Conversational AI is estimated to grow into a $15.
7 billion market by 2024.
However, with this incredible growth comes challenges including the ability to effectively navigate a crowded vendor landscape.
Much of the confusion for companies looking to adopt a conversational AI solution stems from a misunderstanding of what technology is out there, and how it can be used to improve customer experience.
Presently, the market is flooded with rule-based chatbots.
These are the kind of interactive chat experiences that became popular on platforms like Facebook, but which are ultimately limited in their scope.
Rule-based chatbots offer no genuine artificial intelligence, instead relying on buttons to drive a conversation forward rather than natural-language technologies that more accurately reflect the way we, as humans, communicate.
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display(div-gpt-ad-1439400881943-0); }); Chatbots gained prominence in the market due to how simple they are to build, but while they can be helpful in use cases—such as booking a restaurant reservation or ordering a bouquet of flowers—they quickly break down as business needs begin to scale.
Indeed, Gartner predicts that this year, 40 percent of the chatbot projects that were started in 2018 will be abandoned.
Many of these abandoned projects are likely to center on rule-based chatbots, which didn’t deliver on the promise of conversational customer experience due to their inability to scale effectively.
Prior experience with an unsuccessful chatbot project has led many businesses to search for what comes next.
They see the potential in the technology, but also realize that genuine artificial intelligence is crucial to delivering a level of customer service that is not only on par with existing support channels, but to moving the needle far beyond them.
This is where conversational AI-powered virtual agents have begun to take over from the inefficient chatbots of the previous decade, standing up to current customer service requirements as we move into 2020 and beyond.
Enterprise-ready feature sets In the enterprise space, online customer interactions can range from simple informational queries to complex transactions that require multiple API calls to third-party and back-end systems.
Conversational AI is built from the ground up as an enterprise solution to effectively handle these extreme variations in customer intent at scale.
Some of the advanced enterprise functionality that sets conversational AI apart from rule-based chatbots includes superior language understanding via NLU and deep learning algorithms, as well as simultaneous multilingual support.
In addition, contextual-awareness to carry on a conversation even if it has veered off-track, is essential for success.
Advanced spelling correction and support for slang and dialects is crucial, as is the ability to set conversation goals and clearly track customer engagement.
Consequently, enterprise implementations rely on integration with backend systems and third-party platforms such as Genesys and Salesforce.
User authentication to allow transactions to be completed on behalf of customers, from a security and privacy perspective, is a key feature.
Strict data security and privacy features, including compliance with GDPR, are growing in importance as the regulatory landscape expands.
Conversational AI is also designed to help augment existing support staff and to not only assist customers.
For example, if a virtual agent is ‘stumped’ by a request that is outside of its designated scope, it is able to seamlessly handover that customer to a human chat operator within the same chat window.
A centralized hub of knowledge A dedicated set of features and functionality set conversational AI apart from simple rule-based chatbots in the enterprise.
However, a crucial piece of the puzzle in delivering the highest-level customer experience possible actually lies in the body of data that a vendor uses to build a virtual agent from the ground up.
Many of the larger vendors offering conversational AI solutions take a generalized approach to their knowledge corpus.
While this may sound appealing initially, tapping into the existing data of a tech giant’s customer base can prove to be too broad of an approach for a bank or insurance company.
A ‘jack of all trades, master of none’ strategy may work for projects with a smaller scope, but once customers begin asking deeper questions about products and services, it quickly becomes apparent that the ability to answer general trivia about Star Wars isn’t going to cut it.
Instead, the best conversational AI providers have developed a three-pronged approach to building up a holistic body of knowledge for each client.
This can be referred to as the three levels of conversational AI knowledge.
General knowledge (e.
g.
“hi”, “how are you?”, “thank you”, etc.
)Industry-specific knowledge (e.
g.
banking, telco, insurance, public sector, etc.
)Company knowledge (e.
g.
brand-specific products and services) The first two—industry-specific and general knowledge—are pre-built by subject matter experts and include intents numbering in the thousands, unlike many off-the-shelf solutions that claim to have only a few hundred, at most.
Many vendors may offer ‘templates’ for different vertical markets but, like a rented tuxedo, they are often ill-fitting and don’t hold up upon closer scrutiny.
To deliver a virtual agent that truly elevates customer experience, it is necessary to go beyond general-level industry intents and deliver a data set that is specifically designed with each market in mind including common terminology and use cases.
This solid foundation allows a company to kickstart its virtual agent project with confidence, and with all the intents it needs to succeed in a particular sector.
It also means that no superfluous or unnecessary data from other verticals clogs up the model.
Of course, every company also has its own unique nomenclature that is critically important for a virtual agent to accurately reflect.
Company-specific terms are not automatically covered in the vast knowledge corpora of the larger tech companies.
Anyone who has tried using Google Translate on domain-specific documents can attest to just how messy the results can be.
That means a company needs to spend additional time and resources adding this data itself, and in many cases it can’t be done without engaging highly-skilled—and costly—technical staff.
Much like a visit to the tailor to purchase a bespoke suit that fits you perfectly, the very best conversational AI providers work closely with their clients to build a unique model that includes company-specific terms and concepts and is tailored to their own products, services and brand culture.
Developing this top data layer means using conversational AI to parse through existing live chat logs, letting it identify new intents that can be used to enrich the model.
Once the virtual agent goes into production, conversational AI continues suggesting additional new intents based on its interactions with customers that are then approved and added by the human AI trainers whose job it is to continually maintain and improve the virtual agent.
Next-level understanding This approach is a big part of what differentiates conversational AI from typical plug-and-play chatbot solutions.
However, it’s not simply enough to have a data set designed explicitly for your company if the virtual agent isn’t able to accurately put it to good use.
Proprietary algorithms, like Automatic Semantic Understanding (ASU), can greatly enhance the language understanding capabilities of conversational AI.
ASU gives virtual agents a competitive edge in understanding the underlying intent of a customer’s query by finding complicated connections between sentences.
ASU also allows a virtual agent to understand the meaning of any user input—even sentences with multiple intents—and removes dead ends from conversations by drastically reducing the likelihood of false positives.
Rather than applying ASU across a knowledge corpus in its entirety, it is possible to apply it to each model independently.
This results in a smarter virtual agent that doesn’t miss out on the subtle nuances in an interaction.
Activating an algorithm, such as ASU, only once each client’s model has been sufficiently trained, ensures a consistent baseline of understanding that is bespoke to each virtual agent.
This means it can confidently tackle customer inquiries that are unique to each business with a level of accuracy that is otherwise unrivaled by any other solution on the market.
About the Author Henry Vaage Iversen is a Co-Founder and the Chief Commercial Officer of Boost.
ai, a Norwegian-based specialist in AI-powered customer interactions.
Inventor of the world’s most complete software for building, implementing and operating virtual agents powered by conversational AI technology.
Henry leads Boost.
ai’s global sales teams and has expanded the business from the Nordics to Europe and the US.
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