Why artificial intelligence is crucial for sales to bridge the customer chasm

Today’s customers have more choice and buying power than ever before, and they expect unprecedented levels of contextualization and personalization.

The latest wave of technology, led by Artificial Intelligence (AI), may be the first time the sales team has an opportunity to harness a new technology before consumers do, to turn the tables and find new ways to meet and exceed customer expectations.

A recent report from the 2018 State of Sales report by Salesforce, which surveyed sales professionals from around the world, found that high-performing sales teams across the Asia Pacific region will increase their adoption of AI by an average of 271% by 2020.

The industry views AI as the secret sauce they’ve been looking for to reduce the time spent on admin and unearths the next best steps and insight needed to deliver better customer experiences and more sales.

Customer expectations driving sales team change

Customers have never asked more of the sales team. According to the report, globally, more than three quarters of business buyers said they want to work with a partner as a trusted advisor — not just as a sales rep — who can add value to their business.

It is difficult to deliver that value if you’re spending time on the work that isn’t delivering for customers. Sales agents in the region said they spent approximately 62% to 69% of their time in non-sales activities in the last year, according to our report.

No stranger to the increased expectations of sales teams by customers is AirAsia, a leading low-cost airline headquartered in Malaysia. As it revamps its customer care for the digital era, AirAsia has deployed cloud-based solutions as part of its strategy to create faster and more personalized service for its customers.

As a result, the airline’s service agents across eight countries have a single view of all cases from all support channels — web, phone, email, live chat, airport communications — and each guest’s complete history with the airline, allowing them to achieve higher levels of personalized service.

AI is marching toward a tipping point for sales

AI is the next evolution for sales teams using a real-time, single source of truth. AirAsia is leading in this aspect, with the use of AI to pinpoint which areas of customer contact will require more resource allocation and which channels may be phased out over time.

It is critical for local businesses to shift their focus to providing the right data and intelligence to their sales staff, so that they can get closer to the customer and be more efficient in delivering sales outcomes.

Why sales should embrace AI today

It is hard to escape the constant noise around AI and whether we should fear it or embrace it. However, it is clear high-performing sales teams are the ones who use the technology to contact more customers and close more deals.

AI is allowing salespeople to sell smarter in new ways, and delivering more customers to the teams that have already made the switch. According to the report, globally, the majority of sales teams using AI have increased their number of sales representatives since 2015.

New technology will continue to disrupt the ‘way it has always been done,’ and the increasing role of AI in the sales process is no different. It is time for sales teams to embrace AI to stay ahead of the curve to exceed customer expectations.

 


This article was originally posted on enterpriseinnovation.net


IBM Showcases Artificial Intelligence Superiority with Project Debater

The IBM algorithm Deep Blue beat chess champion Garry Kasparov in 1997. It was 2011 when IBM’s Watson won the game show Jeopardy. Shortly after, the IBM Research team was ready to go beyond game playing and began to brainstorm the next feat to challenge an artificial intelligence algorithm. They decided to create an AI algorithm that would be trained on the art of debate.

Adobe StockADOBE STOCK

This past June, a small group of viewers got to see the IBM Project Debater’s public debut and its first two debates, when it went head-to-head with Israeli debaters Dan Zafrir and Noa Ovadia on increased investment in telemedicine and government subsidies for space exploration respectively. From all accounts, IBM Project Debater was a formidable opponent and surprised many with its ability to make human-like arguments. It even swayed more audience members to its position on telemedicine that Zafrir did.

This project was the latest in IBM Research’s goal to build a system “that helps people make evidence-based decisions when answers aren’t black-and-white.”  Debate not only helps us convince others of our opinion, but it can help us understand and learn from other’s views. By training machines in this way, it is hoped that in the future, AI algorithms will be able to help humans make important decisions regularly. IBM Project Debater doesn’t just search its database of millions of articles from well-known newspapers and magazines—its corpus—but it has AI technology that can “work with humans to discover, reason and present new points of view.”

The IBM Research team was able to create an algorithm with the ability to:

  • Generate an opinion driven by data
  • Listen and understand an opponent, parsing out the critical bits of data from flowing narrative
  • Express the situation and arguments with concise language and complete human-like sentences

Even though there were visible stumbling blocks in IBM Project Debater’s debating skills, for the most part, its debut was a resounding success. Since it’s gone from theory to actual implementation, albeit with some tweaks still necessary, it makes you wonder what’s next.

