The Growth of Artificial Intelligence in E-commerce

Artificial Intelligence is altering the ways e-commerce stores operate as it offers new ways of analyzing Big Data, helping retailers to engage with their customers on a new level and create better customer experiences.

Companies new to the space can learn a great deal from early adopters who have invested billions into AI and are now beginning to reap a range of benefits. After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses—and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. Automakers harness the technology in self-driving cars.

A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices ranging from web browsers to turbine sensors.

The entrepreneurial activity unleashed by these developments drew three times as much investment in 2016—between $26 billion and $39 billion—as it did three years earlier. Most of the investment in AI consists of internal R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google.

For all of that investment, much of the AI adoption outside of the tech sector is at an early, experimental stage. Few firms have deployed it at scale. AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership. Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment.

However, early evidence suggests that there is a business case to be made, and that AI can deliver real value to companies willing to use it across operations and within their core functions. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.

This adoption pattern is widening a gap between digitized early adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecoms or financial services, are also leading AI adopters and have the most ambitious AI investment plans. These leaders use multiple technologies across multiple functions or deploy AI at the core of their business. Automakers, for example, use AI to improve their operations as well as develop self-driving vehicles, while financial-services companies use it in customer-experience functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.

Governments also must get ahead of this change, by adopting regulations to encourage fairness without inhibiting innovation and proactively identifying the jobs that are most likely to be automated and ensuring that retraining programs are available to people whose livelihoods are at risk from AI-powered automation. These individuals need to acquire skills that work with, not compete against, machines.

The future of AI will be innovative, but may not be shared equally. Companies based in the United States absorbed 66 percent of all external investments into AI companies, China was second, at 17 percent, and is growing fast. Both countries have grown AI ecosystems—clusters of entrepreneurs, financiers, and AI users—and have issued national strategic plans in the past 18 months with significant AI dimensions, in some cases backed up by billions of dollars of AI-funding initiatives. South Korea and the United Kingdom have issued similar strategic plans. Other countries that desire to become significant players in AI would be wise to emulate these leaders.

Significant gains are there for the taking. For many companies, this means accelerating the digital-transformation journey. AI is not going to allow companies to leapfrog getting the digital basics right. They will have to get the right digital assets and skills in place to be able to effectively deploy AI.

When we talk to people about artificial intelligence, or AI, they’re often optimistic about how they think AI will improve their lives in the future. What they aren’t thinking about is sweaters. Specifically, how based on previous online behaviors an e-tailer knows that you’re in the market for a warm, blue sweater instead of a long-sleeved gray shirt. Historically, businesses required human interaction in order to understand exactly what a customer needed. Today, that’s no longer the case. Thanks to lots of data and AI, companies now know exactly what you want, when you want it and how you want it.

Some may fear AI, but I think of it as doing for minds what the Industrial Revolution did for muscles: machines taking on difficult and repetitive tasks and improving output. As in the case of an online retailer, AI will change the way marketing is done by turning to machine learning in order to discover patterns in data on human behavior. We don’t have to look far into the future to see how AI will make every marketing campaign smarter and more personalized; we’re already there.

To understand how AI will change the role of the marketer, we have to first look at what it does well. AI finds patterns in data, which we humans do poorly by comparison. Here’s why: first, there’s too much data to pour through, and second, we’re biased. We only ask what we know to ask for. By contrast, machines aren’t necessarily looking for something—they’re just looking.

By looking through massive amounts of information, AI discovers its own design as it goes. The machine finds surprising information in patterns, and not just in individual shopping patterns. It’s able to capture large and impactful trends like social patterns that define a community’s habits.

So why is the time for AI now? Only in recent years do we have the volume of data needed to find these patterns, and the economies of scale to be able to store it.

Marketing has traditionally been linear and deterministic. But with AI, journeys become intelligent and dynamic, and marketing can be predictive for each customer. The ability to capture, store and retrieve a boundless amount of data opens up avenues to reach customers with personalized messages and experiences. And, since the system learns, it gets smarter and more accurate with further iterations.

Let’s take a look at the famous auto company Peugeot and how it has been able to use AI to drive impressive marketing ROI. Of the millions of people who come through its doors, visit its websites, even look at an ad on the open web, only thousands of them buy a car. We can ask what sequences of events people followed to buy, or not. There are thousands of ways in, from downloadable content to walk-ins. Using data and AI, Peugeot created 800 micro-segments that enabled content personalization across 2,200 microsites. By following the buying journey of those who did buy, Peugeot saw how each path was different. With the power of AI, companies like Peugeot can tease out the many different variations to sell and market accordingly.

What about the creativity and art in marketing and advertising? Design remains a huge component of successful marketing. AI certainly has the power to help determine what sorts of digital ads, for example, a consumer is likely to click on—from color preferences to style and price. Many experiments with AI-created trailers, tweetbots and other interaction experiments have taught us that we still need both human and machine. The AI algorithm can interact, iterate and optimize for success, but design still lies in the art of human creativity.

AI removes the tedium and guesswork of running a marketing campaign, which includes creative, email, social media and more, by generating insights from haystacks of data. But the marketer still designs and runs the campaign. What I see is people and AI working together to be more efficient than either one alone.

Creativity aside, the beauty of AI is that even if marketers don’t have specialized IT skills, they can still act on high-level insights. For example, iteration cycles start to become much faster as AI kicks in, so we think of this process as a layer cake.

The first layer is a huge quantity of data that AI uses to figure out what to probe for insight. If you perform a large-scale data-driven experiment with a piece of content to test engagement (like A/B testing), you’re going to get a lot of data back. This is the second layer. AI can then process that information quickly, and the iteration cycle gets shorter. In the third layer, AI works through nuances in the data so that it’s not just A or B, but a mix of both. By the fourth layer, AI has optimized the system, working out inefficiencies, maximizing speed and providing a clearer picture of customer preference and behavior.

Everything I’m describing here—the massive data gathering, the importance of trust, the ability to analyze, iterate and predict, and intelligent journeys—are separate pieces of what is already shaping up to be a seamless experience.

As marketers work hard to get ever closer to the customer at every touch point, AI gives them the total view of what’s possible and what makes sense, reducing the friction to customer satisfaction even as their expectations are constantly rising.


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