The future of workExecutives who are early adopters of AI and cognitive analytics technologies continue to be enthusiastic supporters. According to Deloitte’s second edition of the State of AI in the Enterprise, these trailblazers are reporting substantial returns on initial AI projects and planning more projects for the future.¹

AI and its ability to augment human workers is one of the most influential forces shaping the future of work. AI is nearing ubiquity in today’s economy, appearing in both the micro economies—the share of jobs requiring AI increased by 450 percent from 2013 to 2018²—and macro economies—industry experts predict that AI deployment will add $15.7 trillion to the global GDP by 2030.³ Humans with machines, data with actions, companies with their customers. The future of artificial intelligence is now.

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Access to talent is a major issue in this new age of humans with machines. The demand is especially intense for technical skills that don’t widely exist. Among the early adopters surveyed for the Deloitte's State of AI in the Enterprise study, lack of AI skills is a top-three concern for 31 percent.⁴

Clearly, organisations need to look at new ways of sourcing skills if they want to build future success on a foundation of analytics and AI technologies. Deloitte's 2019 Global Human Capital Trends report suggests several ways to tackle this, including finding "gig workers" in the alternative workforce and strategically leveraging technology, which can range from AI-driven chatbots to video interviews to targeted job advertising.⁵

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A few examples of future jobs in the future of artificial intelligence:⁶

• The cognitive work (re) designer. Redesign knowledge-based jobs and their underlying business processes to take advantage of technologies such as deep and machine learning, natural language processing and generation, and robotic process automation.
• The renaissance worker. Combine technical expertise in one or more domains with expertise in design, project management, or client and customer interaction. Bring background in science, technology, engineering, art and mathematics (STEAM), not just science, technology, engineering and mathematics (STEM), fueled by art courses that add creativity and communication skills.
• The digital twin engineer. Whether it’s a jet engine or a shop floor, create a virtual representation of both the physical elements and the dynamics of how an IoT-connected product operates and interacts with its environment and use machine learning to optimize product performance.

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While the very concept of a job—where it is done and by whom—is being redefined by such forces as the gig economy, proximity, and technology, there are fundamentals that contribute to a positive and fulfilling employee experience that remain constant. These include having meaningful work, a flexible work life balance, and opportunities to learn and perform activities that have an impact.

AI in the workplace has the potential to make jobs much more interesting for human workers by augmenting their tasks and freeing them to perform higher-level functions and make better-informed decisions. Artificial intelligence can spark new ways of working that blend the best of what machines do with what humans bring to this collaboration: experience, judgment, and empathy. In fact, 78 percent of executives surveyed believe that AI-based augmentation of workers will fuel new ways of working.

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This is a fundamental question that leaders in business and academia are grappling with, and we are only beginning to get a sense of what the enduring human skills are in this nascent phase of human and machine collaboration.

Imagine that you are responsible for operations at a large manufacturer and you want to maximize the life of equipment parts while avoiding unnecessary downtime. In the past, your maintenance team relied on "tribal knowledge" estimates or doing an ongoing, in-depth analysis of each piece of equipment. Today smart factories can use machine-to-machine and machine-to-human communication with cognitive technologies to make timely, accurate decisions. This predictive maintenance raises thought-provoking questions about the role of maintenance personnel versus the role of predictive algorithms, and what kind of traits a modern-day maintenance worker should possess.

Determining the immutable human skills needed in a workplace filled with AI solutions is hard, but necessary work. The need for human involvement is not going away, and the value of powerful, esoteric capabilities like imagination cannot be underestimated.

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