At the recent Salesforce confab in New York, AI was top of mind, especially in regards to the continuing enhancement of the company’s Einstein analytics capability. However, with this bunch, the preferred term for AI was augmented intelligence — versus artificial intelligence.
This distinction makes a lot of sense, because, contrary to the fears that AI and its related technologies will be taking over the work of the world, it likely means something very different is unfolding. That is, AI is expanding and amplifying human capabilities.
What does this mean for jobs in the months and years ahead? Likely, two things. Business professionals will see new avenues of innovation opening up, while technology professionals will see new fields of endeavor as demand for AI-related skills grows.
A recent analysis of job demand by LinkedIn finds machine learning engineer leading the list of skills in demand. It’s fair to say that other jobs on this list — including data scientist (#2), sales development manager (#3), and customer success managers (#4) are also occupations that will flourish as a result of access to AI platforms and insights. (My colleague Louis Columbus provides more details on the LinkedIn jobs report.)
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Gartner sees a great deal of opportunity in AI, predicting that within the next two to three years, AI “will create more jobs than it eliminates.” The consultancy even pegs some actual numbers to its prediction: 2.3 million jobs created by 2020, versus 1.8 million lost. The industries that will see the most gains are healthcare, the public sector and education, while manufacturing will see some job losses due to AI.
The job gains will be in new positions of the “highly skilled, management and even the entry-level and low-skilled variety,” Sicular predicts. She is also an advocate for human and machine interaction, which will be the core of this potential job growth. Gartner predicts such augmentation will generate $2.9 trillion in business value and recover 6.2 billion hours of worker productivity. “Focus on augmenting people with AI,” Sicular advises. “Enrich people’s jobs, reimagine old tasks and create new industries. Transform your culture to make it rapidly adaptable to AI-related opportunities or threats.”
In addition, the benefits of AI will increasingly be seen as it moves up from repeatable, rote tasks to less-routine work, Gartner predicts. Within the next four to five years, one in five “workers engaged in mostly nonroutine tasks will rely on AI to do a job.” AI applied to nonroutine work “is more likely to assist humans than replace them as combinations of humans and machines will perform more effectively than either human experts or AI-driven machines working alone will.”
We will see many jobs, then, elevated and enhanced with AI (or more broadly, cognitive) capabilities providing greater insights, automation and predictive powers.
There will also be new kinds of job titles arising. In a study from earlier this year published in MIT Sloan Management Review, H. James Wilson, Paul R. Daugherty, and Nicola Morini-Bianzino, all with Accenture, predicts the rise of three emerging categories of job roles associated with AI development: “trainers,” “explainers,” and “sustainers.” Just to be clear, a trainer in an AI-driven organization will be focused on training AI systems — not humans — to the ways and whims of business processes. They provide examples of jobs for each category:
- Customer-language tone and meaning trainer: This professional “teaches AI systems to look beyond the literal meaning of a communication by, for example, detecting sarcasm,” Wilson and his co-authors state.
- Context designer: “Designs smart decisions based on business context, process task, and individual, professional, and cultural factors.”
- Automation ethicist: “Evaluates the noneconomic impact of smart machines, both the upside and downside.”
- Automation economist: “Evaluates the cost of poor machine performance.”
Along with the Accenture team’s potential job descriptions, a perusal of the LinkedIn job board for AI reveals a range of non-traditional job titles that now actually exist. Imagine seeing titles as these just a few years ago:
- Voice of the Customer (VoC) analyst: “Responsible for identifying, synthesizing and communicating insights from an ever-growing set of passive and active listening mechanisms. Conduct deep-dive analyses to address topical business questions as they arise.”
- Imagery analyst: “Develop training data for the next generation of artificial intelligence systems. Analyze images and label objects according to pre-defined descriptions.”
- Interactive chatbot designer: “Responsible for writing interactive scripts for a chatbot specific to a high-level-consumer product launch. Create an engaging and user-centric interactive narrative experience that leverages the AI system’s logical architecture, as well as being a part of the team that is responsible for the development of the chatbot’s personality and voice/tone.”
Such jobs are only the tip of the iceberg, and focus on ensuring that cognitive systems are, as Wilson and his co-authors describe, “fair, transparent, and auditable.” Many existing jobs with more traditional titles, such as sales managers and physicians, will also be greatly enhanced by the ability to look at trends and patterns, and apply past learnings, to move forward.