The Drum caught up with Gareth Mackown, partner and European mobile leader at IBM Global Business Services, at the Mobile World Congress this week in Barcelona to ask him about how mobile solutions are becoming more vital for not only an enterprise's customers, but also employees. "Today, organizations are really being defined by the experiences they create," Mackown said in an interview. "Often, you think of that in terms of customers, but more and more we're seeing employee experience being a really defining factor." IBM partnered with Apple to transform employee experiences through mobility, he said, and it's just getting started. Internet of Things (IoT) technology, cognitive computing and analytics will make those mobile solutions "even more critical" for people working in all kinds of different fields.

The world of AI, robotics and cognitive computing are changing business even faster than we thought. JPMorgan Chase & Co now uses software to perform the mind-numbing job of interpreting commercial loans, reducing 360,000 hours of lawyer time each year. AI software can now identify leukemia in photos and X-rays, learning faster than technicians. And the stories go on and on. Is this real and widespread around the world?

Perception - How sensors and processing of information are contextualised and used. How this is defined as a level of "awareness" in seeing, hearing, touching, "feeling" for example in the immediate physical or transmitted field of perception frame. It leads on into questions of understanding and areas of sentience we see in debates of consciousness in bacteria, plants, animals and humans. Action - How physical motor skills are enacted to interact with the environment. This includes the agency of actuators and robotic degrees of freedom but can expand beyond mechanisms of mechanical manipulation to other forms of state change in energy and matter through automation (akin to commentators using numbers to describe eras such as Industry 4.0 and 4th industrial revolution). Language - How symbolic representation of information, its semiotic representation and syntax definition is defined.

As I think back about our research this last year, and all we have learned, I leave you with one simple message. All the changes we discuss are no longer "interesting" or "informative" – they have become real, and essential mandates for the future. We decided to call them "rules" because we believe they are now clear, and we want you to understand them so you can experience greater levels of productivity, performance, and employee excitement in your company. As always we look forward to explaining the Global Human Capital Trends to you in person this year, and hope to hear your comments and feedback as we all learn how to build the thriving organizations of the future.

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart" (omni-present in anything Internet of Things as well), "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture.

Without a doubt, cognitive computing systems are a hot topic around boardrooms, executive suites, and conference tables at major technology firms, which are investing both financial and human resources to bring these systems to fruition. At the same time, government offices throughout the world are holding similar discussions, and these efforts are starting to come to fruition as well. Cognitive computing systems mine both structured and unstructured data to offer hypotheses and solutions for consideration by humans. They thrive on massive amounts of data: greater availability yields better analysis. In addition, cognitive computing systems rely on humans to train them through supervised learning.

As I think back about our research this last year, and all we have learned, I leave you with one simple message. All the changes we discuss are no longer "interesting" or "informative" – they have become real, and essential mandates for the future. We decided to call them "rules" because we believe they are now clear, and we want you to understand them so you can experience greater levels of productivity, performance, and employee excitement in your company. As always we look forward to explaining the Global Human Capital Trends to you in person this year, and hope to hear your comments and feedback as we all learn how to build the thriving organizations of the future. About the Author: Josh Bersin is the founder and Principal of Bersin by Deloitte, Deloitte Consulting LLP, a leading research and advisory firm focused on corporate leadership, talent, learning, and the intersection between work and life. Josh is a published author on Forbes, a LinkedIn Influencer, and has appeared on Bloomberg, NPR, and the Wall Street Journal, and speaks at industry conferences and to corporate HR departments around the world. You can contact Josh on twitter at @josh_bersin and follow him at http://www.linkedin.com/in/bersin . Josh's personal blog is at www.joshbersin.com .

The electrical grid has become a network of billions of linked devices with highly complex energy and information flows. Add to this the elevated role of the consumer as a producer and you are looking at a massive volume, velocity and variety of data from smart meters, transformers, and substations that remains largely untapped. One extremely interesting solution to the impending challenges is cognitive computing, which is gaining traction among industry experts as a way to better manage data and improve both operating efficiencies and customer service. According to GTM Research, applying cognitive computing is expected to deliver an estimated $121 billion global return on investment (ROI) on grid analytics by 2020. There are three key areas where energy companies are already engaging with cognitive systems to experience real benefits.

Managing business data is a big trend right now. Through a couple of clicks and a couple drags, anybody can explore it. Businesses have been collecting and analyzing data since the 1950s. Data is the prime factor for businesses in order to gather the information and related analysis to increase operational efficiency, reduce costs and serve their customers. You might be wondering, why one must care about Data Analysis, in particular?

Artificial intelligence (AI) is one of the most evocative and confusing terms in technology. According to Accenture, artificial intelligence (AI) could add an additional US$814 billion in 2035 to the UK's economy--with growth rates increasing from 2.5 percent to 3.9 percent in 2035. We have seen a machine master the complex game of Go, previously thought to be the most difficult challenge of artificial processing. We have witnessed vehicles operating autonomously, including a caravan of trucks crossing Europe with only a single operator to monitor systems. We have seen a proliferation of robotic counterparts and automated means for accomplishing a variety of tasks.