinar, you’ll learn:
The importance of an intelligent front door at the beginning of every interaction
What calls are best handled by a collaboration between AI and live agents
How information gathering and authentication in natural language through an AI agent can cut down on AHT and save money on operating costs
What the intelligent front door experience looks like in a live demonstration
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This is webinar #1 in a four part series:
Start every convo with AI
Why convo AI projects fail
How to build ROI for AI
Masterclass: Conversational AI for contact centers (or why convo AI projects fail - part II)
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Very excited for today’s webinar. We’re all familiar with voicebots and chatbots…and the concept of automating customer service with virtual agents – it’s nothing new.
But what is “new” are the advancements in technology…and the evolution of virtual agents that have brought us to this point – where it now makes sense to start every conversation with AI. So we’ll discuss what it means to do that, and how that will affect contact centers from a containment standpoint, a data mining standpoint, and perhaps most importantly – from a workforce capacity standpoint.
So with that, let’s go ahead and get started on why we are in need of AI now more than ever
Brian
We deliver AI-powered virtual agents as a service. That means we deliver the full conversational AI technology stack. It’s turnkey. It’s omnichannel. All of our clients use our voice self-service module. Most clients coming to us today rely on us for more than voice but their digital channels as well over chat and text. We use an open platform that incorporates best-of-breed AI and machine learning tools from both Google and Microsoft to augment our proprietary tools and stitching, so we really do believe we are delivering the best experience in the marketplace.
But what makes us a little different is that we’re not just trying to sell a software licenses or seats and throw them over the fence and wish you good luck on your journey. Conversations with machines are complex. It needs experts. So we bundle end-to-end CX services with our technology. And when I say end-to-end, that means everything – the design, the build, and even the ongoing operation after go-live because it requires care and feeding where a team needs to dedicated to training AI models, examining data and optimizing the experience. So at the end of the day, we’re really stepping in more as a partner instead of just a technology provider. That makes us responsible for delivering the CX that was promised and the ROI that was promised.
We’d like to think that approach is working for us. We operate the AI-powered CX for more than 100 brands
[only say this if not following with the Gartner Peer Insights slide]
currently the top-rated conversational solution on Gartner Peer Insights. So if you’re interested in what others have to say about us, starting with those reviews is a good place to start.
Helena
Helena
Let’s start with a little context as to how we got to where we are now – so this graph shows the labor participation rate in the US – and you can see from our record high back in January 2000 – where the participation rate was 67.3%, that even with the slight ups and downs, it’s been steadily declining since then.
And we’ve had two recessions that had a significant impact – you can see that marked by the vertical gray bars –
1) There was the early 2000 dot-com bubble burst, when an over-inflated Nasdaq lost more than 75% of its value and wiped out a generation of tech investors.
2) Then, we had the Great Recession, which ran from December 2007 to June 2009 – and that was the longest economic downturn since the Great Depression – so you can see that it severely affected the labor participation rate enough to drop it to 63% until it normalized (and I use that term loosely here) around 2013. 7 years of hovering around 63% came to an end when the pandemic began and brought it to the lowest point — 60.2%
This decline is not a phenomenon that’s particular to the US -- it’s been on the decline for decades on a global scale as well.
https://fred.stlouisfed.org/series/CIVPART (graph above)
https://www.census.gov/library/stories/2021/06/why-did-labor-force-participation-rate-decline-when-economy-was-good.html
https://www.investopedia.com/terms/p/participationrate.asp
https://www.bloomberg.com/news/features/2021-08-05/why-is-u-s-labor-force-shrinking-retirement-boom-opioid-crisis-child-care
Helena
So we have the shrinking workforce, which isn’t a new concept – but to further complicate matters, we also have the great resignation -- a term we’ve all heard ad naseum lately. The surge of quits is colliding our exacerbating labor shortage, and it’s creating a lot of pains for companies with hiring and retaining.
