Unlocking AI's full potential requires operational excellence For successful AI adoption, leaders need to focus on structure rather than speed. Talk of AI is inescapable. A record 58% of S&P 500 companies mentioned AI in their second-quarter earnings calls, according to Goldman Sachs. But it's difficult to walk the talk. Just 5% of generative AI pilots are driving measurable profit-and-loss impact, according to a recent MIT study . That means 95% of generative AI pilots are realizing zero return, despite significant attention and investment.

In the words of J.R.R. Tolkien, "shortcuts make long delays." I get it, we live in an age of instant gratification, with Doordash and Grubhub meals on-demand, fast-paced social media and same-day Amazon Prime deliveries. But I've learned that in some cases, shortcuts are just not possible. Such is the case with comprehensive AI implementations; you cannot shortcut success. Operationalizing AI at scale mandates that your full suite of data–structured, unstructured and semi-structured get organized and architected in a way that makes it useable, readily accessible and secure.

Corey Jaskolski is the founder and CEO of Synthetaic, the leading synthetic data company for impossible AI. It's a common misconception that most businesses are drowning in data and that AI is spoiled for choice when it comes to training data. The truth is that despite the big data boom, most businesses still lack the quantity of high-quality data they need, and it's holding back the development of the highest-value AI applications. Current AI research is yielding phenomenal results, but the AI beating the world's best players at Go or outperforming human lip readers are the exception, not the rule. AI keeps getting better by training on increasingly massive models, some of which have a billion tunable parameters.

New developments in AI could spur a massive democratization of access to services and work opportunities, improving the lives of millions of people around the world and creating new commercial opportunities for businesses. Yet they also raise the specter of potential new social divides and biases, sparking a public backlash and regulatory risk for businesses. For the U.S. and other advanced economies, which are increasingly fractured along income, racial, gender, and regional lines, these questions of equality are taking on a new urgency. Will advances in AI usher in an era of greater inclusiveness, increased fairness, and widening access to healthcare, education, and other public services? Or will they instead lead to new inequalities, new biases, and new exclusions?

AI is the cornerstone of the next generation of technology applications and is already on its way to infiltrating every part of our daily lives. As such, training AI from a diverse set of perspectives on the world is vital if we hope to advance AI in a way that makes the world a better place. The ever-important task of fostering diversity in the burgeoning AI community is a responsibility that falls upon all of us, not just corporate gatekeepers or select data scientists with advanced technical degrees. Matt Zeiler unveils groundbreaking new technologies that will transform the way AI is "taught" and make both teaching and using AI accessible to anyone in the world.