Arvind Gupta, Partner and Head, Management Consulting, KPMG in India, explains why HR functions are still reluctant to use AI, the co-effect of workplace culture and digital transformation on each other and the CIO's role in all of this. Edited excerpts: What factors are causing reluctance in adopting AI in HR functions? One of the main challenges that see's reluctance is the fact that employees' data are not present in one single location. In most cases, the data is distributed over many different data sets; and often the absence of one set in the analytics could lead to a totally wrong estimation. Another challenge is that the world of HR is not one where black and white decisions work.

A bigger issue to overcome is the building of trust and confidence in AI. Executives interviewed as part of our 2019 AI Predictions report suggested that ensuring AI systems are trustworthy is their top challenge for the year ahead. And according to our 22nd PwC CEO Survey, 82% of business leaders agree AI-based decisions need to be explainable in order to be trusted. While the overall feeling is that it will create opportunity, opinions vary when it comes to the impact of AI on job figures. In'How will automation impact jobs?' PwC reports that the net impact will be roughly flat, with significant variation by sector.

In 1888, the London-based accounting firm that became PricewaterhouseCoopers (PwC) faced a major technological upheaval thanks to the Burroughs adding machine. The first-ever mechanized calculator, an invention by William Seward Burroughs, cut the time to perform accounting tasks in half, and PwC's hundreds of workers had to quickly master the new system, or get left in the dust. Today, PwC isn't simply an accounting firm--now it's a global consultancy with 250,930 employees in 158 countries, raking in $43.1 billion in revenue in 2018--but once again it, along with thousands of other companies, faces a seismic technological shakeup with the advent of AI and other advanced technologies. It's rising to meet the challenge by preparing its workers to use digital technologies at all levels, from entry-level staff to C-suite executives. And it's not alone in its reskilling push--AT&T, IBM, Walmart and other forward-leaning companies also have major retraining programs underway.

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).