Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

Based on our interactions and the results of this study, we expect to see organizations not only adopt AI – but scale it across their enterprises, by building/developing their own AI, or putting ready-made AI applications to work. For example, according to the survey, 40% of respondents currently deploying AI said they are developing proof-of-concepts for specific AI-based or AI-assisted projects, and 40% are using pre-built AI applications, such as chatbots and virtual agents. I see the excitement building with clients every day. Consider just a couple of recent examples. Legal software developer LegalMation has leveraged IBM Watson and our natural language processing technology to help attorneys automate some of the most mundane litigation tasks, speeding, for example, the written discovery process from multiple hours to a few minutes.

To cross the Atlantic with an unmanned, autonomous ship will push technology to its limits. Like the Mayflower's 101 passengers in 1620, we'll also need a lot of help and some good luck. I grew up in Boston, Massachusetts. I spent a lot of time messing around in the ocean, swimming and diving. Unlike my friends and brothers, I preferred looking at the fish to eating them.

It's clear that data and AI are two-sides of the same coin; in fact, this understanding spawned the AI Ladder concept. We've developed skills training in the areas of data science and machine learning; we've partnered with standards bodies to create new certifications around data science; and we even created a group of experts, the IBM Data Science Elite Team, which is dedicated to helping clients get this first AI model into production, on our nickel. Based on our experience, I have identified three areas (or beachheads) that are essential to leading in AI: a Differentiated Platform, Skills and Research. It is important to note that more than 90% of AI technology is "common knowledge." So, what matters is where a company puts its focus in technology hardening and productization.

AI has gone through many cycles since we first coined the term "machine learning" in 1959. Our latest resurgence began in 2011 when we put Watson on national television to play Jeopardy! This became a cornerstone event, demonstrating that we had something unique. And we saw early success, putting Watson to work on projects with clients. This created even more excitement. That excitement led to more opportunity.

When people think of tech in media, their first thoughts are likely around digital platforms that bring more content to our fingertips. Now, leading innovations -– namely, AI and blockchain -- are enabling media companies to take this a step further by delivering even more compelling content to broader audiences. AI is being used to watch and understand live video in real-time to provide insights that enhance the viewer experience. Technologies such as computer vision and image recognition can tag video based on different characteristics. There is no better example of using AI to create dynamic experiences than in sports, where AI can identify exciting plays by evaluating players' actions or an audience's reaction.

For centuries, electricity was thought to be the domain of sorcerers – magicians who left audiences puzzled about where it came from and how it was generated. And although Benjamin Franklin and his contemporaries were well aware of the phenomena when he proved the connection between electricity and lightning, he had difficulty envisioning a practical use for it in 1752. In fact, his most prized invention had more to do with avoiding electricity – the lightning rod. All new innovations go through a similar evolution: dismissal, avoidance, fear, and perhaps finally acceptance. Almost two hundred years after Franklin's lightning experiment, man was routinely harnessing electricity, even though we still lacked a deep understanding of its origins. The Lineman's Handbook of 1928 begins with the line: "What is electricity?

AI is a transformative and exciting technology that will no doubt reshape the way we live and work for decades to come. That said, we need to be extremely thoughtful and intentional about how we design these solutions. Those who design, develop, and deploy AI are responsible for ensuring that every solution they create has a clear intent and provides consistent value to users, while accounting for the ethical implications of its existence. For the past three years, my team and I have been researching and creating a formal approach to designing AI solutions that are intent-focused rather than tech-led. When it comes to the creation process, defining and aligning on the intent of the AI is the single biggest key to success.

We are at an inflection point. Data Science and AI technologies are not just a continuum of old technologies. They represent a complete paradigm shift that take us from a deterministic world to a probabilistic world, increasing the potential to profoundly change human society, and to become a new engine of economic development. Around the world, governments and businesses are implementing AI strategies to ensure readiness for a new round of industrial transformation, with the birth of new technologies, new products, new industries, new formats, and new business models. Collectively, we have never had more access to powerful technologies, or to more massive amounts of data.

As enterprises turn to artificial intelligence (AI) to beef up business performance and drive competitive advantage, IBM is working to make analytics, data science and machine learning – the core of AI – more accessible and easy for any organization, large or small, via the cloud. It's no surprise that large organizations understand the value of AI. When dealing with and managing massive data volumes that swell with each day, capabilities like data science and machine learning, can provide business insights fast allowing strategies to be confirmed or adjusted. In fact, a new IBM Institute for Business Value study reported that 82 percent of enterprises are considering AI deployments. But as I talk with clients, I've found that data insights often follow the path of least resistance – when discovered, word spreads fast.