5 AI Trends and Challenges to Look for in 2020

5 AI Trends and Challenges to Look for in 2020

The landscape of many industries is changing with the widespread adoption of artificial intelligence. Devices are becoming more autonomous. Voice control is an increasingly used interface method. Customers are receiving more customized service thanks to recommendation engines.

The combination of AI and Internet of Things (IoT) devices is even changing the oldest industry of all, agriculture. But deploying AI technologies — such as machine learning, vision processing, and natural language programs — in ways that optimize processes and positively impact the bottom line is challenging. Fitting AI into legacy workflows can do more harm than good if not done correctly. Moreover, bias in algorithmic decision making can produce skewed results that put companies at risk.

Looking ahead, 2020 will be a year of rapid AI adoption and equally rapid learning how to use those technologies effectively. Here are five trends you should follow as the year evolves:

Voice is the New Mobile

Alexa and Siri were just the first wave of a radical shift in how we interact with AI. Tobias Dengel, CEO of WillowTree expects “every app will have to be re-engineered to be voice-first, just like we all became mobile-first a decade ago."

In addition to the widespread use of voice control, AI will improve the quality of voice interactions. “Voice assistant adoption will be massively improved if you get a level of empathy in that machine dialogue" says Holger Reisinger, senior vice president of large enterprise at Jabra.

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Tailoring the Customer Experience

The combination of AI and detailed data about people's behaviors and interests is changing the way retailers attract and interact with customers. While this tailoring of the customer experience emerged in the 2010's, 2020 appears to be the time where mass adoption of tailored customer experiences will take center stage.

The McKinsey Global Institute estimates that retailers can increase their operating margins by 60 percent by using data analytics. These opportunities are not limited to large enterprises. Small businesses have access to advanced AI, such as natural language processing, that is now delivered as a service by public cloud providers like Google Cloud and AWS.

“AI-based chatbots and virtual assistants further streamline business transactions and increase operational efficiencies, while also providing an optimal customer experience," says Phil Grier, commerce engineer at Yahoo Small Business.

AI Moves to the Edge

AI is not constrained to the data center or cloud, it is moving to the edge as well. Cloud is processing that exists on the internet, where edge exists on the device you're using.

"With edge AI, software can proactively interface with live data streams and cater to intelligence at or near the source, leading to increased overall productivity, efficiency, and cost-savings," says Senthil Kumar, vice president of Software Engineering, at FogHorn.

Intelligence at the edge has an obvious role in autonomous vehicles and manufacturing equipment but it will also impact agriculture. Applications include monitoring climate conditions, automating greenhouses, and collecting data on crop growth and conditions.

“Integrating AI into farming will strengthen farmers' expertise and knowledge by providing them with valuable insights and deeper understanding of their fields," says Ofir Schlam, co-founder and CEO of Taranis.

Adoption Challenges become Apparent

Deploying AI is no guarantee of a positive return on investment. In fact, fewer than 40 percent of businesses see a benefit from their AI efforts, according to an MIT Review survey.

This is not necessarily because of weaknesses in the technology - organizations need to learn how to apply AI to business needs. The lack of benefits from AI is in part an organizational issue.

“This is due to departmental structures (and politics), aging technology infrastructures, and a lack of senior and mid-level managers who can articulate business needs and translate them into technological solutions," says Liad Agmon, CEO of Dynamic Yield.

Even when businesses successfully integrate AI and apply it to a business problem, there are risks around the impact of the use of AI. As Forrester report notes, “the spread of deep fakes, misuse of facial recognition, and overuse of personalization can harm, offend, or creep out customers and employees,"

Data Management Challenges

Data is the fuel that drives machine learning and other forms of AI. Getting that data into a usable form for AI is a significant hurdle that enterprises will need to address. The scope of the problem becomes clear with the fact that most companies only analyzing 12 percent of their data.

To compound the problem, enterprises are seeing the fastest growth in time series data, which requires specialized analysis. “Many enterprises," concludes Evan Kaplan, CEO of InfluxData, "will realize they need a specific strategy for time series data to glean the full value of its business potential."

As we move into 2020 and beyond, your organization will likely face some of these and similar opportunities and challenges. Turn to the AI and analytics experts at Keyence to get a jump start on your 2020 AI initiative.

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