In my experience leading transformation initiatives, I have seen many similarities between broader business transformations and AI transformations, but there is one key difference – in AI, the world is still highly experimental. In fact, 3 out of 4 of AI projects fail to show positive returns on investment. Because of this, there hasn't been a map of what AI success at scale really looks like. It remains uncharted territory for enterprises. In October 2019, we commissioned a study with Inc.digital, where we talked to 550 executives across a variety of industries to understand why enterprises don't more frequently see tangible, measurable results from AI. Through this research, we were able to determine the DNA of the few that are successful with their AI strategy – the one common thread is keeping the AI strategy, data, technology, people and processes close to the core and controlled.

I hate it when I hear senior executives state that they want to become data-driven, as if somehow having data is value in of itself. Now, one can hardly blame the unenlightened executive whose only perspectives on data are associated with statements like "Data is really the new oil" (Wall Street Journal) or "The world's most valuable resource is no longer oil, but data" (The Economist). The infatuation with "data-driven" versus "value-driven" can be confirmed from Google Trends (Figure 1). However, this is where the value determination of data and oil diverge. Oil has value as determined by Generally Accepted Accounting Principles (GAAP).

I hate it when I hear senior executives state that they want to become data-driven, as if somehow having data is value in of itself. Now, one can hardly blame the unenlightened executive whose only perspectives on data are associated with statements like "Data is really the new oil" (Wall Street Journal) or "The world's most valuable resource is no longer oil, but data" (The Economist). The infatuation with "data-driven" versus "value-driven" can be confirmed from Google Trends (Figure 1). However, this is where the value determination of data and oil diverge. Oil has value as determined by General Acceptable Accounting Principles (GAAP).

Artificial intelligence (AI) is rapidly being adopted by organizations of all sizes and yielding impressive results, transforming businesses and industries globally. While senior executives, managers and senior IT architects are often catalysts for change, it is the developers who are deciding which specific products and technologies individual companies will use on their journey to AI innovation. Interestingly, while many AI projects get started following executive mandates, often it is the AI developers and data scientists who shepherd new and innovative AI projects along in the organization. AI developers and data scientists aren't as motivated by executive mandates as they are by the combination of experimentation, the desire to proactively satisfy new client demands, and of course by the fun realized in delivering new and innovative AI solutions. In many cases, it's the AI developers and data scientists who also can quickly establish which specific AI tools and algorithms will immediately help an organization solve business problems.

Key Points: – The market for AI is on an exponential growth curve and is expected to reach $16.06 billion by 2022. Artificial intelligence is rapidly coming of age, poised to transform businesses and industries globally. The market for AI is on an exponential growth curve and is expected to reach $16.06 billion by 2022. With over half of all developer teams projected to embed AI services in their apps by 2018, it's inevitable that consumers will soon be interacting with these new technologies on a regular basis. While the growing popularity of AI is clear, who's actually driving the adoption of these new technologies at organizations?