Businessman drawing several arrows in same direction with the exception of one arrow pointing the other way. - AI in the mainstream concept
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Artificial intelligence (AI) is everywhere. As a rapidly developing tech that is integrating itself into every aspect of our lives, it exists in the virtual assistants living inside our smart homes through the Internet of Things (IoT) and by extension through our smartphones. As consumers, it drives our interactions with brands. But when it comes to business, what is encouraging this widespread adoption? According to Gabriel Shaoolian, founder and executive director of DesignRush, there are two main drivers:

1. AI Simplifies Processes, Reduces Human Error

AI technology streamlines workflow and simplifies solutions and services. With AI technology, it’s easier than ever to gather data and draw actionable conclusions that can increase revenue, drive business growth goals and establish your business as a leader in the industry. AI can eliminate repetitive tasks that take up time, reduce the risk of human error, lower costs and better prepare your business for the evolving world ahead.

AI tech establishes stronger customer relationships, giving your brand real-time insights into how your audience perceives their brand, what questions they’re asking and what ways you can strengthen this bond. This intuitive integration can manage large amounts of data and unearth successful ways to make your audience happier.

Related Article: How Artificial Intelligence Will Impact the Future of Work

2. The Cloud Enables AI Adoption

However, while the advantages of using AI are clear, the costs of investment and the difficulties of finding qualified staff to deploy have, until recently, been a major deterrent. What is different now, according to Kevin Verde, data practice manager at Onix, is that AI deployments and technologies are increasingly possible and economical through the cloud.

Even if it seems at the moment that everyone is using AI in the digital and marketing campaigns, he said that is not the full picture. While there is definitely signs of a major uptake in AI-driven technologies, just like cloud computing previously, there are many industries that are still unsure as to whether they should make the jump or not.

Data Drives AI Adoption

It is important to understand, that AI and machine learning are, essentially, a duo and coexist, Verde said. He pointed out that increasing amounts of data have spurred greater interest and adoption of AI and machine learning, a trend that will keep growing as our connected world produces more and more data. From the IoT to Gmail to social media and more, we’re generating more than 2.5 quintillion bytes of data in the cloud every day, according to the Domo’s study, Data Never Sleeps 6.0

Not only does the cloud facilitate this data explosion, but it has also played a major role in democratizing AI and ML, Verde says. “In the past, you needed incredible computing power and resources to enjoy the benefits of machine learning and AI. Because of the cost, this wasn’t available to smaller companies. You couldn’t do it on-premises without spending significant amounts of money,” he said. “Now, thanks to the power of the cloud, a two-person startup can do world-changing things with AI, something unimaginable even a decade ago. The power of cloud computing allows you to have widespread AI regardless of a company’s scale.”

The result is that AI and machine learning will continue to have an increasing role in enterprises driving better data-based decision-making for campaigns, client satisfaction and even internal communications and collaboration. The challenge is how to get there.

Related Article: 7 Ways Artificial Intelligence is Reinventing Human Resources

AI Reaches Maturity

A number of major technology companies are racing against each other to produce AI-driven products, which is a big help in normalizing the use of AI globally. For example, Google is developing AI capabilities in their Google assistant to call and make reservations for you, while Amazon is automating its white collar jobs. Essentially, enterprises are moving to adopt AI and machine learning to solve real world problems.

The primary driver of this is breakthroughs and maturity of AI/machine learning capabilities as well as the democratization of AI for regular users, said Nikhil Bhatia, Riversand’s senior director of product management.

These technologies are now being offered as managed services with easy to use libraries and user-interfaces, which has made it possible for nontechnical users who understand real world issues and problems to utilize and innovate, not just tech specialists who might be very good at using these cutting edge technologies, but do not fully understand which problems to solve. “In addition with the proliferation of cloud technologies the barrier of cost of entry to utilize these services is getting eliminated,” he said. 

Bhatia cites the example of the Google Duplex, an AI chat agent that can carry out specific verbal tasks such as making a reservation or appointment over the phone. Duplex is built on a recurrent neural network (RNN) using TensorFlow Extended, a general purpose machine learning platform.

All it will take is an app to connect different functions to this AI service, provided by Google, and everybody in the world has a concierge service in the palm of their hand (or on their wrist). With the understanding that AI/automation is not to be used to solve the entirety of the problem, but to automate and possibly improve the mundane and the repeated tasks which otherwise would just be time consuming without adding any real value, AI has finally come out the lab and into normal people's lives.

Building on AI Success Stories

Doug Bordonaro, the chief data evangelist of ThoughtSpot, an AI-driven analytics company, said that while the hype around AI has been building for years, across the business landscape, there are now a number of AI success stories that we can pattern implementations on. From smarter email inboxes to advances in diagnostics through image recognition, vendors and implementers know at least some of the ways that AI can improve their products and services, making it much less risky to follow that path than it has been before. “The same success stories have also increased the comfort level of the purchasers because they have an idea of what success looks like,” he said. “Few people want to be first, but no one wants to be left behind. As the track record for successful AI solutions grow, so too will adoption.”

Another factor is trust, which has increased significantly in the past year. A lot of this is being driven by the consumer market. As more people put an Alexa-powered device in their homes, use personal technology like email and cell phones that incorporate smart learning and other AI technologies, they’re increasingly looking for the same thing at work.

“We’ve learned — at least to some extent — to trust AI in our personal lives, now we’re extending that trust to the workplace,” he added.

AI continues to get better. While the base technology has been around for decades, the ability to consider context, personalization and outside factors is much stronger today than it was even in 2017. And that trend is accelerating, which increases the number of potential use cases, which increases the addressable market.