We've understood for a long time that organizations who spend more on, and are better at, deriving value from their data using analytics significantly outperform their peers in the market. All of us also know, because we feel it, that the pace of change is ever increasing. I see this all the time with the customers I work with, many of whom seem to be suffering from the "Red Queen" effect – each having to change and innovate faster just to keep standing still, let alone make progress against a tide of change. I've also had cause to re-read Salim Ismail's book, "Exponential Organizations", recently which got me thinking: in order to compete, we should be designing and building solutions that allow us to exponentially grow our capacity to create value from data in order to meet business demand. Importantly though, how do we do that without also exponentially growing infrastructure costs (a bad idea) and the number of Data Scientists employed (an impossible dream)?

We've understood for a long time that organizations who spend more on, and are better at, deriving value from their data using analytics significantly outperform their peers in the market. All of us also know, because we feel it, that the pace of change is ever increasing. I see this all the time with the customers I work with, many of whom seem to be suffering from the "Red Queen" effect – each having to change and innovate faster just to keep standing still, let alone make progress against a tide of change. I've also had cause to re-read Salim Ismail's book, "Exponential Organizations", recently which got me thinking: in order to compete, we should be designing and building solutions that allow us to exponentially grow our capacity to create value from data in order to meet business demand. Importantly though, how do we do that without also exponentially growing infrastructure costs (a bad idea) and the number of Data Scientists employed (an impossible dream)?

How do I become more effective at leveraging (big) data and analytics (artificial intelligence) to power my business? Success with artificial intelligence doesn't begin with technology, but rather the business, and more specifically the people and processes running the business. Before deploying technology, leaders should seek to understand (envision) how artificial intelligence could power a profitable business, and drive compelling customer and operational outcomes. Collaboration with stakeholders and key constituents is critical to understanding the decisions and needs of the business. While every organization's needs vary, there exists a consistent, transparent process that can drive a more stable and widespread adoption of artificial intelligence.

Will I ever understand the nuances of the advanced analytics landscape? Well, maybe the better question is will the advanced analytics landscape ever stop changing? The advanced analytics landscape, into which I include Deep Learning (DL), Machine Learning (ML), Reinforcement Learning (RL) and Artificial Intelligence (AI), seems to be in a constant state of evolution. New advanced analytic algorithms and tool sets seem to be coming out of every university, every startup, every digital media company and every technology company. And many of these new advanced analytic algorithms and tool sets are open source, which means that they are available for others to build upon.

Traveling between Silicon Valley, Austin and Boston, I am hearing about artificial intelligence and the future of work from so many angles and couldn't be more excited about the opportunities ahead. The strategies and applications are enticing and the next wave is promising. However, as we rush to embrace AI, I can't help wondering whether we are maximizing its full potential or just riding from technology wave to technology wave. For instance, today our walls talk to us – almost everything in our lives talk to us. With a modest investment, smart technology sensors capture data we use to monitor, measure and manage our energy usage and efficiency (and more).

How do I become more effective at leveraging (big) data and analytics (artificial intelligence) to power my business? Success with artificial intelligence doesn't begin with technology, but rather the business, and more specifically the people and processes running the business. Before deploying technology, leaders should seek to understand (envision) how artificial intelligence could power a profitable business, and drive compelling customer and operational outcomes. Collaboration with stakeholders and key constituents is critical to understanding the decisions and needs of the business. While every organization's needs vary, there exists a consistent, transparent process that can drive a more stable and widespread adoption of artificial intelligence.

Imagine you wanted to determine how much solar energy could be generated from adding solar cells to a particular house. This is what Google's Project Sunroof does with Deep Learning. Enter an address and Google uses a Deep Learning framework to estimate how much money you could save in energy costs with solar cells over 20 years (see Figure 1). But let's assume there "might" be an even better way to estimate solar energy savings. For example, you want to use Deep Learning to estimate how much solar energy we could generate with solar panels on the Golden Gate Bridge (that probably wouldn't be a very popular decision in San Francisco).

It's Super Bowl LII week and the Philadelphia Eagles and New England Patriots are on the precipice of a championship. One game to decide it all, where one decision could swing the fortunes of either team, depending on a single play call. What can Bill Belichick or Doug Pederson do to avoid a letdown or propel his team to victory? It's simple – play the percentages. We only need to look back one year to observe how a coach's thought process may have prevented his team from claiming a Super Bowl title.

Digital transformation is a directive that organizations of all shapes and sizes are striving to achieve and for those of us in services, it's no different. We're constantly innovating, implementing and improving on capabilities that make it easier for customers to adopt and optimize technology that will transform their businesses and lives. In the recently released Dell Technologies 2018 Predictions, reference was made to how things such as artificial intelligence (AI), augmented reality (AR), machine learning and deep learning (ML, DL) – essentially data science – will work hand-in-hand with humans to enable deeper relationships with customers. There's a quote from MIT Media Lab founder, Nicholas Negroponte that says: "Computing is not about computers anymore. When it comes to services, it's no longer just about technology, it's about making support and deployment as smooth as possible and enhancing and augmenting the customer's experience with the products they've purchased.

Chaos: When the present determines the future, but the approximate present does not approximately determine the future.