By Holly Ransom, 2019

Encryption. Extremist social media content. Driverless cars. Blockchain. Drones. Facial recognition. Cybersecurity. AI. Fake news. The list of technology issues confronting our society is mind-boggling, and only growing in number and complexity by the day.

We’re navigating a new world order… and it requires a skilful balancing act.

On one hand, as almost goes without saying, the impact of technology is staggering, both in terms of growth momentum and potential upside; business spending on AI alone jumped 54% in 2018 and by 2030 could bump up the global GDP by $15.7 trillion, representing a 14% increase. In many ways, AI is becoming the next space race, with countries around the world announcing their strategies for global dominance. In 2017, China launched its New Generation Artificial Intelligence Development Plan, with a catalytic $30B investment, a strategy aimed at claiming global leadership in AI by 2030. When I interviewed the United Arab Emirate’s AI Minister (which is a world-first portfolio), Omar Sultan Al Olama last year, he was fresh from announcing a 50-year AI strategy for the UAE that by 2030 alone is predicted to contribute $96 billion to the UAE economy (12.6% of their GDP). Leaders in nation’s making assertive AI plays will tell you that economic upside is not only worth playing for, in the face of automation and predicted wide-spread job loss, but some kind of coordinated AI strategy and investment is also a compulsory entry ticket to the 21st century’s main game.

And for all the fearmongering surrounding what the rise of technology means for people and jobs (‘Terminator’-style visions of a robot uprising, anyone?), which I’ll touch on in a moment, there’s a strong case for optimism: tech capability + human capability (a combination often referred to as augmentation) provides us with the opportunity to be faster, smarter and better problem-solvers.

An app a day keeps the doctor away

An example of this new problem-solving capability is healthcare AI, expected to receive a stunning $6.6 billion in total public and private sector investment by 2021. As our population grows and ages, AI will likely be an essential component of how we meet the challenge of the projected ballooning of our healthcare costs. It’s predicted that top AI applications may result in annual savings of $150 billion by 2026. But it’s not just about cost saving, it’s about effectiveness and reach: AI can already diagnose skin cancer more accurately than humans, provide alternative service to the 20% of clinical demands that currently go unmet (because our doctors, nurses and healthcare professionals are maxed out!) and offers a means of providing medical care and health support services to some of our most remote communities.

Automation of a nation?

However, it’s not all sunshine and rainbows. History has shown us with past industrial revolutions, that we will navigate a period of significant disruption as this next wave of innovation pervades every aspect of our lives. The challenge of workforce automation is real and significant. Current forecasts suggest 50% of jobs will be obsolete over the course of the next decade or so (the timeline shifts depending on which forecast you read) and this will be one of the greatest large-scale economic challenges our world has faced. Governments are grappling with how to train (and retrain) their populations and each of us is crossing our (yet to be automated) fingers that we’ll be in that 50% whose jobs aren’t lost. This is a significant challenge that shouldn’t be underplayed, particularly given the way the ‘digital divide’ (a term used to reference the wide variation in digital skill levels) is likely to play out socioeconomically. However, it’s worth noting that this isn’t a zero sum game- new jobs will be created in this next wave of disruption, so to paint it (as often reported) as a net ‘loss of jobs’ glosses over some of the complexity of the changing labour force dynamic. In the Harnessing Revolution report, Accenture argued that fewer jobs will be lost if people are able to reallocate their skills to tasks that require more “human skills” such as complex analysis and social/emotional intelligence. They went as far as to say that if this strategy were properly implemented, the UK would be able to reduce the share of jobs at risk of being fully automated to less than six percent, Germany to ten percent and the US to four percent by the year 2035. So, in essence, if we get smart about how we upskill and reskill, we have the ability to make our economic transition smoother.

Governance – all on board

As Dr Melvin Kransberg, a Technology History Professor at Georgia Institute of Technology, said in a 1985 address:

“Technology is neither good nor bad; nor is it neutral.”

What Kranzberg identifies is that the way technology interacts with the social ecology will frequently have environmental, social, and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves. Further, that same tech can have radically different results and consequences when introduced into different contexts or under different circumstances.

This is the reality we’ve increasingly been awakening to in the last 18 months as the Facebook/Cambridge Analytica matter snowballed into a much broader conversation about the ethical frameworks governing how the big tech companies operate and how they should be governed. Facebook CEO Mark Zuckerberg originally called for the government to take a hands-off approach, before making an about-turn to say that there was a need for government to step in and provide direction. Last year, It’s also important to acknowledge that these aren’t issues on the horizon, we’re already experiencing the effects, to point to just one data point:

In Australia alone, the annual direct cost associated with cybersecurity incidents to Australian businesses is $29 billion per annum, the equivalent of almost 2% (1.9%) of Australia’s GDP.

And that is just the tip of the digital, physical and political security iceberg we need to be considering as AI rapidly evolves the risk landscape for individuals, organisations and nations. Wherever you sit on the spectrum between optimist and pessimist, the (STEAM) train has left the station and it is a false binary to think our choice is whether or not we get onboard… that’s like suggesting there’s a choice between opportunity and obsolescence. The real question is whether we choose to step into the driver’s seat and take control, and how we choose to navigate change.