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Building customer relationships with conversational AI



Building customer relationships with conversational AI

We’ve all been there. “Please listen to our entire menu as our options have changed. Say or press one for product information…” Sometimes, these automated customer service experiences are effective and efficient—other times, not so much.

Many organizations are already using chatbots and virtual assistants to help better serve their customers. These intelligent, automated self-service agents can handle frequently asked questions, provide relevant knowledge articles and resources to address customer inquiries, and help customers fill out forms and do other routine procedures. In the case of more complex inquiries, these automated self-service agents can triage those requests to a live human agent.

During times of uncertainty and emergency, customer service operations powered by artificial intelligence (AI) can be invaluable to businesses, helping customer service or human resources call centers keep up with spikes in demand and reduce customer wait times and frustration. According to recent estimates, Gartner predicts that by 2022, 70% of customer interactions will involve emerging technologies such as machine learning applications, chatbots, and mobile messaging. That’s an increase of 15% from 2018.

“In these types of conversational interactions, AI chatbots can extend the reach of an organization’s customer service and maintain a level of reciprocity with their customers,” says Greg Bennett, conversation design principal at Salesforce. “There’s also the opportunity for the business to express its brand, its voice, and its tone through words and language it uses to create a greater degree of intimacy.” Bennett is deeply involved in training AI systems that power conversational chatbots and ensuring they are inclusive and able to understand a broad range of dialects, accents, and other linguistic expressions. 

Not only is the use of AI automation becoming more widespread, it is also proving to be a significant business driver. Gartner anticipates that in 2021, AI augmentation will generate $2.6 trillion in business value. It could also save as many as 6.2 billion hours of labor.

Conversational intelligence defined

According to research conducted by management consultancy Korn Ferry, conversational intelligence is a collaborative effort. And that collaborative effort is reciprocity of two participants to communicate in ways that lead to a shared concept of reality. That closes the gap between the individual reality of the two speakers—and helps businesses help customers.

With that in mind, Salesforce and other companies have taken that concept one step further by looking for ways to combine conversational intelligence with technology. In fact, through these efforts, AI-powered conversational intelligence has vastly improved over time. This started with simple text recognition in which it’s fairly easy to achieve a significant degree of accuracy. But text recognition can be somewhat two-dimensional, which is why research has progressed to include automated speech recognition. Automated speech recognition systems must account for different languages, accents, and acoustic inflections, which is much more difficult and nuanced. As AI algorithms have become more sophisticated and have had the time and experience to incorporate more linguistic variations, AI technology has improved its ability to accurately understand the deeper subtleties of human conversational interactions.

“Conversational intelligence is the constellation of features and technologies that enable humans and machines to take turns exchanging language and work toward accomplishing a discursive goal,” says Bennett.

These AI systems focused on linguistics use a number of different technologies to understand written and spoken interactions with humans. Some of these include the following:

  • Automated speech recognition, which is used to understand spoken language for voice systems;
  • Natural language processing, which helps computers understand, interpret, and analyze spoken and written language; and
  • Natural language understanding, which makes it possible for AI to understand intent.

Going well beyond simple text recognition, natural language understanding is where AI is truly bringing its strengths to bear. By facilitating deeper, more nuanced conversation, it increases the efficacy of human-AI interactions. When an AI-powered customer service system is better equipped to recognize and discern natural language with fewer errors, it can guide a customer through an entire interaction without having to engage a human service agent. This frees up the agents to focus on more complex cases.

And using these capabilities in customer service environments can help companies not only expedite and improve interactions with their customers but also improve the overall customer relationship. “If we can have a machine that helps facilitate that type of interaction between a company and a customer, then it helps to further build a relationship with that customer in a way that a help article would not,” says Bennett.

And the more an AI system engages with humans, the more effective its algorithms become. By interacting with humans, an AI system can gather the data required to improve natural language understanding to better understand intent, helping to facilitate more nuanced human-computer conversations. Human interaction also helps these AI systems improve recognition and predictive capabilities to deliver more personalized content. By learning the many ways people behave and interact, the system’s response becomes more accurate.

AI algorithms absorb, process, and analyze the data sets fed into the system using their own specific equations. This processing is done in one of two basic modalities: supervised or unsupervised. In supervised improvement, data sets will have an assigned target value or category. In unsupervised improvement, the algorithm analyzes the dataset on its own with no guidance or restrictions.

