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If you’re watching the impact of artificial intelligence on the IT organization, your interest probably starts with your own job. Can robots do what you do? But more importantly, you want to skate where the puck is headed. What emerging IT roles will AI create? We talked to AI and IT career experts to get a look at some emerging roles that will be valuable in the age of AI.
[ Check out our related article, 12 emerging IT job titles with a bright future. ]
Alex Jaimes, head of R&D at DigitalOcean, notes that today, AI and machine learning expertise is typically the domain of people with PhDs. “Rising demand may open the door for differing types of experts,” Jaimes says. “We’ll continue to see the scientist – PhDs in fields such as computer science and electrical engineering – with deep technical expertise and experience in [AI and] machine learning, but we’ll also see more practitioners, [who] quickly learn how to use the technology to capitalize on the growing number of jobs, but do not necessarily have a deep understanding of how it works.”
The rise of the “practitioner” is one of many factors that will create new jobs, even as old ones disappear.
“Although AI will lead to the automation of certain jobs, it will also create many new job opportunities, especially in IT,” says Akash Ganapathi, co-founder and CEO of Trill A.I. Ganapathi expects a growing enterprise focus on AI and machine learning to generate new roles in areas including:
And that’s just a starting point. Here are some other AI-related job titles and roles experts expect to crop up in the future.
“I envision Intelligence Designers as AI professionals in charge of strategic choices about how, when, and where develop artificial intelligence components in very large, complex IT systems,” says Alessandro Perilli, GM, Management Strategy, Red Hat.
He envisions this position as a descendant of today’s data scientist role, but with a key difference.
“In my mind, today’s data scientist has a more specialized focus, looking to turn normal applications into smart applications. And in some cases, it will be all a company needs. But eventually, as AI becomes more pervasive across the application portfolio and more elements of the IT environment could be correlated in a meaningful way, there will be the need for somebody who has the big picture and sees all localized intelligences getting together as a single corporate brain, if you will.
“The analogy that comes to mind, of course, is the evolution of the human brain. It’s very exciting. We are in the early, early phases of artificial intelligence and we are still thinking about isolated smart applications, like neurons in charge of a single specific aspect of the brain. But the potential for these neurons to be integrated into a complex neocortex is enormous. And we’ll need an Intelligence Designer for that.”
“Although AI will handle many of the routine IT decisions made by humans today, it is much more dependent on data that has been organized, cleansed, and tagged with semantic meaning,” says Doug Bordonaro, chief data evangelist, ThoughtSpot. “Today, analysts and data scientists share this function, but these positions are primarily responsible for producing insights and answers. As AI increasingly takes over the insight part of the equation, we’ll see a new role of data curator rise in importance, focused specifically on preparing data for use by AI algorithms across the organization.”
“AI has the promise to lower the bar for both access to data and ease of interaction, but it’s not going to be some silver bullet where all of a sudden everyone is using data to inform every decision,” Bordonaro says. “Even after adopting AI applications, businesses will need to teach their organizations about what data is available, how it can be applied, and how it should be applied.”
"That’s why internal data evangelism will become critical to the adoption and growth of AI solutions. To bridge this gap, companies will invest in data evangelist roles, specifically focused on working across the organization to educate users about available solutions, the value of data-driven decision making, and how to change traditional workflows to take advantage of the new capability.”
“While not a new title in itself, in order to leverage the full potential of machine learning in a big data environments, enterprises must hire a dedicated ML Data Scientist to implement and properly train the system, then provide data analysis to add value to the information collected,” says Todd Loeppke, lead CTO architect at Sungard Availability Services.
“[This] is a business analyst type role where you perform process assessments and identify areas for automation using robotics platforms,” says Felix Fermin, manager, recruitment at Mondo.
Duane Forrester, VP of Industry Insights at Yext, points out that virtual assistants and other “intelligent” services today – think Alexa, Siri, Google Home, and so forth – are already changing how consumers discover and choose businesses. Those businesses will need to invest deeply in how they manage the information available about them in the “intelligent ecosystem.”
“The age of structured data needs professionals to provide context for maps, info cards and specific answers, and digital knowledge. Companies are increasingly appointing a Digital Knowledge Manager to be the cross-functional leader responsible for the strategy behind a company's key digital knowledge, the cornerstone of a company's success in the coming years,” Forrester says. “From ensuring online data is accurate, to coupling internal projects and extending the value of content, product and contextual investments, these DKMs will be at the forefront of guiding a company's digital focus in the future.”
There will be a growing need for IT and design pros who can make AI interfaces usable for mass audiences, says Mondo's Fremin. This role will “create the personality of artificial intelligence agents with the goal to make them as human-like as possible.”
Sean MacPhedran, director of future platforms at Smith Labs, expects this to become an increasingly trendy role as more companies begin building natural language processing functions into their customer interactions. It’s a great example of a role that transcends traditional organizational silos: It’s a mix of technology, marketing, customer service, and other disciplines. Here’s how MacPhedran defines the job:
A technically minded creative writer who can:
“A technologist is often less focused on customer experience, and a writer is often less focused on practical limitations of NLP,” MacPhedran says. “The two meet in the role of cognitive copywriter, someone at home with logic trees, intents, data passes, brand voice, and the nuances of customer behavior.”
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