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Work Automation

March 15, 2017

This is the first of three blogs leading up to a forum on the future of work in health. IN this first blog, David Smith, Chief Executive of Global Futures and Foresight, looks at the wider context of automation in the workplace and its impact

DS blog

The rate of change is such that by 2027, 75 percent of the companies in the S&P 500 are predicted to be companies that are not in the index today. Adapting to the underlying economic, technological and social changes that are reshaping the world of work will be critical for incumbents wishing to survive and thrive.

One major area of change obviously lies with technology; not so much with the technology per se but in what it enables organisations to do. Many of the technologies underpinning digital transformation are relatively prosaic yet nine in ten organisations in 2016 still reported the implementation of digital transformation as a significant challenge, with 70 percent of these citing internal complexity as an inhibiting factor . In addition, a wide range of ostensibly differing industries and professions possess a common set of characteristics that are open to codification and thus automation. How such organisations can successfully merge artificial intelligence (AI) and humans to work together remains an infinitely bigger task than the digital changes made thus far, and one that few seem prepared for.

The organisational and leadership implications of artificial intelligence are enormous. Jobs, processes, the relationship with consumers, patients, citizens and organisational models will all be reworked by artificial intelligence. Indeed, PwC says 64 percent of CEOs believe that robotics will bring new innovations to their business models. Crafting models that enable a given organisation to frame these challenges as opportunities – and then to capitalise on them – will be a huge task, requiring multidisciplinary thought and genuine innovation.

Such changes may in fact be necessary for the organisation to evolve at the same rate as its wider environment. The rate at which medical knowledge is being generated is estimated to be doubling every 18 months. This gives practitioners little chance to stay abreast of all developments, even within their field of expertise. Even medical students stand little chance in the current educational set-up, since the time from acceptance in a program to graduation is likely to see a doubling of medical knowledge twice over. The ability of algorithms to help deliver the most relevant details and updates to doctors (as well as for students), on-demand and on a case-by-case basis holds great potential.

There is also the possibility, that, along with other trends, emerging AI will open new possibilities in future medical business and organisational models. AI capability is advancing at such a rate that as many as 47 percent of jobs (or at least the tasks within them) will be highly susceptible to automation over the next two decades. In 2016, an autonomous robot surgeon bested human doctors in stitching up pig intestine. In addition, haptically enabled robots, able to touch and feel, have enabled remote diagnosis and abdominal ultrasound imaging. Alone these developments are not enough to constitute an immediate threat to the current model. It is, however, worth recounting that in their seminal study, Osbourne and Frey suggested surgeons and physicians were amongst the least likely of all professions to be automated, at 0.42 percent. This clearly suggests that even within those jobs unlikely to be fully automated, automation is likely to feature alongside the human worker.

Automation is likely to contribute to a quickening ‘skills turnover’ in the coming years. A Deloitte survey found that 75 percent of executives believe that automation will require new skills over the next several years. The ability for the organisation at large to change successfully is questionable however, much as it is with the related digital transformation process. Only 13 percent of executives believe their organisation’s capabilities to redesign work done by computers to complement talent are excellent. By contrast, 34 percent see them as weak. The friction, between what is needed and what is organisationally possible will account for much turbulence.

As a result, ‘…in the Next Economy, companies (will) use technology to augment workers, not just to replace them, so that they can do things that were previously impossible,’ says Tim O’Reilly. Given that flatter and even decentralized work structures are better equipped to cope with ambiguity, speed and evolving digital norms, it is quite likely that much work will occur within team units. Bain predicts that ‘…by 2027, most work will be project-based, with agile teams (internal and external) the dominant unit.’ Designing for this will be key, with GE Healthcare’s Chief Patent Officer Greg Petroff, noting that design thinking should be used ‘…to have multidisciplinary teams frame the problem space more accurately. It’s a great process for stakeholder alignment,’ he suggests. Healthcare is set for radical change, and carefully planned and implemented AI solutions may be key to a successful transformation.

In the next two blogs we will look at the social, technological and environmental changes that will drive change in health care; and then look at specific potential impacts on the work that professionals and others do in health and social care.

Written by David Smith, Chief Executive, Global Futures and Foresight.

The views expressed are those of the author and not necessarily of SAMI Consulting.

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