Designing your organisation for the machine age

Artificial Intelligence (AI), Machine Learning and the future of work; all topics gaining a fair share of attention and hype. A review of the latest articles, blogs and media suggest jobs will continue to be lost and re-designed in line with advancements in machine intelligence[1].

What is gaining less attention, however, is that new jobs and new teams are emerging in line with new opportunities stemming from advancements in machine intelligence. In his book, Deep Thinking, chess grandmaster Garry Kasparov describes it best saying, “many jobs will continue to be lost to intelligent automation. But if you’re looking for a field that will be booming for many years, get into human-machine collaboration and process architecture and design[2].”

Material improvements in computational power and applications, together with increased access to Big Data, means organisations are in a better position than ever to explore and leverage the benefits associated with AI and Machine Learning.

Key to leveraging the benefits of AI and Machine Learning will be the design of new jobs and the sourcing of new talent.

The application of AI and Machine Learning is changing

Traditionally, AI and Machine Learning technologies and solutions were focused on providing leaders with enhanced insight and decision making capability. A few years ago, this broadened into the use of AI and Machine Learning technology for automation – using machines to execute repeatable, manual tasks through training and a clear set of instructions.

Decision making support required more Data Analysts, Data Scientists and Data Engineers. The rise of automation required more Software Engineers, User Experience Designers and Process Specialists.

The rise of AI and Machine Learning products and solutions, such as Apple’s HomePod and the Google Home, cognitive chat agents and customer service robots is driving the creation of a third wave of new jobs, including Machine Learning Engineers and AI or Interaction Designers.

While most large organisations already have Software Engineers, Data Scientists and Platform and Solution Architects, consistent skills gaps are emerging in the design of human to machine interactions as well as the scaling of a Machine Learning algorithm to be deployed in a robust production environment.

Talent as a competitive advantage

In order to capture the competitive advantage associated with AI and Machine Learning, organisations must invest in the design of new roles in line with their AI strategic objectives. Once designed, the next challenge becomes sourcing the talent required to fill these new jobs.

While Machine Learning Engineer is the fastest growing job in the US market[3] there are supply issues, with many universities still designing and implementing courses to educate students for the technology roles currently in demand, rather than the emerging roles or even those yet to be created. And while the concept of human-centered design has been around for a long time, the design of AI products and solutions requires new skills and mindsets related to building dialogue maps, defining intent and inserting personality to make them feel natural and human-like.

To overcome this challenge, organisations will need to identify and develop internal talent to execute these roles.

For example, Software Engineers with the cognitive capacity required to understand Machine Learning models and algorithms may be re-trained to become Machine Learning Engineers. Alternatively, Data Scientists who already understand the statistics and mathematics and are interested in becoming more sophisticated in how they write code at scale may also move into the Machine Learning Engineer role. In addition to this, traditional UX/UI designers can be retrained to design natural, empathetic and realistic conversation and interaction flows for human to machine interactions.

At the end of the day, the machine age is upon us and one can only expect that while jobs may continue to be lost or re-designed, new jobs in fields we had not previously imagined will continue to emerge[4].

For more insights on how organisations are preparing for the human capital challenges of the future, visit the Human Capital pages of the Deloitte website and pre-register for a copy of our Global Human Capital Trends 2018 report coming in April.

1 Jim Guszcza, “Smarter together: Why artificial intelligence needs human-centered design”, Deloitte Review, issue 22, Jan 2018.
2 Garry Kasparov, “Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins”, May 2017.
3 Economic Graph Team, “LinkedIn’s 2017 U.S. Emerging Jobs Report” LinkedIn, Dec 2017.
4 Tom Liapsis, “Amplified Intelligence through Automation”, Deloitte, Nov 2017.

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