Going Behind the Buzzword
But with great hype comes great responsibility — luminaries such as Elon Musk have suggested that uncontrolled AI could lead to a global catastrophe, and Vladimir Putin declared that whichever nation leads in AI “will be the ruler of the world.”
With any breakthrough technology come the platforms, tools, and services that suggest they can help marketers tap into the “power of this new force.” So how can marketers really use AI to drive business growth?
One of the reasons it’s so tough to “do AI right” is because its definition is constantly in flux. The term Artificial Intelligence has been around since the 1950s, and is used to describe the branch of computer science that aims to build machines capable of intelligent behavior.
The larger AI umbrella encompasses smaller technology and methodology groups, such as machine learning, smart robotics, computer vision, natural language, autonomous vehicles and virtual agents. Machine Learning is the largest area of investment, attracting around 60% of total venture investments. It involves programming computers so they can learn without being reprogrammed (quite literally, teaching machines how to learn).
If the definition of AI still seems vague, it can be helpful to understand AI by thinking about what it’s not. That’s why the term “robots” can be misleading — because mere automation doesn’t really rank under “intelligent behavior.”
The development in AI technology has been accelerating. In 1996 Gary Kasparov beat IBM’s Deep Blue, the chess playing supercomputer. A year later, Deep Blue finally took Kasparov’s crown. Less than a decade later, in early 2016, Google’s Alphago beat the world champion Go player, a game with almost infinite moves, an order of magnitude more complex than chess and something that most experts thought could never happen.
That technical acceleration is also reflected in the speed of investment into the sector. Billions of dollars have been invested in over 2,000+ AI-related startups over the last several years. According to McKinsey, tech giants like Google, Adobe, Amazon, Microsoft, Oracle, Salesforce, IBM, and Facebook are AI’s biggest spenders.
3x Investment Levels
Numerous factors contribute to today’s thriving Machine Learning climate: the availability of fast-changing data across vast tracts, access to cheaper and omnipresent sensors, the ever-lowering costs of cloud storage, and the ever-increasing skill of processing. In turn, this surge in one aspect of AI is driving other technologies, opening the floodgates for more experimentation across the broader category.
Additionally, many of the big players have open-sourced their data, improving the data pools of new entrants and encouraging innovation. With any “hot” technology one expects an initial period of hype and over-promise, but due to all of these advances, AI tech is already passing “the peak of inflated expectations” (Gartner) and becoming useful to consumers.
The most family friendly and physical embodiment of AI – virtual personal assistants such as Amazon Echo and Google Home – are set to go mainstream over the next twelve months, truly putting the power of AI in the hands of consumers. This shift has the potential to completely change the way we buy goods and services, positioning AI as more integral than ever before.
Though we may not notice it, AI is already a part of our everyday lives. Everything from Google search to Netflix to our Facebook Newsfeeds host recommendation engines that are powered by Machine Learning. In recommendation engine technology, every additional request made by the user acts as new information, and the machine then uses this data to improve user experience, connecting consumers evermore closely to the service. Netflix alone estimates that deploying machine learning has helped retain subscriptions worth $1 billion annually.
eBay, Pinterest, and online retailer ASOS are just some of the companies that leverage visual search — technology that turns the user’s smartphone camera into a discovery tool. With visual search, the user simply takes a picture of a product, from which the “machine” identifies shape, color, and pattern to cross-reference with inventory and serve up relevant results.
Marketers have long benefited from advancements made in search, programmatic, and social targeting, but now the industry is looking toward dynamic creative messaging solutions. The end goal of these solutions is to tailor messaging not just to past behaviors, but also to the different psychological profiles of consumers. The solutions being built for this area aim to consolidate data, analyze user activity, and use algorithms to optimize messaging across channels.
Chatbots & Voice Technology
As indicated by the rise in AI assistants, natural language technologies and chatbots have already seen rapid growth and innovation. While the early versions of Siri and Alexa may have been clumsy, these technologies have increasingly become more effective, leading many other companies to experiment themselves.
Though the technology is nascent, the potential for early adopters is huge. For example: Macy’s On Call is an in-store smartphone-based helper, powered by IBM’s Watson AI technology. When a customer enters a store, they can go to macys.com/storehelp and start chatting with the digital assistant. Using natural language processing, Macy’s can understand a wide variety of requests and direct shoppers towards their desired items within the store. But the bot’s most impressive aspect is also the most human: it detects when users are growing frustrated with the machine’s conversation skills, and readily alerts the closest member of the in-store staff.
Tesla, BMW, and Nissan lead auto manufacturers in their machine learning investments, and one key technology is motivating their spends: driverless cars. Toyota is investing $1 billion in a new research institute devoted to AI for robotics and driverless vehicles. They’ve launched a $100 million venture capital fund to invest in similar companies — the first three companies to get financing include a company that creates autonomous car-mapping algorithms, a manufacturer that produces intelligent cameras, and a developer of robotic companions for the elderly.
Fundamentally, brands and businesses need to consider the way AI will disrupt their current purchase funnel and turn what could be a threat into an opportunity:
- Don’t be distracted by the hype. It’s easy to get excited about the trends in AI, but true success is achieved by a deep understanding of objectives and desired achievement. AI is a solution to help you achieve your goals — not a goal in itself. Use the technology to tell your brand stories or shift audiences towards your products and services, rather than making the tech the only story.
- AI needs data to learn. Often this starts with interrogating existing data – small data sets can be just as insightful and useful in achieving your goals for a fraction of the cost of tech-heavy, big data solutions. You don’t always need to conduct a new research push to get desired results.
- Take the time to understand what consumer benefit the technology will deliver. Strive to build AI that adds value to your relationship with consumers either as a standalone function or as a part of an existing ecosystem. For example, don’t build a recommendation engine that always recommends the same thing. Ensure you work with partners that can articulate this.
- Keep in mind the human and emotional impact of using these AI techniques and technologies. Brands are built on trust — even the most helpful AI communication can erode a consumer relationship if it feels invasive. Partner with people who understand this.
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