Quitting has been especially high in hospitality, healthcare and retail, as well as low-wage sectors in general, where workers have been taking advantage of strong demand to look for jobs with better pay or working conditions. No surprise that these are the top reasons for quitting:
Feeling burned out – people are feeling restless and unsupported. Elevated stress from the pandemic has affected everyone and its causing burnout, along with job-related stress, too.
Better opportunities – many feel that they’re no longer progressing. In a recent survey of 1,200 Americans who are planning to quit their jobs in the next 6 months, 78% have enrolled in an online training course or certificate program to learn new skills
It’s a strong candidate’s market – workers have more bargaining power to negotiate higher pay, more benefits, and greater flexibility
Lastly – the pandemic has brought this collective moment of reflection – where it has many of us questioning – does the work I do really matter? – is this what I want to do? Is this making me happy? So we’re seeing this shift in mindset – and many are taking the opportunity to quit --
millions of baby boomers retiring early, but also millions of "Gen Z" workers - people in their teens and early 20s. Many more women than men
https://www.cengagegroup.com/news/press-releases/2022/great-resigners-research-report/
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https://www.nytimes.com/2021/11/03/business/jobs-workers-economy.html
https://www.washingtonpost.com/business/2021/10/12/jolts-workers-quitting-august-pandemic/
https://www.washingtonpost.com/business/2021/11/12/job-quit-september-openings/
https://www.bls.gov/news.release/pdf/empsit.pdf
https://www.washingtonpost.com/business/2021/10/13/great-resignation-faq-quit-your-job/
Phill
…making the case for conversational AI. UP until very recently, the business case to invest in conversational AI was driven by cost. What’s the ROI? What’s the cost saving? And the selling point was that you’d be able to automate common, repetitive tasks in the contact center for a third of the cost of a live agent.
And while those things still hold true, the biggest driver now for conversational AI in the contact center business continuity. We’re seeing a reduction in agent supply. We’re hold times and AHT go up – and these things are happening in contact centers for all the reasons we just mentioned. There’s a real opportunity to leverage technology to meet service demand in a way that’s cost effective and scalable.
Brian
Segue slide to starting every conversation with AI. You can say things like “xxx” is the old, directed dialogue way.
Which brings us to the point – that every conversation should start with AI.
So up until recently, there was a clear delineation between what call types would go to a virtual agent and what would go to a live agent. Now, we’ve reached a tipping point where now all customer interactions can begin with AI...and it can then resolve the issue or pass the baton to a live agent will full context.
Phill
Let’s explain why that is – so the “old” way was directed dialogue where you were limited to a few prompts – for example, “You can say things like, '''What's my balance?, make a deposit, or transfer funds.’”
Now you can ask the open-ended question, “How can I help you today?” without adding any limitations of what you could ask. The AI is able to understand and process what it is that you’re trying to do. And this capability refers to natural language intent capture – or intent recognition.
So across different verticals and use cases,
And this is leaps and bounds beyond the traditional IVR experience that's very rigid and as to follow a structured path.
Helena
Let’s dig down a little deeper – and break down what’s happening on the backend, so AI isn’t this black box of mystery.
In this example, the customer is asking – Can I change my appointment to Friday at 4pm? That whole phrase that she spoke is called the utterance. And what AI does – or specifically, natural language understanding is to parse that utterance – in other words, it’s converting the sentence into a format that I can understand.
So in that sentence, it extracts the intent, or the goal that the customer is trying to achieve. [Another way to think of the intent is as “the intention” or what is the customer’s intention?]
In this utterance, we also have an entity, or in this case, we have two entities – Friday (the day) and 4pm (the time). And an entity acts as the modifier to the intent. It’s essential capture both the intent and the entities correctly in order to deliver what the customer wants. It’s not enough to know that the customer wants to change their appointment – we also need to know what day and time to change it to.
Which brings us to the question -- what happens when the virtual agent isn’t able to understand the customer?
