A common goal among companies in today’s data-driven world is to become smarter—to know where the market opportunities lie, where supply chain logjams are and where process improvements can be found. Data science has been the fuel behind this trend, and now data science is itself becoming smarter. Thanks to astonishing advancements in artificial intelligence (AI) and its sub-segments machine learning and deep learning, companies are achieving new levels of efficiency in data analysis that impact their entire business. The rising tide of AI adoption across industries will drive significant growth in the next decade, with AI software revenue set to reach almost $90 billion by 2025. AI’s presence is tantalizing to data scientists and business managers alike who seek to let machines do the number crunching to make the business smarter on a holistic level.
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AI on the Fast Track
A leading indicator of a market segment’s growth path can usually be found by following the money trail. Investors and venture capital (VC) firms are always looking for big growth opportunities, and they are finding one now in the AI business. Forbes recently reported that there has been a 14x increase in the number of active AI startups since 2000, and investment into these startups by VC firms has increased 6x in that period. Meanwhile, companies that both build and utilize AI applications are on a similar growth path, with jobs requiring AI skills increasing 4.5x since 2013.
IT Is a Big AI Beneficiary
It should come as no surprise that the department that deals full-time with data—namely the IT organization—is perhaps the biggest beneficiary of AI’s capabilities. A Harvard Business Review study reports that between 34 and 44 percent of global companies they surveyed are using AI to help resolve employee technical support issues (imagine a smart response system to streamline common questions and troubleshoot others), automate internal system enhancements (machine codes can be used to calculate where bottlenecks can be fixed), and ensure that employees only use technology from approved vendors (picture a smart authorization engine that keeps up with daily updates and knows vendor subsidiaries and partners).
But AI Is Cross-enterprise Too
Where else is AI finding a home? Among the most common examples of AI in the enterprise are image recognition and tagging, patient data processing, localization and mapping, predictive maintenance, predicting and thwarting security threats, and intelligent recruitment and HR management techniques. But perhaps the most active adoption is being seen in the marketing and sales operation, where intelligent use of data and the ability to learn from human interactions can produce big financial benefits. In a Statista worldwide survey, 87 percent of current AI adopters said they were using or considering using AI for sales forecasting and for improving e-mail marketing. While sales forecasting is often automated by technology to a point, it can be vastly improved with an AI agent that monitors and reacts to customer interactions and shifting market patterns. Email marketers similarly can create a sense of one-to-one marketing through more intelligent targeting and content creation for various audiences.
The bottom line is also important. McKinsey found that companies who benefit from AI initiatives and have invested in infrastructure to support its scale achieve a three to fifteen percentage point higher profit margin. Healthcare, financial services, and professional services are seeing the greatest increase in their profit margins as a result of AI adoption.
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Examples of Companies Taking the Lead in AI Adoption
Here are a few examples of how specific companies in various industries are leveraging AI in their businesses:
According to the McKinsey study, tech giants including Baidu and Google spent between $20B to $30B on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. The current rate of AI investment is 3x external investment growth since 2013.
Netflix has also achieved impressive results from the AI algorithm it uses to personalize recommendations to its 100 million subscribers worldwide, improving search results and avoiding canceled subscriptions from frustrated customers who couldn’t find what they wanted (with a potential impact of $1B annually).
Financial data specialist Bloomberg uses techniques like computer vision and natural language processing to improve the breadth of information available through their ubiquitous terminals that financial staff use to access market information. Users can use natural language in queries instead of specialized technical commands, which is analyzed and executed by AI.
Uber has a core team providing pre-packaged machine learning algorithms 'as-a-service' to its team of mobile app developers, map experts, and autonomous driving teams. These capabilities are used to better predict traveling habits and improve maps using computer vision, and to create algorithms for its autonomous vehicles.
And Royal Bank of Scotland recently launched a natural language processing AI bot that will answer its banking customer questions and perform simple banking tasks such as money transfers, with the goal to make digital customer support as powerful as face-to-face interaction.
AI and machine learning are revolutionizing the way companies access and process data to become smarter and more efficient organizations. And IT and data science teams are gearing up for the immense benefits of AI in their enterprises.