In virtually every industry, the Fourth Industrial Revolution (4IR) has spurred a transformative journey that is redefining the very nature of work. While technology has played an essential and often defining role, people have nonetheless remained at the core of these revolutionary transformations. While the type of work varies across different industries and functions, 4IR transformation shifts the workforce away from highly manual tasks to a much more data-driven and automated future. Repetitive, manual factory-floor duties have been replaced with higher-level tasks that involve making data-driven decisions in collaboration with automated technology, including robotics and cobotics (or collaborative robotics). Building those new skills is the greatest business challenge for 80 percent of CEOs, according to data from the Harvard Business Review. 1 1.

The global life insurance industry has seen significant changes over the past decade. Developing economies--predominantly emerging markets in Asia that were formerly small contributors--have become global growth drivers and now account for more than half of global premium growth (Exhibit 1) and 84 percent of individual annuities growth (Exhibit 2). The availability of data has skyrocketed, and insurers have made progress in advanced analytics and artificial intelligence. Digital and mobile advances have raised the bar on transparency and service quality: customers can now file claims and access agents, insurance quotes, and policy information with a few taps on a screen. The past decade has also introduced new challenges. Life insurers have not benefitted from the bull market (Exhibit 3). Global penetration fell to 3 percent, and premium growth within most developed markets, hovering just below 2 percent per year, struggled to match GDP.

The rise of artificial intelligence (AI) is one of the defining business opportunities for leaders today. Closely associated with it: the challenge of creating an organization that can rise to that opportunity and exploit the potential of AI at scale. Meeting this challenge requires organizations to prepare their leaders, business staff, analytics teams, and end users to work and think in new ways--not only by helping these cohorts understand how to tap into AI effectively, but also by teaching them to embrace data exploration, agile development, and interdisciplinary teamwork. Often, companies use an ad hoc approach to their talent-building efforts. They hire new workers equipped with these skills in spurts and rely on online-learning platforms, universities, and executive-level programs to train existing employees.

Governments around the world are under pressure to operate more efficiently, serve citizens better, and provide more satisfying working environments for their employees. Lessons from the private sector show automation at scale has the potential to serve those purposes, but to get there governments must become more strategic in their approach, embrace new technologies, and be prepared to act at scale. Process automation and technologies based on artificial intelligence can bring benefits across numerous functions of government, including much lower operating costs, more efficient processes, and less wastage and errors. McKinsey estimates that as many as four out of five processes in HR, finance, and application processing are at least partially automatable, with the potential to reduce costs by at least 30 percent. The benefits of automation can be achieved relatively quickly.

To help their organizations capitalize on artificial intelligence and analytics, CAOs must do more than demonstrate their technical chops. They need to lead like a Catalyst. The role of the chief analytics officer (CAO) is being thrust into the spotlight as artificial intelligence (AI) technology continues to improve--and prove its value. AI and other advanced analytics will unlock $9.5 trillion to $15.4 trillion annually, with recent AI advances such as deep learning alone making up nearly 40 percent of the total. Given the enormity of the stakes, it's no surprise that CEOs are asking their CAOs (or those assuming CAO duties under a different title) to deploy and scale AI and advanced analytics--stat. Yet while the opportunity is great, so too is the challenge.