Brian
(938)-200-0023
We do a lot of scheduling for different industries. We schedule everything from home repair, house tours for real estate, service appointments for auto dealerships, and public transit rides. But the biggest is in healthcare. So I’d like to showcase an example that we do for doctors and dentists – Brian will
STEPS TO FOLLOW
1. “Hi Helena, are you calling to schedule a new appointment, change an existing appointment, or cancel?” Umm yeah, I'd like to schedule a new appointment.
2. IVA pre-emptively offers the provider from last visit. Yes.
3. IVA authenticates caller (name, birthday, and zip code).
4. “Is this a new exam, follow-up, or something else?” It’s going to be a new exam.
5. In a few words, tell us why you are coming in. Yeah, so I have this persistent cough that won’t go away. I’ve had it for 3 weeks now and nothing I've been taking for it has been helping.
6. “The next appointment I have available is {xxx}. Does that work for you?” No.
7. “What day would you like to come in for your appointment?” Do you have something available next Tuesday in the early afternoon?
8. I have an appointment available on {xxx}. Yes, that’ll work!
Point out that when I said "no" to the first time it offered, it asked for which day I want to come in. Instead of giving a day, I said the day AND time frame (afternoon) when I wanted to come in and it handled no problem. When I didn't like the time, it asked what day I'd like to come in. Instead of saying the day, I asked for the times it had on Wed and gave me the first available time. As you can see, the flows aren't strict and are able to react in natural language in much the same way a human conversation might go.
364-202-3762
Phill
When you adopt a strategy of open-ended intent capture for your front door that allows your customers to say anything, that builds up a gold mine of data that you will use to optimize your bot. This will allow you to mine that data to find out why your customers are calling. A lot of contact centers don’t have a good understanding of why their customers are calling and exactly what they are calling about
On Day 1, your bot will not be trained to account for all the things your customers are asking for and that’s fine – just transfer to a live agent for anything you don’t understand – but this will present a data hierarchy on all the things your bot has not been trained to handle and allow you to prioritize the roadmap of optimizations you need to make.
And those optimizations will fall into 2 different buckets. One bucket is confusion transfers. Confusion transfers mean the AI did not understand what the caller was asking for so the bot transfers the call to a human. A business rule transfer means the AI understood what you were asking for but hasn’t been trained to handle that, so transferring you to a human. It’s a purposeful transfer.
You have to encode these insights into your bot or otherwise you’re going to get a flood of data and have no idea on where you need to focus your training and optimization efforts (a topic for another day)
Phill
Here’s what makes your confusion transfer bucket such a gold mine - because it tells you all the reasons why customers are calling that either the AI model has missed or the bot has not been trained to handle
The first two on the left are straight forward and not too hard to deal with, it’s just time consuming. These are instances where someone said a word or phrase related to an intent the bot is supposed to handle, but the AI model was a little immature -- wasn’t trained to handle that particular word or phrase, thus a confusion transfer that shouldn’t have been a confusion transfer. This is merely a case of training the AI on what it should do the next time it sees a similar phrase or identical word. This means you can’t have just anyone training your AI model. They have to have a full grasp of what the bot is capable of handling to know which prompt the AI needs to trigger the next time it hears the same thing.
A big misnomer is that AI learns on its own and gets better without any human intervention. (don’t we wish that was true?!) But while it doesn’t learn on its own, AI does a good job of pointing humans to where decisions need to be made on how it should be trained.
(26:29)
Misspelled words from your speech-to-text engine are little tougher. In fact this is one of the biggest reasons for failed voice experiences. Some technologies only allow for machine learned based NLU and don’t have algorithmic rules based approaches to deal with the words that machine learning doesn’t capture. There is no easy button on this. So first of all, you have to be equipped with a technology tool set that allows you to use both approaches but then you also need the NLU expertise on how to best use those tools in concert with each other. (example)
Last on the far right, this is a big one on Day 1. In fact, this is the most important one to deal with on Day 1 and the reason for that is because it really isn’t a confusion transfer in the sense that the bot didn’t make a mistake, rather, the caller asked for something the bot hasn’t been built to handle. That’s not a confusion transfer. That’s a business rule transfer. You just didn’t anticipate callers would be asking for that. So on Day 1, the biggest thing you’re doing is identifying all the things your callers are asking for that the bot wasn’t built to handle, and label them as a business rule so you can make smart decisions on how to optimize your bot.
