We already know it, artificial intelligence (AI) is reshaping businesses and organizations, although not at the speed some thought. Companies that are successfully adopting AI aren't just launching a few projects to evaluate results. These companies are changing organizational structures, changing how people and teams collaborate, and changing how managers make decisions. Although technology changes and evolves exponentially, the capabilities that we develop for one of them, for example AI, will be there and will be necessary for the next technology. In other words, we are building new organizational capabilities to understand, take advantage of and exploit new technologies.

In my last blog post, I talked about the importance of establishing the right team for data science projects. Here, I'm going to talk about some of the barriers that can prevent successful adoption of data science. You can read my whole "data science in the wild" blog series here. Data scientists tend to work on loosely defined business problems that involve messy data combined with computationally intensive machine learning algorithms. It is, therefore, important to establish a data strategy.

In 1953, US senators grilled General Motors CEO Charles "Engine Charlie" Wilson about his large GM shareholdings: Would they cloud his decision making if he became the US secretary of defense and the interests of General Motors and the United States diverged? Wilson said that he would always put US interests first but that he could not imagine such a divergence taking place, because, "for years I thought what was good for our country was good for General Motors, and vice versa." Although Wilson was confirmed, his remarks raised eyebrows due to widespread skepticism about the alignment of corporate and societal interests. The skepticism of the 1950s looks quaint when compared with today's concerns about whether business leaders will harness the power of artificial intelligence (AI) and workplace automation to pad their own pockets and those of shareholders--not to mention hurting society by causing unemployment, infringing upon privacy, creating safety and security risks, or worse. But is it possible that what is good for society can also be good for business--and vice versa?

Finally, AI is ready for the mainstream. When your enterprise is handling transactions between 25 million sellers and 182 million buyers, supporting 1.5 billion listings, manual decision-making processes just won't cut. Such is the case with eBay, the mega commerce site, that has been employing artificial intelligence for more than a decade. As Forbes contributor Bernard Marr points out, eBay employs AI across a broad range of functions, "in personalization, search, insights, discovery and its recommendation systems along with computer vision, translation, natural language processing and more." As part of a massive operation with so much experience with AI, Mazen Rawashdeh, CTO of eBay, has plenty to say about the current state of enterprise AI.

Lack of trust: One of the biggest difficulty for AI or ML products is lack of trust. Millions of dollars have been spent on prototyping but with very little success in the real-world launches. Essentially, one of the most fundamental values of doing business and providing value to customers is trust, and Artificial Intelligence is the most-heavily debated technology when it comes to ethical concerns and related trust issues. Trust comes from involving different options and parties in the entire development phase, which is not done in the prototype phase. The complexity of a launch: Building a prototype is easy, but there are tens of other external entities that need to be considered when moving into the real world.

Hands-on tips for companies to build Machine Learning Products that are being adopted by their users and customers. The biggest difficulty for products based on machine learning (ML) will be user or customer adoption. How did I come to this conclusion? A top executive of one of the biggest European insurance companies told me: "We have the money and technical talent to build sophisticated ML-products, but we do not know how to make users adopt those products. We spent millions of dollars on an ML-based app but only got around 300 users. We do not understand why people do not want to use our app."

Utilizing artificial intelligence on a smaller scale is relatively simple in nature. This may be a contributing factor to the recent survey results from Gartner indicating that while 46% of CIOs have plans for implementing AI, only a mere 4% have actually done so. Those early adopters no doubt have faced and overcome many challenges along the way. Here are four lessons that you can learn from that will make adopting AI at scale easier. Desmond Tutu once said that the best way to eat an elephant is "one a bite at a time."

In 1953, US senators grilled General Motors CEO Charles "Engine Charlie" Wilson about his large GM shareholdings: Would they cloud his decision making if he became the US secretary of defense and the interests of General Motors and the United States diverged? Wilson said that he would always put US interests first but that he could not imagine such a divergence taking place, because, "for years I thought what was good for our country was good for General Motors, and vice versa." Although Wilson was confirmed, his remarks raised eyebrows due to widespread skepticism about the alignment of corporate and societal interests. The skepticism of the 1950s looks quaint when compared with today's concerns about whether business leaders will harness the power of artificial intelligence (AI) and workplace automation to pad their own pockets and those of shareholders--not to mention hurting society by causing unemployment, infringing upon privacy, creating safety and security risks, or worse. But is it possible that what is good for society can also be good for business--and vice versa?

Only Homo sapiens, of all the descendants of Homo erectus, survived on earth whereas other species such as homo soloensis, homo denisova, Homo neanderthalensis, Homo floresiensis faded away more than 40,000 years ago. What advantages did Homo sapiens possess that helped them to flourish while other species are extinct? Apparently a cognitive revolution (according to Prof. Yuval Harari in his famous book Sapiens) triggered by some kind of genetic mutation provided Homo Species with more cerebral power and thus they acquired an ability not possessed by any other species – ability to imagine things that did not exist. This ability helped them to invent things including powerful communicative languages, religion, tools and more. Does current cognitive revolution ushered under various nomenclatures such as artificial intelligence, cognitive computing etc provide more powers, this time to machines and bring in unprecedented progress to human life?

Only Homo sapiens, of all the descendants of Homo erectus, survived on earth whereas other species such as homo soloensis, homo denisova, Homo neanderthalensis, Homo floresiensis faded away more than 40,000 years ago. What advantages did Homo sapiens possess that helped them to flourish while other species are extinct? Apparently a cognitive revolution (according to Prof. Yuval Harari in his famous book Sapiens) triggered by some kind of genetic mutation provided Homo Species with more cerebral power and thus they acquired an ability not possessed by any other species – ability to imagine things that did not exist. This ability helped them to invent things including powerful communicative languages, religion, tools and more. Does current cognitive revolution ushered under various nomenclatures such as artificial intelligence, cognitive computing etc provide more powers, this time to machines and bring in unprecedented progress to human life?