Reasons enterprises, Artificial Intelligence need to connect better

Artificial Intelligence is a hot topic across the board. Everyone, from developer to data scientists, journalists, and general user-bases of all profiles are talking about it on a global scale. Entrepreneurs have taken notice of the benefits of AI in most areas of everyday life, whether assistants, mobile, entertainment, and even healthcare or insurance. In turn, the demand for companies to adopt AI technology has risen in unprecedented levels in recent years.

Before the large-scale adoption of AI came into play, there was much speculation over the way it could potentially disrupt numerous industries. Many were concerned it would replace humans and somehow take over the world. To the contrary, AI serves as a counterpart to humans by increasing efficiency and eliminating needless frustrations. It has the capability to assist enterprises with error reduction, data management, medical applications, monotonous tasks, and more- all of which allows their human counterparts to better utilize their skill sets towards tasks that are more meaningful. According to a report by PwC, AI will also serve as a global economy booster, by contributing as much as $15.7 trillion to the world economy by 2030 due to productivity and personalization improvements.

Tech giants such as Google, Amazon, Facebook, Baidu, and Microsoft have made huge strides towards AI adoption by using AI tools and frameworks to create usable and impressive products, all of which were greeted with acceptance. I’m sure you’ve heard of IBM’s Watson, for instance.

The success of these tech giants helped create a ripple effect, inspiring other enterprise corporations to look into ways they can also integrate AI into their processes and/or product offering. But some things are easier said than done. In most cases, it isn’t advisable for the fresh new technology to be built in-house, as it ends up getting costly in terms of time and resources. The best bet is to implement the innovations that are offered by AI startups that have already tried, tested, and perfected the technology that the enterprises are searching for. But even that can sometimes be easier said than done.

The discovery aspect- the ability for enterprises to find the right startups with the right innovations- is broken. It’s tedious for enterprises to allocate resources to find relevant technologies by sifting through marketplaces of innovation. The frustration is multiplied when factoring the PoC (Proof of Concept) element.

Add this to the lack of scale in hiring data scientists in-house. This problem will grow bigger and become a multi-billion dollar problem over the next decade – and I couldn’t find a single promising solution to gain access to such talent, putting aside the growing market of outsourced data science contractors.

54% of enterprise incumbents see data storage, privacy,and protection as the main regulatory barrier to innovation. There is also the issue of compatibility. Will the PoC even work? Is it worth the effort? Not only are enterprises on the hunt for AI startups- the startups themselves are also eager to collaborate with large enterprises in an effort to scale. However, each aspect of the process to make it happen, at least in the traditional sense is, gently put, tiresome.

Despite the fact that 80% of executives view AI as a strategic opportunity, only 15% of enterprises worldwide are presently using it. This is largely due to the inconveniences that go behind the implementation process. On the flip side, 31% of enterprises have the adoption of AI on their agendas in the next 12 months. In many of these cases, the sudden spark in adoption stems from the fact that the enterprises came across ways to make the PoC process more efficient.

“PoCs are something both enterprises and startups want, but the process isn’t getting easier,” tells me Toby Olshanetsky, CEO of prooV that aims to simplify the POC process, “Quite the contrary, with all the noise in today’s ecosystem, enterprises are only finding it more difficult to run effective PoCs. The demand for AI today is equal to how badly those AI startups need new clients, and that’s an opportunity for both sides of the ecosystem.”

At the end of the day, the ultimate goal for any enterprise corporation is efficiency, productivity, and of course significant increases of the bottom line. Technological innovations have made it such that the implementation of AI is the surefire way to make that happen. Now is the time for CTO’s to explore the many ways their respective companies can benefit from AI, and begin research on startups that have already developed the technology that will help reach those objectives.

The adoption of AI technology is taking place at an accelerated rate, and it is crucial for all enterprises to do so in order to stay ahead of the curve.

By Yoav vilner, CEO, startup-mentor and a blogger with ReadWrite, Huffington Post, Forbes, Inc, Entrepreneur, Venturebeat, CNBC and TheNextWeb.

Article courtesy ReadWrite