Two people dressed in black are kneeling on the floor, so still that they must surely be in pain. If they are grimacing, there would be no way to know – their features are obscured by oversized, smooth gold masks, as though they have buried their faces in half an Easter egg. Their stillness makes them seem like sculptures, and only by checking for the subtle rise and fall of their chests can you confirm they are indeed human. Which is fitting, really – because they aren't actually human, at least not totally. They're human-machine hybrids, "Idioms", created by French artist Pierre Huyghe for his largest ever exhibition, Liminal, at the Punta della Dogana in Venice.

On Sunday, the New York Times reported that Samsung, which makes more smartphones than any other company, has considered switching its devices' default search engine from Google to Bing, thanks in part to the excitement around Bing's AI features. The Times reported that threat sparked "panic" at Google, whose name is synonymous with online search.

My last blog discussed AI innovations in the health care sector, and this one will share a few perspectives of new developments in this industry. According to Business Wire, the Artificial Intelligence (AI) spend in media and entertainment industry in the United States forecast period (2019-2025) is expected to grow at a CAGR record of 28.1%, increasing from US$ 329 million in 2019 to reach US$ 1,860.9 million by 2025. Some of the top application areas used in this sector are: gaming, fake story detection, plagiarism detection, personalization, production planning and management, sales and marketing and talent identification. One of the areas which is very exciting is understanding how AI is being used in news. AI is making major impacts in aggregating massive data analysis of the conversations across the world-wide web and classifying into themes to appreciate topics trending globally or even identify increasing risks of terrorism or even health risks, like a pandemic.

Earlier this year, my co-author, Malay Upadhyay, and I released our new book, The AI Dilemma, a leadership guide to assess enterprise AI maturity and explore AI's impact in diverse industries. Over the past year, during COVID-19, I have been writing a series of blogs for CEOs and Board Directors and executive leadership teams to advance their knowledge in AI having develop a comprehensive AI set of skills that I called The Brain Trust. Over fifty skills were defined with examples of AI innovations. The response has been overwhelmingly positive. Why I have been writing these blogs is to drive an increased sense of urgency of board directors and leadership teams to internalize that human civilization as we know it is changing at a clip that is unprecedented in human history.

Some call it the "AI dilemma:" Companies recognize the importance of incorporating artificial intelligence into their business models, but only one-third of their projects are successful. Some 96% of organizations face data-related problems including silos and inconsistent datasets, according to a Tuesday report from Databricks. The data issue can also lead to interpersonal conflict in the workplace, with 80% of the 200 IT executives surveyed citing that there was friction or lack of collaboration between data scientists and data engineers. Some 90% of respondents noted that unifying the data scientists and data engineers could help solve the AI dilemma, according to the report. SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research) These data-related challenges are driven by new machine learning tools, and technology and organizational silos, the release noted.

Today, only one in three AI projects are succeeding, and, perhaps more importantly, it is taking businesses more than six months to go from concept to production, according to Databricks. The primary reasons behind these challenges are that 96 percent of organizations face data-related problems like silos and inconsistent datasets, and 80 percent cite significant organizational friction like lack of collaboration between data scientists and data engineers. IT executives point to unified analytics as a solution for these challenges with 90 percent of respondents saying the approach of unifying data science and data engineering across the machine learning lifecycle will conquer the AI dilemma. The survey, conducted by IDG, surveyed 200 IT executives at larger companies (1000 employees) across the U.S. and Europe. So, what will help these organizations conquer the AI dilemma?