To say that artificial intelligence (AI) is the next step in enterprise would be an understatement.
AI has already become a reality for many industries, including, but not limited to:
In other words, the so-called “AI revolution” is already here. Moreover, it’s growing in strength and popularity.
According to McKinsey and Company, by 2030, 70% of companies will have adopted at least one kind of AI technology. The expansion of AI also stands to have a significant impact on the world’s economy and job-force.
The hype around AI has also led to a rapid increase in companies investing in AI and Big Data out of fear of being left behind. According to New Vantage Partners’ 2019 Big Data and AI Survey, 88% of Fortune 1000 companies feel an urgency to invest in big data and AI, with 55% of these companies spending more than $50 million dollars on these investments.
McKinsey and Company also predict that AI technologies could lead to a performance gap between companies that fully absorb AI tools across their enterprises over the next five years compared to those that do not by 2030.
But it’s not just about a company’s fear of being left in the dust; 84% of global business organizations believe that AI will give them a competitive advantage, according to MITSloan Management Review.
How? Let’s start with Chatbots. Innovation Enterprise believes chatbots will power 85% of customer service by 2020...yeah, that’s soon.
And of course, revenue and economic gains are major factors in why companies are realizing AI’s worth. PwC believes AI could contribute up to $15.7 trillion to the global economy in 2030, while Tractica predicts the AI software market to reach $118.6 billion in annual worldwide revenue by 2025.
And when Element AI revealed in its 2019 Global Talent Report that the number of people claiming to have the educational and skills profiles to qualify as an AI expert rose by 66% between 2017 and 2018, it’s easy to understand where the job landscape in tech is heading.
While it is well known that AI is the next step forward, myths and misconceptions about AI and its processes still run rampant. It lead us to an important question:
So, we created a two-part survey and quiz to help us examine how well industry executives comprehend AI and machine learning (ML).
The results of our survey supported one clear answer: Business and industry executives do not understand the majority of AI and ML.
These are the leaders in charge of implementing AI projects, the ones responsible for determining how AI and ML will be used and what it will be used for. And that leads to another key question:
In order for AI and ML to be used to their maximum potential to help streamline enterprise, reduce costs, reduce risk and increase profits, it needs to be implemented with precision by those with realistic expectations.
The drive to incorporate AI and ML into enterprise is there. Of the respondents, 44% had ongoing AI or ML projects, 14% were working towards implementing it, and 17% were not yet working on it, but want to.
Our survey and quiz showed that myths about AI and ML are prevalent among industry executives. It’s also interesting to note that students scored higher on the AI and ML quiz than C-Suite level executives did, even those engaged in ongoing AI and ML projects at their company.
On the other hand, it’s not surprising to see that students outperformed C-Suites. While classes in AI and ML are now common at all technology-centric universities, that is a recent development. Chances are, when most of these C-Suites were in school, taking courses on AI wasn’t an option.
For those familiar with AI and ML, it is well understood that ML is a subset of AI. However, 31% of our respondents answered that AI and ML were two completely separate things.
