Artificial intelligence (A.I.) isn’t just some abstract concept or parlor trick: More enterprises are spending millions of dollars on the technology, according to a new report by analyst firm Deloitte.
Specifically, Deloitte says that 53 percent of the enterprises it surveyed for its new “State of AI in the Enterprise” survey (PDF) spent more than $20 million over the past 12 months on A.I.-related technology and talent.
“Seventy-one percent of adopters expect to increase their investment in the next fiscal year, by an average of 26 percent,” the survey stated, adding that A.I. isn’t a cost-sink for some firms, either: “Seasoned adopters also typically achieve payback on their investments in a shorter amount of time, with 81 percent reporting their payback period is less than two years.”
How exactly is that “payback” achieved? Cost savings is a huge part of it; A.I. and machine learning allow companies to refine and accelerate processes, preserving cash. A.I. also “enhances” products and services, the definition of that varying wildly from company to company. For example, a drug company might leverage machine learning to rapidly sort through possibilities for a new drug, or analyze test results.
Although these early A.I. adopters may gain a competitive advantage, though, the increasing prevalence and commercialization of A.I. tools means the playing field may level out quicker than some executives and analysts expect: “As a result, companies that already have an AI-powered edge should continue differentiating themselves. Companies that haven’t yet adopted AI technologies should begin to accelerate efforts across their products, processes, and talent.”
Companies also fear that A.I. initiatives could fail—resulting in lots of burned budget—or that data might end up misused. Since this is still relatively early days for A.I. as an industry, there’s also a fair bit of regulatory uncertainty. It takes a diverse, effective team to successfully take advantage of everything A.I. tools have to offer while avoiding these risks, Deloitte concludes: “Include both technical and business experts in selecting AI technologies and suppliers. Having a broad perspective from developers, integrators, end users, and business owners can help ensure organizational alignment and a focus on business outcomes.”
Of course, this environment is rich with opportunities for technologists skilled in A.I.
The A.I. Opportunity
Different companies and industries demand different kinds of A.I. skills. For example, if you’re interested in autonomous driving, and you want to work for a firm such as Tesla or Waymo, you’ll need to demonstrate your knowledge of computer vision, 3D detection, and domain adaptation (if a recent Waymo open dataset challenge is any indication).
Meanwhile, an analysis of Apple’s job postings suggest that the company is hiring for machine learning and cloud-related jobs; it seems likely that anyone tasked with A.I work at the company will end up working on Siri, the voice-activated digital assistant. At a firm like Google, someone skilled in A.I. could participate in a number of initiatives, from search to image recognition.
But many positions that demand A.I. aren’t highly specialized, or designed to service a single project; roles such as data scientist and data analyst are increasingly asking for machine learning skills. Here’s a breakdown from Burning Glass of the percentage of tech job postings requesting such skills, by profession:
Employers on the hunt for A.I. talent may want you to have certifications that prove your knowledge. Fortunately, there are a rising number of such certifications, including ones in TensorFlow, AWS machine learning, and more. And even those employers who don’t request certifications will no doubt subject you to rigorous testing before offering you a role. Fortunately, if you’re new to A.I. and machine learning, there are a variety of “crash courses” and training videos that can quickly bring you up to speed on the fundamental principles.