7 Facts About Enterprise AI

Gartner predicts the business value created by artificial intelligence will reach $3.9 trillion by 2022. AI technology is finding its way into every corner of the modern-day enterprise, and company leaders across all industries are realizing its benefits. In fact, a higher proportion of executives believe that AI will be more of a “game changer” than any other emerging technology today – including cloud, IoT, mobile, blockchain, and more.

From streamlined operations to improved customer experiences, AI offers tremendous business opportunities. Here’s a look at the current state of AI in the enterprise and where it’s headed.

1. One in three enterprises have implemented some form of AI

Over the past three years, the proportion of companies with AI initiatives has grown from one in 25 to one in three, according to Gartner. The adoption of AI in the enterprise is happening at an unprecedented speed – possibly one of the fastest in technology history.

Today, there’s no shortage of AI-driven software suppliers and plug-and-play AI services. Nearly every global technology vendor, including Amazon, IBM, Google, and Microsoft, is also offering AI products or services. Companies now have a variety of AI solutions from which to choose.

2. One in 10 companies now use 10 or more AI applications

A number of AI applications have emerged to help improve business strategies and streamline operations. As a result, many companies are turning to AI-based applications for a competitive advantage. According to Gartner, one in 10 companies now use 10 or more AI applications, and revenue from AI-based applications is expected to reach $31.2 billion by 2025.

The most popular enterprise AI application use cases include:

• Chatbots (26%)

• Process automation solutions (26%)

• Fraud analysis (21%)

And although not as prevalent, companies are increasingly using AI applications for consumer or market segmentation, computer-assisted diagnostics, call center virtual assistants, sentiment analysis or opinion mining, facial detection and recognition, HR processes, and much more.

3. 82% of businesses have gained a financial return from their AI investments

According to Deloitte, 82% of businesses have gained a financial return on their AI investments. What’s more, businesses are achieving a positive ROI from AI across a variety of industries. From manufacturing to media and entertainment, AI can help improve operations and deliver higher quality customer service.

For example, Netflix discovered that customers who search for something to watch for more than 90 seconds give up and leave the platform. The company used AI to improve search results and found that they could reduce frustration and customer churn, resulting in $1 billion in savings per year for the company from potential lost revenue.

4. 23% of enterprises in North America now have machine learning embedded in at least one company function

McKinsey & Company surveyed 2,135 senior enterprise executives and 23% based in North America indicated that they have embedded machine learning in a least one business function.

North America is a global leader when it comes to enterprise machine learning. For comparison, the study showed that only 19% of Chinese and 21% of European senior enterprise executives had successfully integrated machine learning in business functions.

5. Business leaders are doing more to understand AI

Business leaders around the world are doing more to understand AI and how it can be used strategically. In the last 12 months, between half and two-thirds of leaders said they’ve improved their understanding of AI to a greater extent, according to MIT Sloan Management Review.

Business leaders are increasingly aware of the disruption AI may cause and the challenges it will bring to their businesses. AI is predicted to shift how companies generate value, and the technology will require workers to develop new skill sets to work alongside it. Therefore, business leaders must be prepared for AI’s effects. Leaders that haven’t already introduced AI in their business functions should at least familiarize themselves with the technology’s possible implications on their specific business, workplace, and industry.

6. Demand for AI talent has doubled in 24 months

The supply of AI talent simply isn’t keeping up with demand. Currently, there are two roles available for every one AI professional. The technology and financial services industries are absorbing 60% of available AI talent, resulting in limited access to AI talent in other sectors and causing an academic “brain drain.”

The good news is that supply is increasing. According to MMC Ventures, machine learning is the top emerging field of employment in the United States. Data science boot camps, online courses, and the inclusion of AI in university computer science courses are all helping to close the AI talent gap. For now, investing in staff training programs and upskilling current employees can help organizations facing a talent shortage get the help they need.

7. 47% of tech leaders have concerns about whether or not their team has the skills to implement and support AI and ML projects

Staff readiness remains a top concern among tech leaders who are taking AI and machine learning projects to production in their organizations. A recent Tech Pro Research poll found that most respondents feel their AI/ML projects will be more difficult than previous IT projects. Furthermore, 47% of respondents worried that their IT team lacks the necessary skills to implement and support these projects.

AI and machine learning are emerging technologies, so it makes sense that so many business leaders feel uneasy about how they can implement AI and ML projects successfully. Between a skills gap and lack of tools necessary, businesses often fail to get machine learning models into production or face extremely long delays when doing so.

How enterprises can work with AI

To combat any hesitations surrounding AI and ML, company leaders should consider smaller pilot projects and proofs of concept before full implementation. Data science platforms such as Quickpath can help with pilot projects as well as bridge the gap between the team who builds the AI or ML models and the deployment environment – including infrastructure and IT requirements. This not only allows businesses to test their solutions on a small scale but it can help teams feel more comfortable about deploying future AI and ML projects as well.

Quickpath grants organizations a repeatable and consistent path to AI and ML production, helping leaders make more data-driven decisions and extract more business value from these emerging technologies.

For more information about how Quickpath helps enterprises achieve their goals with AI, check out this overview of our platform or contact us for a free demo today.