The ABCs of Artificial Intelligence, Bots & Commerce

More than just a Silicon Valley buzzword, AI technology is revolutionizing daily interactions—and it's something every company should care about.

While the phrase "artificial intelligence" may bring to mind talking robots and sci-fi movies, the reality is you likely already interacted with AI today without even realizing it. Artificial intelligence and bots are seamlessly integrated into many services we take for granted, including online shopping, search engines and grocery stores. The technology is ubiquitous—which means that to stay competitive, businesses need to understand the AI options in order to best serve their customers.

What is artificial intelligence?

Movies get some of it right—and some of it wrong.

Artificial intelligence (AI) refers to the capability of machines to perform human tasks that require cognitive elements. AI as a concept has been around for decades; talking machines and human-destroying robots have been shown in movies for a while now. While there is no immediate danger of computers running us, some of the intelligence depicted in movies is not all that outlandish. (2001: A Space Odyssey predicted tablet computers, and Back to the Future Part II showed us a future with wearable tech.)

Today, computers have beaten chess grandmasters. Self-driving cars are here. Virtual assistants such as Alexa, Cortana and Siri are getting smarter. When it comes to very specialized tasks, the state of AI is promising. But there is a huge gap between a human's ability to perform a wide range of tasks and a computer's. That gap is even wider when it comes to mimicking or responding to the emotional aspects of us Homo sapiens.

How is artificial intelligence achieved?

Just like humans, machines learn.

The process of machines achieving artificial intelligence is no different than humans achieving intelligence. Humans learn in a variety of ways:

·     We receive information from our parents, teachers and society. We process and store it as foundational intelligence.

·     We explore. For example, we burn our hands and learn not to touch the stove.

·     We experiment and ask questions.

·     We draw inferences by correlating different pieces of information.

·     We come up with abstract ideas based on what we already know.

Computers are no different—they learn. Their learning process, called machine learning, works very similarly. Computer systems develop models by ingesting lots of data and applying advanced algorithms to develop a deep understanding of patterns and insights.

Humans play the role of parent/teacher (at least for now) and tweak these models to improve accuracy. The level of human involvement varies. In the case of “supervised learning,” humans typically provide input and output variables, and machines derive relationships and predict values for unseen or future data. With "unsupervised learning," we turn the machine loose at data, and it identifies interesting patterns.

What is a bot?

Think of a bot as a small-yet-mighty AI robot for the internet.

In this context, "bot" is short for web robot. Essentially, it's a software program that performs automated tasks over the internet. The simplest and most widely used example is search engines. Search engines need to build a dictionary of websites, with indexes of their content. This requires automated programs to crawl the massive web and gather content…over and over and over again. Other examples of bots are self-service customer service interfaces.

Bots can be used for both good and bad purposes. They can be used as customer service agents, personal assistants and librarians. They are also used to spread viruses and malware, falsely increase the ratings of apps, or even cut ahead in line to score good seats at concerts.

Why the sudden uptick in AI and bots?

AI needs data—and there's a lot of that to go around.

AI is deeply rooted in data. The information landscape has been evolving very quickly, which allows for the rapid growth of AI. Some ways the landscape has changed include:

·     Abundance of data – The Internet of Things (IoT)—devices, machines and objects that collect and transmit data without human-to-computer or computer-to-computer contact—has created an abundance of data. From heart monitors to automobile sensors to fitness trackers, there a lot of new items producing a lot of data.

·     Cost of storing data – Storage is cheap, and information is an asset. This mantra has been adopted and valued by modern businesses. Storage technologies have become commoditized, and cloud storage makes it even more affordable by accounting for economies of scale.

·     Cost and speed of processing data – It wasn’t long ago when only really big businesses could dream of processing a few TB of data. That scale is certainly laughable today. The cloud has really made a difference, as you don’t need to invest in big infrastructure if your processing needs are occasional.

·     Ecosystem – AI has a lot of interest from a huge variety of companies, from big computing vendors to academia. We're witnessing the snowball effect; as more companies gather interest, the more common AI becomes.

Why should a traditional business care?

Simply put: businesses that don't adopt new technologies risk getting left behind by competitors.

Machine learning and AI drive a number of very successful businesses. From suggesting what movie to watch to pricing real estate, use cases and success stories are significant—and numerous.

These technologies aren’t just for those working in the tech field; they have become relevant for most businesses. A number of AI and bot uses have become mainstream enough to provide significant competitive advantage. Some of these include:

·     Customer service – Most big businesses are using bot technology already. From Smart Search to Intelligent Routing to complete self-service, this area has had years of investment to reduce the cost of call-center capabilities.

·     Fraud and risk – Machine-learning technologies are commonly used to quantify financial risk, ranging from loan default to investment strategies. Credit-card companies and banks have used these technologies for decades now. What has changed is the speed and accuracy of these machine-based decisions.

·     Personalization – Personalization is a key differentiator for businesses. It has the ability to deliver great customer experiences, which create customer loyalty. A number of personalization technologies (including data management platforms and recommendation engines) have become mainstream.

·     Sales and marketing ROI optimization – Wouldn’t it be great to know what customers are most likely to buy your products, or what customers you risk losing? Advanced analytics are increasingly being used to compute those probabilities, resulting in better allocation of your sales and marketing resources.

·     Manufacturing and maintenance – Equipment shutdowns cost businesses a lot of money. With today’s sophisticated AI technologies, it's possible to predict part failures and replace them as part of proactive maintenance programs.

It's clear that AI and bots have already affected daily commerce. Even if your company isn't in the tech industry, it's critical that you evaluate how they impact your business. It's more than a trend—AI and bots are here to stay, and companies that want to stay competitive will need to be at the forefront of the AI revolution.

Mukesh Kumar is a Managing Director at Slalom and a recognized leader in business technology solutions and analytics. With deep expertise in technology strategy and enterprise architecture, Mukesh is passionate about using technology and innovation to drive business outcomes.