As we start the New Year, one of the big trends that nearly everybody in the big data analytics industry agrees on is that AI will continue to grow in scope and importance. In many ways, AI is picking up where big data is leaving off. Whereas people once associated machine learning with big data, they’re now lumping that technology and its offshoot–deep learning–into the AI category.
Whatever definition you use for AI, it’s clear that the current phenomenon has legs. Encouraged by the AI success of tech giants like Facebook and Amazon and buoyed by consumer demand for “smart” devices, companies appear quite eager to bring AI tools and techniques to bear against their big data collections to generate actionable insight.
The numbers associated with AI’s growth are simply gaudy. The market research firm Tractica recently forecast that AI spending will grow from a paltry $640 million in 2016 to $37 billion by 2025. In particular, AI use cases like image recognition, algorithmic securities trading, and healthcare patient data management, “have huge scale potential,” Tractica research director Aditya Kaul says, while other use cases in consumer products, business services, advertising, finance, media, and defense can also drive big spending.
It’s tough to talk about AI without what is arguably its most visible use: self-driving cars.
Companies like Ford, Uber, and NVIDIA are investing billions in advanced sensors and decision-making technology with the ultimate goal of letting drivers take their hands off the steering wheel.
But it’s not just your little hatchback getting the AI treatment. “This revolutionary AI technology will also move beyond passenger cars into other types of vehicles for industrial and commercial use, including trucks, tractors, industrial robotics, and even retail environments,” says NVIDIA’s Senior Director of Automation Danny Shapiro. What’s more, vehicles will also be able to recognize your face, check your calendar, and suggest alternate routes through a city.
Speaking of retail, AI is on track to leave a lasting impression in how people shop. Scott Horn, CMO of customer engagement solutions firm 7, says a recent study showed that 40 of consumers are open to interacting with chatbots, and that about a third of people actually prefer the bots to phone and email-based communications. “For retailers, AI can automate many aspects of customer service, saving human agents for higher purposes, such as for retention and upselling,” Horn says.
In 2017, enterprises will start experimenting with deep learning for a greater range of activities, and for making predictions and recommendations based on different types of source data, according to Splunk VP Engineering Toufic Boubez.
“With deep learning, more layers of processing elements are added for the ability to aggregate not only textual data, but also more complex data like voice and sensor data into meaningful patterns,” he says. “In the longer run, advanced learning capacities allow all data to become part of a neural network fabric, expanding beyond internal data and encompassing higher-level data from outside sources.”
As they move into the enterprise, AI assets will need to be fed and cared for, just like every other IT asset. Assaf Resnick, the CEO of IT alert correlation company BigPanda, says a new discipline will be created around the management of the algorithms called AIOps.
“We’ve gotten past the hype of the Big Data wave and AI is replacing analytics,” he says. “According to Gartner, data exploration and experimentation by AIOps platforms provides IT ops teams with unique new business insights from combined IT/non-IT datasets.”
Some of the most promising big data analytic providers of the past five years focused on managing the end-to-end lifecycle around big data analytics. We’ll see something similar happen in the AI space, predicts Rajeev Dutt, CEO of enterprise AI solutions provider DimensionalMechanics.
You can leave the experts at the door, Dutt says. “Companies are creating easy to use AI models that don’t require a dedicated team of experts–simply plug and play,” he says. Dutt says 38 percent of businesses surveyed already use some AI technologies, a number that’s expected to grow to 64 by next year.
There’s been a brain-drain of AI talent over the past few years, as tech giants like IBM, Microsoft, and Google gobble up AI startups and attract ML experts with big salaries. It’s no wonder, then, that venture capital lists will keep an eye out for promising AI startups.
“AI will continue to be the hot funding item in VC/PE rounds, pBossibly approaching 25 percent of all events,” predicts Jeff Catlin, CEO of Lexalytics, a provider of NLP and sentiment analytics technology. “Anyone who watches the AI space believes that it will be the driving force behind software development for the next 10 years, and that means it will be the field where VC’s flock.”
There will also be spectacular failures, Catlin says. “Given the complexity, there will be a high degree of failures for both the funded companies and some of the VC firms that don’t have enough institutional knowledge to invest well,” he says. “We expect three of the well-funded ML/AI companies to go out of business, while a number of the lesser funded companies will not get off the ground. In addition, we’ll lose more than a few pure-play text analytics companies as ML/AI subsumes more and more of the functionality.”
As with any emerging technology, there’s a certain amount of hype that surrounds AI. We’ve certainly seen that with big data, which Gartner tracked over the years with its hype cycle, before abruptly dropping big data off the hype curve in 2015. We’re now at a similar point with AI, according to Talend CMO Ashley Stirrup.
“IDC predicts that by 2018, 75 of enterprise and ISV development will include cognitive/AI or machine learning functionality in at least one application,” Stirrup tells us. “While dazzling POCs will continue to capture our imaginations, companies will quickly realize that AI is a lot harder than it appears at first blush and a more measured, long-term approach to AI is needed. AI is only as intelligent as the data behind it, and we are not yet at a point where enough organizations can harvest their data well enough to fulfill their AI dreams.”
AI is picking up where big data left off. The only problem is that much of the work around efficiently using big data remains unfinished (we’re looking at you, data integration and governance). Joe Korngiebel, SVP of User Experience at Workday, sees a lot of work remaining.
“No, the machines are not taking over, but we are at a critical inflection point,” Korngiebel argues. “As companies implement advanced data strategies, it’s crucial we create systems that can solve problems quicker than we can for ourselves…Companies without a data strategy nor the ability to leverage AI will be operating at a deficit.”