Azeem Azhar: Ultimately, we're living beings who've lived in a world that hasn't moved at exponential rates, and so we get caught out by the speed with which these technologies improve. Annie Veillet: Is it too late to start, and to start putting in the right frameworks and controls? Azeem: Society was really disengaged. It looked at technology as manna from heaven that bright and brilliant people produced as gifts from the gods--and far be it for us to ever ask a critical question of it. And we need to stop doing that, right? We need to be there and ask those questions. Lizzie O'Leary: From PwC's management publication strategy and business, this is Take on Tomorrow, the podcast that brings together experts from around the globe to figure out what business could and should be doing to tackle some of the biggest issues we face. Developments such as AI are changing the way we live. But what happens when those changes happen too quickly for business to deal with?

Successful integration and scaling of exponential technologies such as artificial intelligence (AI) and automation is just as much about people as it is the technology, argues Rickard Wieselfors, Head of Automation and AI at Ericsson. According to a recent student commissioned by Ericsson, 99 percent of the 2,000 senior leaders interviewed from different organisations said they faced challenges integrating AI and automation initiatives. Moreover, 87 percent of those respondents faced greater challenges relating to people and culture over technology or organisational challenges. Wieselfors says there are many challenges and lessons to learn when integrating new technologies, regardless of the size, age, or industry of an enterprise. In this new Euronews Next SAP vodcast episode, Marika Auramo, Chief Operating Office of SAP (EMEA North) and Jesper Schleimann, Chief Strategy and Information Officer (EMEA North) sat down with Wieselfors to learn more about what it takes to integrate and scale technologies successfully, both from a technical and people perspective.

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A recent article published in the Guardian caught the attention of internet users worldwide. Unlike ordinary works of journalism that go viral, however, this particular piece was not written by a human. In a style that is evocative and attention-grabbing, The Guardian aptly titled it: "A robot wrote this entire article. Are you scared yet, human?" The "robot" in question is GPT-3, or "Generative Pre-Trained Transformative 3", OpenAI's third iteration of an autoregressive language model that uses deep learning to produce human-like text.

Columbia University professor and robotics engineer Hod Lipson knows the importance of artificial intelligence (AI) on a global level. "It permeates everything we do, from the stock market, from predicting the weather to what product you're going to buy," he said Wednesday during the second day of the virtual Ai4 2020 conference. AI falls into the category of an exponential technology, meaning it accelerates with time. Both biopharma and med-tech companies are increasingly pulling the technology into their business operations, working on programs that can assist in everything from drug discovery and clinical trial recruitment to precision diagnostics and patient compliance efforts. Computing power has doubled every 20 months or so for the past 120 years, Lipson said, moving from mechanical instruments to graphics processing units (GPUs) today.

Getting work done is a fundamental concern for any business. But today, paradigm-shifting forces seem to be driving significant changes in both work and the workforce. New digital and communications technologies are changing how work gets done. The growth of the gig economy and advances in artificial intelligence are changing who does the work. Even the question of what work looks like is coming under examination as a continually evolving marketplace drives organizations to explore new business models. In the face of these technological and social forces, it could be imperative for businesses to rethink their approaches to the how, who, and what of work in fundamental, perhaps even transformative ways. And as usual, there seem to be no easy answers.

If you had to guess how long it takes for a drug to go from an idea to your pharmacy, what would you guess? Well, here's the sobering truth: 90 percent of all drug possibilities fail. The few that do succeed take an average of 10 years to reach the market and cost anywhere from $2.5 billion to $12 billion to get there. But what if we could generate novel molecules to target any disease, overnight, ready for clinical trials? Imagine leveraging machine learning to accomplish with 50 people what the pharmaceutical industry can barely do with an army of 5,000.

The world is in the midst of a technological revolution. Rapid developments in artificial intelligence, augmented and virtual reality, edge and quantum computing, autonomous vehicles, precision medicine, nanotechnology and digital biology are transforming the way we live and work, creating new challenges and opportunities. It is essential that federal leaders understand the potential of these breakthrough technologies and capitalize on those with the greatest promise to improve government operations and services. During this time of swift change, the government is taking steps to explore and leverage these emerging technologies. In February, President Trump issued an executive order to strengthen American leadership in artificial intelligence, asserting that the U.S. should "drive technological breakthroughs in AI across the federal government, industry and academia in order to promote scientific discovery, economic competitiveness, and national security."

Legend has it that when an emperor asked an inventor to name his reward, the inventor asked the emperor for payment in the humble grain of rice, giving the inventor the total gained by doubling a single grain of rice over a 64-square chessboard. In the end, the final squares had exponential mountains of rice. The velocity of technology advancement, especially for the manufacturing industry, is no different--it's exponential. Despite its lower digital maturity and research and development (R&D) investment than other industries,1 the manufacturing industry has been successful in gradually furthering its innovation agenda by increasing its patent-based innovation intensity to build new product and service capabilities. Continuous advancement will likely become critical to the success in an ecosystem that industry leaders believe is nearing the "second half of the chessboard."2