e-Social + AI DL IoT

Artificial intelligence, or AI as it's called, has been a buzzword for nearly a decade already, yet sometimes it still feels as though we’re just in the early stages of discovering what predictive analytics and machine learning can do for enterprises.

Nowhere is this truer than in marketing and sales functions. According to Forrester, as of 2017 marketing and sales accounted for more than 50 percent of all AI investments.

But when you look at investors who have already sunk serious money into AI projects, only 45 percent have seen any results at all. And among those who are seeing results, 25 percent agree that they’ve become more effective in their business processes. These discouraging numbers paint a vivid picture: Most marketing and sales teams simply aren’t properly equipped to implement AI.

As a marketing leader who has helped companies like Salesforce and Symantec with digital marketing transformations, I’ve seen many "use cases" of how AI is being employed by today’s leading marketers and sales forces. And I’ve learned that often, the best way to kick off an AI initiative and make sure everyone is on board is to show them where others have succeeded.

Here are three ways in which AI has completely transformed enterprise sales and marketing in the 21st century for at least some companies:

1. Predicting outcomes to increase lead generation

Marketing is by nature a very competitive and data-driven endeavor, especially at the enterprise level. Every facet of global, cross-channel marketing relies heavily on a competent knowledge economy comprised of data inputs (and proactive recommendations) gathered at every touchpoint with visitors, leads, and customers.

The massive impact AI has already had in marketing, and what we expect to see of it in the near future, is a hot topic here at MarTech Today. In my previous columns, we’ve explored how AI will be woven into marketing organizations, where it belongs in your marketing stack, and where CMOs should focus today to get the best results from their investments in AI.

There’s no doubt it’s become widespread; in fact, global spend on artificial intelligence is expected to grow from an estimated $2 billion this year to $7.3 billion per year by 2022, according to a study from Juniper Research. Yet, as abundant as it is, artificial intelligence is still a mystery to many.

Case in point: Only 33 percent of consumers think they use AI-enabled technology, yet new research shows that 77 percent actually use an AI-powered service or device.

Marketers are perhaps savvier to the opportunities than most, so it was no surprise that when my company, BrightEdge, recently asked over 500 marketers to identify the next “big trend in marketing,” 75 percent pointed to some type of AI application.

CMOs are challenged now to not only identify the right AI applications to solve specific problems, but to then sell those to the CEO, other company leaders and the teams that will use the technology. Today, we’re going to broaden the scope and take a look at just a few of the ways AI is transforming entire enterprises, particularly through the lens of marketing and IT integration.

The birth of the field of AI in the 1950s predetermined a new milestone in scientific thought: Since then, it became possible to use computer programs for solving mathematical, economic and other problems, which were relying before on human intelligence. The further development of programs and their growing complexity required more work on code, rules and decision-trees. At that point, realizing the need for more advanced data processing approach, researchers came to what is known today as “Machine Learning” — the ability of computer systems to learn without being explicitly programmed.

Today Machine Learning is an integral part of search engines, navigations systems, email providers and social media networks. But in the recent few years, the mobile app market has seen the biggest growth in the use of Machine Learning for customizing the app experience, boosting sales and providing app security.

A significant part of today’s mobile applications is connected to some extent to Machine Learning for the purpose of better user experience and app functionality. The examples of ML in the app include Google Maps (traffic predictions, ‘find parking’ feature ), Netflix (classification of the content by genre, actors, reviews, length, year and so on, personalized recommendations), Flo (period predictions and tracking), Uber (estimated time of arrival, cost of the ride, real-time information on maps), MSQRD (in-app face detection), Tinder (‘smart photos’ feature, personalized recommendations), and many others.