MORE FROM FORBES

Avoid blind trust by implementing checks and balances

It might be easy for many people to put too much faith in a machine. Although a machine can cull through the data at a rate and depth impossible for humans in a similar timeframe, it’s not immune to bias in its findings. The machine is only as good as the information it was fed. If some of the resources it used to develop its argument contained false logic, the algorithm was influenced by that logic in its debate. Being able to search and summarize millions of human-generated articles is no small feat, but Project Debater’s prowess isn’t representative—yet—of some superintelligence capable of reasoning in a self-generated manner (although that’s likely on the horizon).

To avoid machines just echoing back erroneous human opinions—or being manipulated by a government or corporation for its own purposes—there needs to be a system of checks and balances to ensure the program’s credibility.

IBM’s Project Debater work critical to natural language processing advances

Natural language processing is progressing on many fronts; however, what Project Debater exhibited was progress in loosely structured language in the form of conversations and articles. An algorithm’s ability to put together an argument based on small pieces of text supported by facts while understanding all the facets of an argument (logical, emotional) is a higher level function.  Project Debater can analyze its opponent’s argument and determine the appropriate response supported by facts. This represents a massive leap from “present information” to “make an argument.”

Practical applications of this technology

One of the impressive abilities IBM Project Debater exhibited was the combination of AI techniques it relied upon to solve many problems and join them together in a solution. Now that IBM Research succeeded in this first debate, the team needs to determine practical applications of this technology that they can sell. That’s precisely what Arvind Krishna, Director of IBM Research said he plans to do: “Project Debater’s underlying technologies will also be commercialized in IBM Cloud and IBM Watson in the future.”

Now that AI has gone beyond playing games to learning the art of persuasion and debate; it has proven that it can handle the “gray area” and nuances of human interaction and not just follow clear-cut rules.

“From our perspective, the debate format is the means and not the end. It’s a way to push the technology forward and part of our bigger strategy of mastering language,” said Aya Soffer, who runs IBM Research’s global AI team.

It was an impressive debut, and it will be intriguing to see what’s up next.

 


This article was originally posted on Forbes


How to Use AI Throughout The Insurance Value Chain, Starting with Sales & Distribution

How can insurers meet increased customer expectations at a lower cost? AI-powered care delivers on a future vision of customer service with an opportunity for savings of 30 percent by, for example, driving customers to digital experiences. In this post, I will explore how to apply AI using an intelligent customer engagement (ICE) framework.

How can your insurance company increase its artificial intelligence quotient (AIQ) with a balanced innovation strategy? In this blog series, I’m exploring the myriad ways in which AI adds value to financial services in general and the insurance value chain in particular. In my previous post, I defined the term AIQ and I revealed and discussed three key ingredients to building a strong AIQ: technology, data and people.

In this post, I’ll take a close look at one of the key areas in the insurance value chain—sales and distribution—and explain how AI-related technologies can add value to this function. But first, I want to reiterate the value of AI and why it’s important to transform your business into an AI business.

Why a strong AIQ is vital for your business—and why you need a strategy first.

Most of what’s written about AI relates to cost-cutting and job losses, but as we saw with the example given in regards to the health industry in the previous post, AI is a much more optimistic story. Its greatest benefits are not only efficiency and productivity, but innovation, improved customer and employee experiences, and the development of new sources of value and growth, especially when it is used to augment human capabilities.

However, to gain these benefits and to identify relevant use cases, it is necessary to develop a cross-enterprise AI strategy that clarifies the strategic goals: the whys, the hows and the whats of the business model leveraging our “AI strategic approach”, as outlined below.

Once the strategic goals have been clarified, the potential use cases for AI can be identified and prioritized according to the impact and estimated implementation effort they have on supporting the achievement of these goals (such as enhanced operational efficiency or improved customer experience) along the Insurance Value Chain:

How can insurers use AI in sales and distribution?

As mentioned in my previous post, there are numerous use cases of AI that can be applied along the insurance value chain. In this post we focus on AI in marketing, sales and distribution, including:

  • Enabling intelligent customer engagement
  • Workload balancing / lead allocation for agents
  • Machine learning insights to support customer segmentation
  • Automated data extraction from PDF reports and comparison against various policy combinations
  • Automated demand analysis and generation of new product offerings
  • Intelligent reporting and visualization
  • Customer personality and tone analysis
  • Automated creation of targeted marketing materials and promotions
  • Enablement of intelligent self-service product research for customers
  • Automated product recommendations and natural language question answering

When it comes to deciding which AI to employ, insurers need to focus on the things that AI and humans do best together. When AI is combined with human ingenuity across the enterprise, it can help solve complex challenges, develop new products and break into and create new markets.

Data analytics for better customer engagement

In sales and distribution, insurers can use data analytics to improve customer engagement. I will discuss intelligent customer engagement further in the use case below.