Brian
On day I, 1n the area of reservations – somebody is going to. Bot wasn’t trained to handle it. You’re labeling it as a ‘business rule’ transfner.
Why is that important? Once you fill enough in that business transfer bucket – all the reasons why users are calling you that your bot isn’t equipped to handle.
find out how much volume you’re getting on a monthly basis. What kind of
Brian
Stack ranking why all the transfers are happening in the first place.
Business Rule Transfer is a known intent the bot has not been trained to handle.
What should we do to rain or make changes to the bot? Uou need a conversation designer. Hand if off to the NLU expert involve phrases.
Helena
(setup for why the AI fails. What happens if somebody goes off the rails?)
?…and it really is a matter of when, not if.
So when you launch your conversational AI solution, it’s a given that your virtual agent isn’t going to understand everything. And that’s because the training it’s received so far has been mostly from the QA team. It hasn’t had a chance to interact with your customers, so it’s not able to anticipate all the possible intents and entities.
And so in this example, “my mother in law is flying in from out of town on Tuesday, so I won’t be able to make it to my appointment…” this isn’t something we’ve trained the virtual agent to handle [even if you are using prebuilt models to speed up intent prediction and extract the context within the utterance].
So the key point here, is that it’s only after deployment that critical data starts pouring in – and you can see how your customers are engaging with the virtual agent. Are they dropping off at certain points in the conversation flow? Do we see any patterns where the virtual agent is getting stumped?
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(if the IVA doesn’t get it the first time, directed dialog)
Brian
A successful AI or machine learning initiative requires experience in people, process, and technology, and good supporting infrastructure. Gaining that experience does not happen quickly.
Many AI projects fail because they are simply beyond the capabilities of the company. This is especially true when attempting to launch a new product or business line based on AI. There are simply too many moving parts involved in building something from scratch for there to be much chance of success.
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Why AI Fails to Deliver: https://www.infoworld.com/article/3639028/why-ai-investments-fail-to-deliver.html#:~:text=Many%20AI%20projects%20fail%20because,be%20much%20chance%20of%20success.
Brian
We deliver AI-powered virtual agents as a service. That means we deliver the full conversational AI technology stack. It’s turnkey. It’s omnichannel. All of our clients use our voice self-service module. Most clients coming to us today rely on us for more than voice but their digital channels as well over chat and text. We use an open platform that incorporates best-of-breed AI and machine learning tools from both Google and Microsoft to augment our proprietary tools and stitching, so we really do believe we are delivering the best experience in the marketplace.
But what makes us a little different is that we’re not just trying to sell a software licenses or seats and throw them over the fence and wish you good luck on your journey. Conversations with machines are complex. It needs experts. So we bundle end-to-end CX services with our technology. And when I say end-to-end, that means everything – the design, the build, and even the ongoing operation after go-live because it requires care and feeding where a team needs to dedicated to training AI models, examining data and optimizing the experience. So at the end of the day, we’re really stepping in more as a partner instead of just a technology provider. That makes us responsible for delivering the CX that was promised and the ROI that was promised.
We’d like to think that approach is working for us. We operate the AI-powered CX for more than 100 brands
[only say this if not following with the Gartner Peer Insights slide]
currently the top-rated conversational solution on Gartner Peer Insights. So if you’re interested in what others have to say about us, starting with those reviews is a good place to start.
Helena
So as Brian was just mentioning, the old way was directed dialogue where you were limited to a few prompts – for example, “You can say things like, '''What's my balance?, make a deposit, or transfer funds.’”
Now you can ask the open-ended question, “How can I help you today?” without any caveat – and the AI is able to understand and process what it is that you’re trying to do. And this capability refers to natural language intent capture – or intent recognition.
So across different verticals, use cases, and customer interactions, there’s an opportunity to leverage AI in a whole new way.
