The short answer is no. However, there are numerous aspects of strategists' work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what's on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform.

How should you spend your time? For centuries, people have relied on their gut instincts to figure out the answers to these life-changing questions. Now, though, there is a better way. We are living through a data explosion, as vast amounts of information about all aspects of human behaviour have become more and more accessible. We can use this big data to help determine the best course to chart.

Highly overparametrized neural networks can display curiously strong generalization performance - a phenomenon that has recently garnered a wealth of theoretical and empirical research in order to better understand it. In contrast to most previous work, which typically considers the performance as a function of the model size, in this paper we empirically study the generalization performance as the size of the training set varies over multiple orders of magnitude. These systematic experiments lead to some interesting and potentially very useful observations; perhaps most notably that training on smaller subsets of the data can lead to more reliable model selection decisions whilst simultaneously enjoying smaller computational costs. Our experiments furthermore allow us to estimate Minimum Description Lengths for common datasets given modern neural network architectures, thereby paving the way for principled model selection taking into account Occams-razor.

In my last post, I wrote about Artificial Intelligence (AI). When I last wrote about AI, I focused on the technological side: what is a part of an AI system and what isn't. However, there is another question which might be more important; what are we doing with AI? Part of my job is to help investors looking at AI companies with their due diligence. I have discussions with them about companies they might want to invest in. Through this process, I have observed how every company pitch is full of content on how they are using AI to solve a business problem.

I have written on Artificial Intelligence (AI) before. Back then I focused on the technology side of it: what is part of an AI system and what isn't. But there is another question which might be even more important. What are we DOING with AI? Part of my job is to help investors with their due diligence. I discuss companies with them in which they might want to invest.

What does a truly data-driven business look like as a new age of artificial intelligence dawns--and how do organisations find the right balance among all the moving parts behind big decisions? Emerging technologies such as machine learning, natural language processing, and conversational agents can create giant leaps of efficiency, meaning, and insight hidden within businesses and the world at large – an enormous opportunity for leaders to make more informed and effective decisions. Seizing the opportunity will require leaders who can weigh the power and influence of both artificial and human intelligence, finding a balanced path that makes the most of each unique capability. In our latest survey we've captured a ground-level view across 2,100 C-suite leaders, business unit heads, and SVPs as they grapple with the biggest choices facing their companies. These leaders say they are sold on the power of data and analytics to deliver insight into key questions they need to answer.

Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data. It's time to welcome science into the C-suite, yielding analysis to algorithms, to find a new mix of mind and machine. PwC's Data and Analytics Survey 2016: Big Decisions TM shows that most executives say their next big decision will rely mostly on human judgment, minds more than machines. However, with the emergence of artificial intelligence, we see a great opportunity for executives to supplement their human judgment with data-driven insights to fundamentally change the way they make decisions.[1] "This is an inflection point," says Anand Rao, an innovation lead in PwC's data & analytics practice, speaking of the growing role of machine learning in the business world.

For insurers, artificial intelligence – as represented by cognitive computing and robotic process automation -- may be the single most disruptive emerging technology on the near horizon. Insurers, along with other businesses, will use machines capable of complex reasoning and interaction to enhance human capabilities. The potential gains in effectiveness and efficiency in areas ranging from underwriting to claims processing to risk management are enormous. A number of converging elements have led to the rise of artificial intelligence. For example, there have been vast increases in the volumes of available data, with an estimated 44 zettabytes of data by 2020.