JDA Software and KPMG LLP recently published a wide-ranging survey regarding supply-chain technology. The main takeaway: end-to-end visibility is the No. 1 priority. But in order to make this a reality, the survey also notes that AI (Artificial Intelligence), machine learning (ML) and cognitive analytics will be critical. Yet pulling this off is far from easy and fraught with risks. Well, I recently had a chance to talk to Dr. Michael Feindt.

For forward looking executives and organizations, planning for a digital workforce needs to be a top priority. By working to address future skills needs and talent instability leaders can prepare now to operate effectively in the business environment of tomorrow. Our combined, unique perspective on the Workforce For The Future provides a holistic and integrated view, enabling organizations of all sizes to transition to a technology-enhanced environment, while ensuring that their workforce thrives. As companies transform their business models and strategies to realize the opportunities of the digital revolution, they are challenged with defining their workforce for the future. Given the anticipated scarcity of skills and the need to make workforce management an integral part of business strategy, Mercer, the leading company in HR consulting, and Oliver Wyman, a premier management consulting company, have partnered to support business leaders and HR functions with an integrated talent, digital and skills strategy approach.

Where does your organization stand? This is the second blog in a four-part series detailing the components necessary for AI success. You can read my earlier post about cultural willingness, which must be prioritized ahead of data and infrastructure readiness (this blog), workforce skilling, and plans for ethics, risk and compliance. Combining the computational power of artificial intelligence (AI) with the critical thinking ability of humans is the ideal solution for organizations looking to accelerate the discovery of actionable insights from their data assets. Even with the human expert in the loop, to achieve valid results with as little bias as possible, AI relies on large volumes of historical data and sophisticated mathematics to generate insights.

According to a PricewaterhouseCoopers study, 20 percent of executives plan to incorporate AI across their enterprises in 2019. Over the past year, countless organizations and Fortune 500 companies have boasted about their AI strategies. When it came time to put those strategies into practice, however, they realized that what they called a "strategy" was little more than tools without guidance. Businesses today have the resources, knowledge and incentive to create effective strategies behind their AI implementations. Despite these capabilities, few companies take the time to do so.

DevOps teams seeking to step up their mojo in developing cutting-edge artificial intelligence (AI) features are facing a big skills bottleneck when it comes to data analytics and machine learning modeling. As a result, the market is seeing an influx of self-service machine learning models and machine learning-as-a-service offerings designed to help development teams more easily integrate AI capabilities into their software. This is coming in direct response to an explosion in demand for AI capabilities in the enterprise. According to Gartner analysts, AI adoption in the enterprise tripled in the past year. A report last fall from MIT Sloan Management Review and Boston Consulting Group found that 91% of enterprises believe that AI will deliver new business growth to them by 2023.

In the last few years, many asset-intensive organizations, particularly in the mining, power and utilities, oil and gas, and chemicals industries, have turned to industrial Internet of Things (IIoT) and cognitive technologies to help improve a critical area of their business: equipment reliability.1 Asset performance management (APM) programs, which connect data and trigger actions via systems across the business, can play a major part in driving these improvements. According to a 2018 Deloitte survey, oil and gas leaders rated the big data derived from programs such as APM as the most likely to provide the greatest business value.2 However, when asked about how digital technology can be used most effectively within their companies, those same executives ranked APM below both cost reduction in maintenance and operations as well as improvements in safety.3 This seems to reveal a pervasive and narrow view of APM that may miss the connection between asset performance, broader maintenance and operations improvements, and safety. Merely implementing APM software and digitizing existing processes is not likely to improve core operations and obtain the financial results that executive leaders desire (and investors demand).

Artificial Intelligence will bring massive new capabilities as well as disruption to businesses as well as society. The term isn't new, and has been around for decades, but AI today has gone far beyond the realm of science fiction. AI comes into play each time you use a smartphone, or when a bank decides to pitch a new financial product to you, or for personalised medicine. And AI usage will only grow, which is what strikes fear among some too. From successful use cases, sectors with massive potential for AI-driven transformation, job-loss fears, and what India needs to do to catch up with the US & China in AI, training AI for bias, etc., Ivor Soans, Editor-Special Features at BloombergQuint, discusses all these and more with Sanchit Vir Gogia, CEO of Greyhound Research, and one of India's finest navigators of technology trends.

A key trend from this year's Accenture Technology Vision, DARQ Power, sets out how the next wave of technologies – distributed ledger, AI, XR and AR and quantum computing – promise to be the catalysts for change that will offer Defence organisations extraordinary new capabilities and also new threats. The pace of change is gaining momentum and public service leaders recognise this. Ninety three per cent of them say that emerging technologies have accelerated innovation in their organisation over the past three years. And as technology continues to evolve rapidly, Defence leaders should investigate and prepare for the wave of disruption that is coming. We see that in the near future XR will change how training is performed.

Big issues call for big ideas. In this Connected Intelligence series, we explore a range of perspectives on today's business demands. We asked business leaders from a variety of industries how they were shaping their companies to meet the challenges of the future. From AI to Big Data, from shifting workforces to achieving growth, this series delves into how these leaders are planning to meet these challenges head on with bold ambitions and fresh thinking. New episodes will be added regularly, each lasting from 10-15 minutes.