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December 9, 2016

Artificial Intelligence - Making Life Easier (Part I)

Posted by Kenneth Toombs (View Profile | View All Posts) at 9:32 AM

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This blog post has been co-authored by Ken Toombs, Managing Partner, Infosys Consulting and Roberto Busin, Partner and Manufacturing Segment Head, Infosys Consulting.

Artificial intelligence (AI) is polarizing. Elon Musk has called it "our greatest existential threat," and tweeted that it is "potentially more devastating than nukes".

At the same time, renowned AI expert and Google/DeepMind Director of Engineering Ray Kurzweil has said, "...biological humans will not be outpaced by the AIs because they will enhance themselves with AI. It will not be us versus the machines ... but rather, we will enhance our own capacity by merging with our intelligent creations."

The debate is not about to be settled, but what we do know is that the age of AI is upon us. For evidence, we again turn to Elon Musk - the man intent on being AI's conscience - who has now embraced it through Tesla's commitment to self-driving cars.

Indeed, in this two part blog post, we see examples of AI making their way into industries as diverse as medical services, financial engineering and travel. The changes underway represent tectonic shifts that will play a key role in determining who wins and who loses in the coming decades and how AI impacts our everyday life.

How will AI impact business?

While the implications of AI on business can be vast, it can be distilled into three key questions, which will be addressed in these blog posts:

  • How will AI shift the expectations of customers?
  • How will AI transform the way my competitors run their businesses?
  • How should my company respond to AI, and what will the implications be if we fail?

How will AI shift the expectations of customers?

AI is fundamentally altering customer experiences in two important ways. One, it is personalizing customer experiences in ways that used to be reserved only for premium clients, and second, it is moving beyond the delivery of tools into the delivery of solutions that can work with unstructured data.

Let me explain. Historically, when a customer engaged with a business, the exchange would start with establishing a dialogue with the customer, learning about their needs, and then prescribing solutions. In a world with AI however, the conversation starts with businesses harnessing contextual information, comparing it with millions of historical patterns and determining what customers need, even if customers aren't able to articulate it themselves. This enables businesses to respond to customers in a highly contextual manner, providing a far superior experience. To demonstrate, let's look at a few real world examples.

Most customers find calling the customer service frustrating. The customer spends time educating the representative about what they need and then often receive solutions to the wrong problem.

Yseop Smart Machine observed this situation and developed an AI program that acts as a 'smart coach' for customer service teams by providing contextual knowledge based on both pattern recognition and CRM data. Instead of relying on training and experience to suggest solutions, the smart coach aggregates meta data about a client and guides the customer service representative through a dialogue that's being informed by all of the conversations that came before it. Even better, the system evolves as time passes, adjusting to changing customer preferences as markets change and more permutations are recorded.

Adding another layer of value, Mattersight offers a tool that uses AI to classify callers based on personality traits and match them with people who communicate in a complementary style. Building a comprehensive emotional profile of people based on their language structure and word choice, Mattersight determines which call center employee is best suited to have an immediate emotional connection with the caller.

Combining Yseop and Mattersight yields an experience where customers are connected with someone they innately relate to, who already know about them and can seamlessly leverage big data to suggest exactly what they need.

Personalization of service is also happening in industries that require highly sophisticated customer interactions, such as financial services. The outsized role of machines in equity markets has been well documented. According to Thompson Reuters, algorithms now account for 75% of all financial market volume, a number that continues to grow.

However, private individuals want a customized, human interaction that matches sophisticated analysis with their unique financial goals. Historically, this has been too expensive to scale and has been reserved for the ultra-wealthy, leaving the investing masses with a disparate set of generic, complex tools.

Startup WealthArc is capitalizing on this customer pain point by "leveraging data analytics and artificial intelligence support systems to empower wealth managers to transform the way they share relevant and understandable information with clients."

Essentially, they're using AI to take a service - in this case heavily customized financial recommendations - that has historically been reserved for 1 percent of investors and extend it to all investors cost-effectively, thus creating a game-changing offering that can be matched only by the power of algorithms.

The CEO of WealthArc sums up this transformation nicely, "I believe that the future of private wealth management will be like Mr. Spock from Star Trek - thinking like a machine but with a human mother."

Moving beyond tools to enhance customer experience

In addition to customer intimacy, the nature of the problems being solved by companies is also changing. Historically, web applications provided users tools to solve problems: banks gave apps to deposit money, retailers offer sites with clothes to search through and buy, and so on.

But with the advent of AI, companies are taking unstructured customer problems - "I want a fun dress for my company holiday party" - and transforming them into complete solutions. A case in point is travel services company, WayBlazer. Speaking with the Harvard Business Review, Terry Jones, founder had this to say about the company:

"I started as a travel agent, and people would come in, and I'd send them a letter in a couple of weeks with a plan for their trip. The Sabre reservation system made the process better by automating the channel between travel agents and travel providers. Then with Travelocity we connected travelers directly with travel providers through the Internet. Then with Kayak we moved up the chain again, providing offers across travel systems. Now with WayBlazer we have a system that deals with words. Nobody has helped people with a tool for dreaming and planning their travel. Our mission is to make it easy and give people several personalized answers to a complicated trip, rather than the millions of clues that search provides today. This new technology can take data out of all the silos and dark wells that companies don't even know they have and use it to provide personalized service."

WayBlazer has fundamentally shifted where the thinking takes place to plan a trip. Instead of a customer saying to themselves, "I want to plan a romantic weekend with my partner to a quiet island" and then using a set of tools to run an exhaustive search for hotels and flights, they simply articulate their problem statement and the program solves the problem for them in a way that is informed by all of the customer experiences that have come before the program.

Stay tuned for the second part of the blog coming up next week.

Comments

This is quite Informative. AI by large has been based on pre-programmed intelligence built into system. The intelligence of DeepBlue for instance does not lie in the system but in the minds of the programming team. Deepmind however goes a step (rather many neuroscience steps) further and Google's synergy with it after acquiring it will manifest itself in true AI.

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