… with help from a machine, the result is typically entirely new systems … of Artificial Intelligence (Harvard Business Review Press, 2022).

In a mid-2020 issue of Supply & Demand Chain Executive, I had the pleasure of speaking with managing editor Brielle Jaekel on the "emerging technologies that claim to help companies in the supply chain." "In the near future, supply chain AI will begin to migrate to machine learning. Currently, supply chain AI consists of developers programming business rules, telling computers what to look for and what action to take when it encounters those situations, but as AI migrates to machine learning, it will begin to think for itself. As machine learning becomes more advanced, technologies will increasingly be able to make note of repetitive situations and past experiences to start learning and making recommendations on its own. Technology like this has already deployed on a wide scale in other industries, and it has the potential to rapidly automate and improve a wide range of supply chain processes."

There have been Kondratiev waves throughout history, commonly referred to as innovation waves, including the invention of electricity, the printing press, and the steam engine. All of these technologies spurred a paradigm shift which resulted in transforming the way the world operated. Today, many believe AI is the next Kondratiev wave and that it will be responsible for transforming how businesses create value, how people work, and ultimately how people live. For businesses to survive the era of AI, they must prepare to abandon legacy technology and invest in new ways of doing things, sometimes reasonably quickly in order to stay relevant. This phenomenon is called the "burning platform" effect, based on the idea that in order to stay competitive, businesses must adopt a radical change strategy as if their current way of doing things was on fire.

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.

There have been Kondratiev waves throughout history, commonly referred to as innovation waves, including the invention of electricity, the printing press, and the steam engine. All of these technologies spurred a paradigm shift which resulted in transforming the way the world operated. Today, many believe AI is the next Kondratiev wave and that it will be responsible for transforming how businesses create value, how people work, and ultimately how people live. For businesses to survive the era of AI, they must prepare to abandon legacy technology and invest in new ways of doing things, sometimes reasonably quickly in order to stay relevant. This phenomenon is called the "burning platform" effect, based on the idea that in order to stay competitive, businesses must adopt a radical change strategy as if their current way of doing things was on fire.

CURT NICKISCH: Welcome to the HBR IdeaCast from Harvard Business Review. A growing number of companies are turning to artificial intelligence to solve some of their most vexing problems. The promise of AI is that it can go through vast amounts of data and help people make better decisions. And one area where companies often search for profitable use cases for the technology is in marketing. It's harder than it looks. Data scientists at one consumer goods company recently used AI to improve the accuracy of the sales forecasting system. While they did get the system working better overall, it actually got worse at forecasting high margin products. And so the new, improved system actually lost money. Today's guest says that many leaders lean too heavily on AI and marketing without first thinking through how to interact with it.

Low code/ no-code platforms are a type of visual software that enable businesses and developers to drag and drop applications, connecting them to great apps. Low code/no-code approaches allow developers to quickly build applications and alleviate the need to write codes line by line. This helps small business owners, office administrators, business analysts and others who are not well versed with software development to develop test applications. These people have little or no knowledge of programming, development work or machine code. Programmers write lines of code to generate the capabilities and features requested in a computer programme or application in traditional software development.

Created video streaming and network acceleration algorithms that have found their way in screens both large and small. It was also the first patent I had granted with Samsung, it was an equally challenging and fulfilling development crunch. Developed a mixed-reality tool for connected classrooms. It was a project with Samsung's Advanced Solutions Lab where one day I'm prototyping hardware for its Tangible User Interface, and another day I'm coding pattern recognition. While Harvard Business Review's D.J. Patil and Thomas Davenport declared Data Scientist "The Sexiest Job of the 21st Century", I have been fortunate enough to personally have been in a position to lead companies in various industries which were going through their own big data and data science transformations.

Few recent technological developments have garnered as much fear and optimism as artificial intelligence (AI). Perhaps AI has captured the popular imagination because it requires us to reflect on what makes us fundamentally human, both in terms of our experiences and our capabilities. AI presents us with stark visions of the future. Visions which can be grouped into two now-familiar over-simplifications: a utopian version where the mundane tasks of work and life are delegated to machines, and a dystopian version where automation heralds a new age of mass unemployment and human misery. These tropes are caricatures, of course, but the promise and the perils of AI are very real.

Companies are interested in buying insurance to mitigate the risk of adoption and deployment of new AI applications with no history of use. "When it comes to the commercial use of AI, businesses can't rely on government regulation to protect them against potential losses in the event it fails to live up to its promise," stated Saar Yoskovitch, CEO and cofounder of Augury, in a recent account in Open Access Government. As deployed AI systems mature, they will increasingly make high risk decisions. "But AI models are often brittle, do not deal well with edge cases and may have been trained on a dataset with inherent biases," stated Yoskovitch. This is especially prevalent with AI systems that use human behavior as an input, such as auto insurance applications that capture an individual customer's driving behavior.