As they receive and process more data, the algorithms evolve, adapt, and improve their analytical models. So the algorithms improve and refine themselves based on both the quality and quantity of data processed. “There are notions that AI can glean distinct intent, scope, and context by interacting with humans,” says Bennett. “These incremental improvements in predictive ability and depth of understanding increase the efficiency of customer engagement.”

Appreciating linguistic challenges

Although natural language processing has come a long way, automated speech recognition technology continues to face challenges in recognizing the full range of linguistic variations. “There are all these different English accents, all of them are robust and valid and should be celebrated,” says Bennett. Other linguistic variations that challenge AI include different slang or colloquial expressions to convey similar meanings and other paralinguistic features like tone, intonation, pacing, pausing, and pitch.

It is paramount to help AI manage the inherent levels of bias present in the system and expand to recognize the full range of linguistic variations. These incremental improvements in the predictive ability of AI algorithms help improve the customer experience by reducing the amount of back-and-forth exchanges and moments of frustration brought on by a lack of accurate recognition.      

But these efforts and advancements present certain ethical conundrums. Consider, for example, how minorities are represented in training datasets—or more accurately how they are not represented. Most widely used datasets exclude more diverse expressions of dialect and social identity. Ensuring a diverse representation on the teams developing AI technologies is a critical step toward developing and evolving AI algorithms to recognize a broader array of linguistic expressions.

Now that AI is capable of allowing for a greater degree of variation, it should be able to account for broader contextual relevance and be more inclusive. Although conversation and language are the conduit, it is incumbent on humans working with AI systems to continue to consider accessibility throughout dialects, accents, and other stylistic variations.

“Under-represented minorities have very little representation of their dialect and the expression of their social identity through language in these systems. It’s mostly because of their lack of representation among the teams creating the technology,” says Bennett. Ensuring that companies developing and deploying AI systems bring more diverse teams into the mix can help resolve that inherent bias.

AI systems have the capacity to allow for a greater degree of variation. When the systems can accurately interpret those variations and generate a contextually relevant response, AI will have evolved to a greater degree than ever before. “That’s really where I think the evolution [of the field] has taken us,” Bennett says.

Of course, that’s not to say there aren’t other ethical and practical concerns surrounding the expanded use of AI. Privacy concerns, responsibility, transparency, and accurately and appropriately delegating decision processes are all still relevant. And then there’s the ethical use of voice recordings. It’s a growing field in which significant parameters still need to be defined.

Forging a deeper human-AI connection

Addressing the full range of linguistic variations and including more diverse groups and historically under-represented minorities in the process is truly building the future of the human-AI connection. This will also lead to more widespread use cases for business. In fact, the biggest competitive differentiator in the future of conversational technology will be the ability to provide robust conversational understanding regardless of language, accent, slang, dialect, or other aspects of social identity.

Bennett recalls a lesson from a grad school professor: “She said, ‘Having a conversation is like climbing a tree that climbs back.’ And that really characterizes the trajectory of where conversational AI technologies must go in order to meet the human needs and standards of conversation as a behavioral practice.” Conversation is not a solo act. It’s a two-way street. True conversation is the act—some might even say the art—of taking turns engaging in speaking and listening, exchanging ideas, exchanging feelings, and exchanging information.

“In linguistics, the paralinguistic features of speech like inflection, intonation, pacing, pausing, and pitch provide the pragmatic layer of meaning to a conversation,” says Bennett. “Instead of focusing on how the users can help AI systems, we should be asking how we can scale the system to meet the users where they are. Given what we know about linguistics, I don’t believe you can force any sort of language change,” he says. “Conversational AI technology is set up in a way that could succeed if we took that approach at the pragmatic layer—the paralinguistic side of things.”

“The capacity to comprehend, fully understand, and scale to that level of linguistic diversity is where AI is heading,” says Bennett. “Startups in the conversational AI space are indexing on that as a differentiating factor. And when you think about it, if you include more diverse groups and historically under-represented minorities in the process, that actually expands your total addressable market.”

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.


The hunter-gatherer groups at the heart of a microbiome gold rush



The hunter-gatherer groups at the heart of a microbiome gold rush

The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

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The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan



The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

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Navigating a shifting customer-engagement landscape with generative AI



Navigating a shifting customer-engagement landscape with generative AI

A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

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