And this is leaps and bounds beyond the traditional IVR experience that's very rigid and has to follow a structured path.
Helena
And just to showcase a quick example – insurance is a big vertical for us -- we handle more than 20 different use cases – which means, more than 20 different intents. And keep in mind that even with just one intent, there are so many different ways that you can ask for the same thing – and so we have to account for all of those variations.
And so the process is that when a customer calls in, we’re utilizing open-ended intent capture to ask ‘how can I help you?’ and the caller can reply to the AI in their own words and essentially say anything. What we’re listening for is certain hot words or phrases to identify the intent they are calling about, so we can put them in the right flow.
So if the customer is asking about proof of insurance, we can take them to that part of the flow to help them get a copy of their insurance. If the virtual agent hears “claim” but isn’t quite sure what the customer wants to do, then we can put them at the top of the claims-intent flow and ask them to describe what they’d like to do and then lead them down the appropriate path from there.
What we’re doing is delivering a natural language experience from beginning to end.
If they call in about points and saying something related to banking or borrowing points, we’ll take them straight to that part of the flow to bank or borrow. If we heard points but we’re not entirely sure yet what they want to do with their points, we’ll put them at the top of the points intent flow and ask them to describe in a few words what they would like to do and hand-hold them from there. The same applies for reservations and member services. If they tell us they want to book a new room AND give us the destination, we can skip asking about reservation type or destination and go straight to check-in and check-out dates to confirm availability.
Helena
Let’s start with a little bit of context – where are we now and where were we
A record-setting spike in coronavirus cases kept millions of workers at home in January and disrupted businesses from coast to coast. But it couldn’t knock the U.S. job-market recovery off course.
Employers added 467,000 jobs in January, seasonally adjusted, the Labor Department said on Friday.
While Omicron appears to have done less damage to the overall economy than many people feared, it has been painful for many individual families and businesses. Six million people reported in mid-January that they had worked fewer hours — or not at all — at some point in the previous four weeks because their employer closed or lost business as a result of the pandemic, the Labor Department said. That was about twice the number who reported such a disruption a month earlier.
To understand the job market as it exists, let’s look at a few key figures from October 2021.
531k jobs were added while the unemployment rate dropped by .2% from Sept to Oct to 4.6% and right now, 7.4M people are unemployed
How that compares to February of 2020, right before the pandemic kicked into gear in the US, we can see the unemployment rate was 3.5% and over 5 million people were unemployed.
To top it all off, we have a macro trend that certainly doesn’t help the job market and that’s the shrinking workforce.
https://www.washingtonpost.com/business/2021/10/12/jolts-workers-quitting-august-pandemic/
https://fred.stlouisfed.org/series/JTSQUR#0
Helena
So what’s interesting is that we’ve come to a convergence of two macro trends – on the left, you have one trend that’s really working against you. There are fewer workers participating in the labor market…but at the same time, companies are offering higher wages to attract and hire talent.
On the right, you have another macro trend – and this one is working in your favor. So it’s very similar to Moore’s law – in that we’re seeing greater technological capabilities and applications for conversational AI…while the price to invest and utilize this technology, is dropping.
And so with this convergence, it all boils down to….
Sofia
So the shrinking workforce isn’t a new concept. It’s been theorized for years prior to the pandemic. And to further complicate matters, we have the great resignation. The surge of quits is colliding with an existing labor shortage, and it’s creating a lot of pains for companies with hiring and retaining.
Today, job seekers find nearly 50% more job openings than they had pre-COVID. That’s the 10.4M jobs available compared to the 6.9M available pre-pandemic. And thanks to the adoption of certain technologies that make remote work possible, job seekers can also expand their search beyond their hometowns.
Front-line and low wage workers typically see high rates of turnover even without a pandemic. but employees in those roles are especially likely to leave now for a mirad of reasons.
Better opportunities with more flexibility like the ability to work remotely and eliminate commute are more available than ever.
Employees that are fed up with stressful work environments and lack of fulfillment are at their wits end. The workers economy gives them opportunity to find better jobs.
