Why AI, Machine Learning Will Become Mainstream in 2017

Artificial intelligence is poised to hit the mainstream in 2017, as the value of machine learning and analytics has become apparent. Here are some predictions.

“AI is a big investment theme for many VCs, including myself. While the vision of autonomous everything taking over all aspects of the way we live, work and play is an enticing one, it is clear that we aren’t even close yet (sorry to everyone I’ve accidentally called when trying to unlock my front door with Siri, or anyone who’s listened to this ghastly song). We are at the beginning of AI at the center of a transformational technology cycle, but startups and investors will need to stay patient and focused on the challenges ahead.” — Rick Yang, Partner at NEA

“In 2017, I expect to see an increased emphasis on democratization of machine learning and artificial intelligence. We’ve seen machine learning evolve from IBM Watson a few years ago to most recently with Salesforce and Oracle. While many think machine learning has gone mainstream, there is the potential for much more, such as performance monitoring and intelligent alerting. While companies might face false starts and initial mishaps while trying to crack the code, the increased number of organizations turning to AI and machine learning will lead to more successes next year. This increased adoption will help bring innovations faster to market, especially from a wide range of industries.” — Mike Kelly, CTO of Blue Medora

“There has been a lot of hype around machine learning for some time now, but in most cases it hasn’t been used very effectively. As we move forward, organizations are learning how to bring together all the ingredients needed to leverage machine learning, and I think that’s the story for 2017. We’ll see machine learning move from a mystical, overhyped holy grail, to seeing more real-world, successful applications. Those who dismiss it as hocus-pocus will finally understand it’s real; those who distrust it will come to see its potential; and companies that are poised to leverage this capability for appropriate, practical applications will be able to ride the swell. It will still be a few years before machine learning becomes a tidal wave, but in 2017 it will be clear that it has a credible place in the business toolkit.” — Jeff Everham, Director of Consulting, North America, Sinequa

“As connections continue to evolve thanks to internet of anything (IoAT) and machine-to-machine connectivity, silos of data will be replaced by clouds of data. Smart devices will collaborate and analyze what one another is saying. Real time machine-learning algorithms within modern distributed data applications will come into play—algorithms that are able to adjudicate ‘peer-to-peer’ decisions in real time. We will begin to see a move from post-event and real-time to pre-emptive analytics that can drive transactions instead of just modifying or optimizing them. This will have a transformative impact on the ability of a data-centric business to identify new revenue streams, save costs and improve their customer intimacy.” — Scott Gnau, CTO, HortonWorks

“For enterprises to succeed with data, apps and data need to be connected via a platform or framework. This is the foundation for the modern data application in 2017. Modern data applications are highly portable, containerized and connected. They will quickly replace vertically integrated monolithic software. Data will become everyone’s product with value to buy, sell or lose. There will be new ways, new business models and new companies looking at how to monetize that asset.” — Scott Gnau, CTO, HortonWorks

“Smart apps will proactively anticipate user desires to satisfy in the moment. Predictive analytics won’t just be an add-on for brands; it will become a must-have to understand and retain users. At Tinder, capturing rich data and predicting future behavior is essential to a good user experience. Leanplum empowers us to innately understand user preferences and serve up successful matches. In the near future, predictive analytics will further improve our understanding of trends and patterns, to help us bring more value to every person on Tinder.” — Jeffrey Morris, Director of Product Management and Revenue at Tinder

“Mobile teams will invest heavily in automation campaigns that contain personalized parameters to move users toward conversions, save acquisition costs and drive more ROI on every new user. Mobile marketing in 2017 will further strengthen the need for user retention over acquisition. Rich data will empower mobile teams to automate personalized travel experiences that draw from travel history, price trends, seasonality and more. Imagine a smarter travel app sending you an offer to book your holiday trip, and featuring options for your favorite airline, flight times you often choose, a hotel where you have a loyalty account, a car rental for your preferred model and reservations for a hot new restaurant. Mobile teams will be required to innovate and significantly improve the app experience, using automated campaigns that intelligently engage their users throughout their life cycle.” — Honey Mittal, Senior Vice President of Product at Wego.com

Chatbots “will impact not just the in-store shopping experience but also mobile shopping. Although social commerce has been traditionally associated with social media platforms, such as Pinterest, in 2017 this experience will be less about what retailers are trying to sell and more about supporting the customer much like a customer representative would in a store. Imagine that you had a chatbot when you walk into a store like Home Depot. Your ‘virtual assistant,’ via the chatbot, could answer: Where are the door hinges? Where are the doorknobs? Or imagine that you’ll be able to go into a department store and just type into your phone, ‘women’s blouses in black, and be mapped automatically to items based on your previous preferences and also local inventory.” — Kurt Heinemann, CMO, Reflektion

“In 2016 artificial intelligence was applied to solve known problems. As we move forward, we will start using AI to gain greater insights into ongoing problems that we didn’t even know existed. Using AI to uncover these ‘unknown unknowns’ will free us to collaborate more and tackle new, interesting and life-changing challenges.” — Abdul Razack, SVP & Head of Platforms, Big Data and Analytics at Infosys

“As the baby boomer generation retires, enterprises are on the brink of losing significant institutional mindshare and knowledge. With the astronomical price tag of losing these workers, enterprises are turning to knowledge management and machine learning to train AI to capture institutional knowledge and act on our behalf. In the coming year and beyond, we will see AI adoption not only come from technological need but also from the need to capture current employee insights and know-how.” — Abdul Razack, SVP & Head of Platforms, Big Data and Analytics at Infosys