Artificial Intelligence and why it matters
A significant amount of hype has for some time surrounded AI, but promises first made more than sixty years ago are now finally being delivered. So what has been the major game changer responsible for putting AI back on the map and on the verge of changing, well, just about everything? The answer is deep learning, another old idea that found an opportunity to mature in the late 1990s and early 2000s.
Based on the notion of learning tasks using artificial neural networks inspired by the biological nervous system, deep learning technology is highly advanced and requires vast volumes of data and a level of computing power only recently made possible. By 2030, AI is estimated to contribute as much as $15 trillion to the world economy, making it the biggest commercial opportunity in today’s fast changing economy. Indeed, the new realities of the post-COVID-19 world require the accelerated adoption of AI in order to deliver the efficiencies and augmentations of a highly digitalized workplace.
For more than 250 years, the fundamental drivers of economic growth have been technological innovations, the most important being general-purpose technologies such as electricity and the steam engine. And now it is AI that stands out as the transformational technology of our digital age which, as with previous GPTs (“General Purpose Technologies”), is expected to trigger waves of complementary innovations and opportunities.
What tangible opportunities does AI offer businesses right now and how do they capture this value? We are currently witnessing the first wave of automation impacting work, usually as a result of companies automating tasks and processes, reducing costs and creating more efficiencies. The work dividends from this first wave of automation are mostly positive. Low level, tedious, hazardous and boring tasks are taken over by machines freeing up time for the humans to do the “higher level”, more productive tasks.
Significant shifts in computing power and availability of large-scale data advance the development of AI applications that continue to rapidly grow in complexity and autonomy. In contrast to automated technology that is controlled by a programmer, AI and deep learning are equipped with capabilities of self-improvement. The uniqueness of AI’s autonomous nature and the way it is trained on data - essentially learning from the mistakes made in the past - makes the technology both an opportunity and a risk.
Ultimately, the value of AI is not to be found in the deep learning techniques themselves, but in a company’s ability to harness them. The real challenge will be in extracting the correct data in the correct format so that it can be effectively run through the algorithms to produce beneficial results.
AI at work
As organisations deploy technologies that automate work or introduce machine intelligence in the organisation, the limiting factor in translating these innovations into real business benefits will be talent. Beyond the designers, developers and data scientists that everyone is battling for today, companies will need to explore what new roles are likely to emerge in digital disruptors.
As with many professions, underwriters have been doing a job one way for decades and now are expected to do things differently. The role is primed for transformation as AI is poised to reconfigure and augment insurance underwriting. Fuelled by an explosion of data, low cost data storage and open source technology, AI has the potential to help underwriters analyse an incredible amount of information, find red flags and help make more accurate underwriting decisions.
While there is no expectation for human underwriters to be replaced, as their judgement will still be needed for complex cases, future underwriters will be expected to work alongside AI systems to ensure all risks are accurately measured and priced. As underwriters increasingly interact with automated AI systems, there will be a need for new skill sets to develop, with some old skills potentially becoming obsolete.
Meanwhile, demand for these new skills far outstrips supply at present, which indicates that the main roadblock to insurers capturing the full value of this new technology is not the science, but the human change management factor. It is a tall order, but starting by having the right people with the right skills in the right roles will far outweigh picking the right technology, algorithm, or latest start-up to work with.
More digital, more human
One of the major transformations of the digital age is to see more companies adopting a flat working structure or platform, where career paths are less clear and the turnaround of young talent greater. In this new environment, a next-generation operating model that supports the opportunity to learn new skills, mentoring by thought leaders, and involves new staff in meaningful projects will be critical in order to attract and retain the best digital talent.
By moving beyond a one-size-fits-all approach to human resources and talent management, digital workforce platforms can help create the conditions in which employees feel energised by their work, valued by their organisation, and happy in their environment.
Google and Apple are two examples of early adopters of digital workforce platforms that built ecosystems allowing them to innovate, take advantage of new technologies to cut costs, improve quality, build value and respond quickly to the fast changing and rising digital expectations of consumers. How can this model be replicated across other industries?
The answer may depend on the ability of corporate leaders to restabilise the workforce — and to reconceive organisational structures — by using the very same digital technologies that have destabilised it in the first place. The incoming AI revolution should reinforce, not weaken, the uniquely human characteristics that define how we work, particularly in the way that we collaborate, communicate and develop relationships. In order to fully exploit emerging digital capabilities, most organisations will continue to depend on people, with human skills actually becoming more critical in the digital world, not less.
As tasks are automated, they tend to become commoditised; a “cutting edge” technology such as smartphone submission of insurance claims quickly becomes almost ubiquitous. In many contexts, therefore, competitive advantage is likely to depend even more on human capacity, on providing thoughtful advice to an investor saving for retirement or calm guidance to an insurance customer after an accident.
AI is likely to be one of the biggest game changers in insurance history, offering a wide range of opportunities from faster and more efficient claims management to a greater variety of on-demand insurance services. As organisations transform to thrive in a digital environment, their success will be affected by how well they integrate their workforce into the transformation journey and manage the tension between the constant drive to innovate and improve and the new governance, compliance and regulatory risks created by new AI technologies. Digital transformation requires the overhaul of culture beyond technology updates or process redesign in order to reap the anticipated benefits.