Elevated stress from the pandemic has affected everyone and its causing burnout, along with job-related stress, too.
People are quitting because of poor compensation. Companies are in more competition than ever against one another, so workers are able to get better paying jobs elsewhere and that’s raising wages.
https://www.nytimes.com/2021/11/03/business/jobs-workers-economy.html
https://www.washingtonpost.com/business/2021/10/12/jolts-workers-quitting-august-pandemic/
https://www.washingtonpost.com/business/2021/11/12/job-quit-september-openings/
https://www.bls.gov/news.release/pdf/empsit.pdf
https://www.washingtonpost.com/business/2021/10/13/great-resignation-faq-quit-your-job/
And so if you’re aiming to deliver a CX that’s as good as a human – you need an NLU engine that is customized to listen for the intents your business anticipates it will hear.
And in customer service, there’s a limited range of questions and answers, so you’re not trying to boil the ocean. As long as you know what those grammars are, you can narrow the aperture of what you’re listening for and tune for only those grammars, or anything that sounds even remotely similar to one of those grammars.
Onscreen is a truncated conversation flow for one of utility customers. When one of their customers call in, and says things like:
I need to make a payment arrangement
Or, can I get an extension on my bill?
Our system has that domain training in customer service to correctly interpret the customer’s intent and take the right actions. As I mentioned, this is just a little piece of the conversation. The customer actually has the power to negotiate the amount if they’d prefer to pay in installments…so this interaction is much more complex than it would be if you were interacting with Alexa. And this is a conversation that is perfect for automation – when customers are calling to negotiate payment arrangements with their utility company, it’s not a conversation they’d rather have with a human. And so in this instance – it's a win-win. We're delivering a customer experience that's on par or even better than a human agent.
https://medium.com/snips-ai/deep-dive-into-snips-spoken-language-understanding-embedded-system-8090914e260f
Brian
Industry-Specific Language
For the application to be meaningful, you must train it on many intents. Each requires capturing different ways someone can ask a question, which involves a lot of training and tuning. It’s a much larger effort than most people think, and often an unpleasant surprise for practitioners. In many industries, it entails teaching the application a specific terminology. Think of mortgage or healthcare. Thankfully, many vendors have been packaging generic “intents.” I recommend looking for providers that cover your application domains. You must assess how industry-specific your use cases are and orient your research accordingly.
Business rules; guard rails (handling rules – the AI will handle calls differently than a human would)
Knowing your grammars. What are the scope of responses that you are going to get from your customers.
Option to self-serve or wait for human agent
Instead of asking, “Is there anything else I can help you with?” consider suggesting two or three potential tasks that are related to what the user just finished.
Sometimes next steps might follow naturally from the business use case: for example, for our customer Tricon, if a potential buyer asks for information on a house, the virtual agent knows to follow up that inquiry with “Would you like to schedule a tour?”
Setting customer expectations. Your hold time is 10-min. Let’s collect some information to get you to the right representative. I just have four questions.
Alphanumeric capture (funnelling down the aperture that you’re listening for) ie vehicle year, then make, then model
Purpose built for the contact center
And so if the goal is to deliver a CX as good as a human would – you need a solution that is purpose built for customer service.
if you know what you’re listening for, you can have developers tune your NLU engine to only listen for vehicle names – and that means pattern matching the acoustics of what we heard and against the acoustics we were expecting to hear so we can run hypotheses on how they correlate even if the speech rec got it wrong. In fact, anytime speech rec delivers something that doesn’t match an expected output, this NLU engine kicks in as a fail safe on accuracy to see if it can find anything that sounds even remotely similar to a vehicle name. That’s the secret sauce to really good speech technology because speech to text is never 100% right.
So there are many conversational AI solutions that solely use an ASR to transcribe speech to text. And there are other general-purpose conversational AI platforms that also have contextual NLU
But in the arena of customer service where you can actually predict how a caller might respond to a question, you can go many leaps beyond contextual NLU and do something highly tailored that really drives up accuracy far beyond anything the best ASR can give you
