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The AI Talent Paradigm Shift: Will Your Workforce Be Ready?

The AI Talent Paradigm Shift: Will Your Workforce Be Ready?

Preparing employees to work side-by-side with machines.

One Silicon Valley start-up is proving that when robots and humans work productively together, the result is great pizza. Alongside the human workers at Zume Pizza are pizza-making robots that handle the low-skill, repetitive, and dangerous tasks. Julia Collins, the company’s CEO, calls it “the perfect marriage of food and technology.”

Of course, it’s not just the pizza industry that is starting to feel the effect from smart machines.

People in every industry sector around the world are in the throes of a workplace paradigm shift that may be as profound as any that has come before. Artificial intelligence (AI) is helping to expand the limits of productivity through the development of smart machines that work side-by-side with their human counterparts.

Learn to love robots

“As we move into the age of artificial intelligence and machine learning, it’s not just important that you collaborate well with people, but also that you can collaborate and train and work with AI,” says Brian David Johnson, author and world-renowned futurist.

Already many work environments are being realigned as automation replaces or augments everything from manufacturing to accounting-related tasks. “Collecting and processing data are two categories of activities that increasingly can be done better and faster with machines,” says Abhijit Bhaduri, columnist and author of “The Digital Tsunami.”

It’s not just mundane tasks that AI is affecting. It’s creative ones, too. Adobe Sensei, for example, helps optimize the creative process with machine learning and analytics. “We’re just scratching the surface, both in the artistic possibilities and the efficiencies we can gain from artificial intelligence,” says Jon Brandt, senior principal scientist and director in Adobe Research. “We are here to invent creative AI and transform the world through AI and machine learning.”

While robots will continue to replace some human jobs, technology will free up human labor for value-added pursuits. “Work is not finite — there’s not a sum total of work,” says Brian. “If machines do all that work, we’ll still have something to do. We have augmented creativity. It will allow us to do work that we haven’t even realized needed to be done.”

Reskill your workforce

AI is a data-driven technology that is both powering autonomous machines and augmenting the flow of information and analysis for human workers. As machine learning evolves, workers must  learn to adjust in much the same way as adapting to a new tool or software service.

As this automated work environment becomes more ubiquitous, says Abhijit, companies need to invest in “reskilling” their workforce. According to a recent study by Accenture, 74 percent of companies expect to significantly automate workplace tasks over the next three years, yet just three percent plan to increase spending on training. As a result, Abhijit says, “Reskilling the workforce will be a challenge for individuals, as well as organizations.”

Adobe, for example, is investing in AI training for its workforce. We recently launched a six-month AI training and certification program for all of our engineers — a robust initiative meant to unleash the scientist in each engineer.

Adjusting to smarter machines

IT and software developers will also have to adjust to “smarter” machines. “Engineers are going to be more important than ever,” says Brian. “Machine intelligence is created, designed, and executed by engineers. It’s not a relationship with a human being, and engineers tend to have a deeper understanding of that than the average employee.”

Brian also thinks that a company’s technical personnel can be important assets when it comes to helping colleagues in other areas of the business understand what machine intelligence is, and how to work with it.

Eylon Stroh, senior computer scientist, machine learning and engineering at Adobe, agrees. “AI, and in particular machine learning, is more than just sitting down and engineering something,” he says. “It requires engineering skills and knowledge of the science behind these machine-learning models, as well as some experience with the art of how to manipulate these models to solve a particular problem.”

IT techs and coders need to be prepared for their roles to be re-defined by AI. “Engineers will have to solve business problems, not just write code,” says Abhijit. “That means they have to work in small cross-functional teams that include designers, anthropologists, and other specialists.”

Machine learning is forcing engineers to think more deeply about how to use data assets to solve customer problems, and it’s changing how code is written. “AI is taking software development out of the pure engineering realm and putting it more into a natural science realm,” says Scott Prevost, vice president of engineering, Sensei and Search at Adobe. “Now, you observe things in the real world, measure these things, and then tweak the data and the parameters to improve the performance.”

Preparing for the future

Workers at at all levels of the company need to become AI-savvy. “There are plenty of machine-learning problems that entry-level people can solve,” says David Parmenter, director of data and engineering at Adobe Document Cloud. “People today really want to work in this space, and it’s critical that the machine-learning capability of the organization is decentralized and distributed throughout the entire organization.”

To develop and support AI technology, workers need to understand it. For companies, that means ensuring that their employees know how AI works, and how it impacts both internal business operations and, ultimately, customer experiences.

“Get acquainted with these machine-learning concepts,” says Eylon. “Realize what’s going on, and try to build models on your own. Put the investment into learning in your spare time, if you have to, in order to get the requisite skills and an understanding of AI.”

Likewise, employers need to think about AI training in the same way they would if they were introducing a new piece of software for enterprise-wide use. Everyone in the organization needs to know how to use the new tool.

“Employers should invest in their workforce,” says Eylon. “If you value your employees as engineers, you will value them more as machine learning-savvy engineers. If a company invests in its workforce, it will be rewarded in the long-term.”

Technology has been one of the driving forces in human development. Each leap forward has required human workers to learn how to embrace the zeitgeist of their era. For workers in the age of AI, the challenge is to learn the skills necessary to help maximize the synergies between humans and our digital doppelgängers.

Read more about our future with artificial intelligence in our Human & Machine collection.

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