Insurers have infrequent interactions with their customers and thus have limited data on which to base their underwriting, pricing, and claims decisions. More-AI-oriented industry players, such as insurtechs, have gotten around this by putting digital and data at the core of their business models. These startups, run by digital natives, have had early success in some markets, such as auto and home insurance. The amount of money flowing their way suggests that they have the confidence of investors, who clearly believe in AI's ability to disrupt the industry and capture a fair share of its profit pools. Insurers hail from a tradition steeped in data of a different sort: historical and static data that comes from claims databases and underwriting questionnaires.

Few topics in the recruitment sector have proved to be more controversial than the use of artificial intelligence (AI). On one hand, everywhere you turn there are endless statistics about how AI is infiltrating business processes. According to LinkedIn, an overwhelming majority of recruiters and hiring managers agree that AI accelerates their ability to source and screen candidates. What's more, 70 per cent of recruiters believe their current process would be more effective if it were more data-driven and used AI. However, on the other side of the fence, research commissioned by the Royal Society of Arts suggests candidates don't agree, with 60 per cent of the public stating they're opposed to the use of automated decision-making in recruitment.

You wake up; your wristwatch has recorded how long you've slept, and monitored your heartbeat and breathing. You drive to work; car sensors track your speed and braking. You pick up some breakfast on your way, paying electronically; the transaction and the calorie content of your meal are recorded. Then you have a car accident. You phone your insurance company.

Companies and entire industries are looking to harness data analytics to make more accurate and effective decisions, within and across organizations. Such real-time and accurate insights have enabled boards and their management to be more effective in conducting their duties. Artificial intelligence (AI) mimics the learning function of the human brain, which means it could be deliberately or accidently corrupted and even adopt human biases, potentially resulting in mistakes and unethical decisions. Control of AI systems by the wrong hands is also a concern. Any AI system failure could have profound ramifications on security, decision-making and credibility, and may lead to costly litigation, reputational damage, regulatory scrutiny, and reduced stakeholder trust and profitability.

You wake up; your wristwatch has recorded how long you've slept, and monitored your heartbeat and breathing. You drive to work; car sensors track your speed and braking. You pick up some breakfast on your way, paying electronically; the transaction and the calorie content of your meal are recorded. Then you have a car accident. You phone your insurance company.

Predicting the future is risky business. You never really know if you are going to get it right. While experts may not agree on exactly how work will change in the next decades, there is growing consensus that "we find ourselves at the edge of another industrial revolution," according to Professor Sabine Kunst, president of the Humboldt University, Berlin. "Advances in artificial intelligence, the Internet of Things, and Big Data are already profoundly shifting all aspects of society -- how we work, connect, organize politically, and learn as human beings," she continues. What to anticipate, how to manage these changes, and how to ensure humans do not get left behind is what business leaders, researchers, academics, policy makers, and innovators met to discuss at the recent SAP research round table on the Future of Work at the SAP Innovation Center in Potsdam, Germany.

This transcript has been automatically generated and may not be 100% accurate.

Every New Year brings with it a host of predictions of how our world will change in the next 12 months. For 34 staff at Japanese life insurer Fukoki Mutual Life, recent forecasts that 2017 would be a big year for artificial intelligence (AI) doubtless now seem particularly prescient. Replaced by a Watson-based AI solution for the payment of claims, its hard not to think these unfortunate employees will prove to be the canaries in the mine for many more financial services workers with replicable skills. Whether hysteria or truth, there is a palpable sense that AI is gaining momentum right now. So too is the conception that technology is moving from enabler to destroyer of jobs, livelihoods, even society itself.