Virtual assistance (VA)

Accenture’s virtual advisor Cathy (Cognitive Agent to Help You) is a self-learning virtual agent that responds to customer queries by extracting information from a back-end database. Cathy is always learning more as it consumes human-agent interactions and stores knowledge on its database, enabling it to make automated product recommendations based on customer profiles. If a more complex customer request arises, Cathy seamlessly transfers the request to a human agent.

Machine learning

Insurers can boost their sales and distribution by using machine learning to analyze customer personality and tone. Machine learning makes selling (and buying) insurance easier than ever—virtual agent Amelia, for example, can give customers a motor insurance quote immediately, without the need to speak to a human being.

What are the benefits of AI for sales and distribution in insurance?

When humans and machines work together, they create the opportunity for growth and innovation. Within insurance  sales and distribution, AI-related technologies can help to enable:

  • Increased lead generation—data analytics helps insurers identify and reach potential customers. The insight derived from data analytics can drive and constantly improve the sales team’s effectiveness at generating leads.
  • Efficient leverage for cross- and up-selling effectiveness—AI such as data analytics and VA gives insurers invaluable knowledge about their customers, making it easier to convince them to buy a comparable higher-end product (up-selling) or a product that is related to the ones they already have (cross-selling).
  • Increased service quality—self-learning virtual advisors like Cathy interact with customers and absorb information about their needs. This valuable feedback drives personalization of products and improves the quality of services.

Use case: intelligent customer engagement (ICE)

With intelligent customer engagement, insurers can strategically deflect issues that need to be addressed from human agents to machine chatbots. They can predict why customers are calling and approach them proactively. Humans now take on the new role of knowledge engineer: they take over where the AI ends and curate the knowledge corpus over time.

In our future vision for insurance, AI-powered care gives insurers the opportunity to save 30 percent of their customer service costs by:

  • Driving customers to digital experiences;
  • Providing conversational interactions that increase digital adoption and containment;
  • Leveraging AI to automate and deliver consistency across channels.

Insurers are looking to better connect with their customers, and to strengthen relationships with experiences that delight them—while reducing the cost to serve. Technology enables them to do this by, among others, shifting the mix of customer contacts:

It’s time to put your AIQ to work

When you combine human ingenuity with AI—such as data analytics, virtual assistance and machine learning—to improve the sales and distribution function, you will see results improving.

AI presents the opportunity for business transformation by enabling intelligent processes in the value chain and intelligent products and services in the market. Success will depend on how well your organization can harness the combined power of technology, data and people.

In my next post, I’ll look at how you can use AI to augment underwriting and service management. Get in touch to find out how you can boost your company’s sales and distribution function, as well as others within the insurance value chain, or download our report on How to boost your AIQ.

 


This article was originally posted on accenture.com


From automation to opacity: Overcoming marketers’ AI anxieties

Artificial intelligence is revolutionizing businesses across industries. More than half of the executives surveyed in a 2017 PwC report said that AI solutions were already increasing their companies’ productivity. As usual, marketers are at the forefront, embracing AI at a particularly rapid pace.

But while any new resource can create excitement in some, it can make others feel uncertain—sometimes even worried about their futures. Many marketers fear that onboarding AI will fundamentally change the way they do business, and not completely for the better.

Below, we’ll take a cool-headed, logical look at how an AI-powered industry is an opportunity for all. We’ll dive into each area by exploring the misperceptions at the heart of these anxieties through the anonymous confessions of several marketers.


Marketers’ jobs have become more and more unwieldy. Their customers are spread across a growing array of devices and channels. It can be tough—and monumentally time-consuming—to try and make sense of all that data. AI tools are designed to cut through the muck, swiftly organizing information and surfacing insights into customer behavior.


The real problem is that so many humans are expected to function like computers. Today’s marketers are asked to comb through oceans of data, assembling it into something structured and coherent.

AI’s job is to take on that extra workload and free up marketers’ time to do higher-level thinking—what they do best. AI isn’t going to make marketers’ jobs obsolete; it will make certain aspects of their jobs manageable for the first time.


It’s true that AI can eliminate mountains of rote, mindless tasks. For example, the Associated Press used AI automation tools to speed up the arduous task of filling out earnings reports. That allowed its staffers to invest that time in telling stories instead. That value is anything but “limited.”

But AI can do much more, augmenting our work by making new connections that give us an edge. For instance, AI tools can comb through social media conversations at breakneck speeds, then take that analysis to the next level by closely analyzing tone and sentiment.

AI can provide invaluable insights that inform our decisions; all we’ll have to do is apply those insights to our creative and strategic decisions. And AI lets us do that in real time.

 

This article was Originally posted on – Digiday