This embed requires accepting cookies from the embed's site to view the embed. Activate the link to accept cookies and view the embedded content. AI is a transformational technology impacting organizations across various industries--from providing new insights to improving operational efficiency to enhancing customer experiences. At Microsoft, we're working to turn meaningful innovation into actionable results. We asked thought leaders inside and outside of Microsoft about the acceleration and impact of AI for businesses. Laura Edell, Chief Data Scientist at Microsoft US; Kirk Borne, Chief Science Officer at DataPrime, Inc.; and Pascal Bornet, Chief Data Officer at Aera Technology, came together to share their perspectives on the top AI themes for businesses in 2022.

It's commonly known that AI feeds on data, yet the importance of data is often underestimated. "The scope and scale of the data problem in AI is far larger than most people realize," explained Jen Snell, vice president of Verint, a chatbot development company. "So many organizations run into problems with their projects due to data -- from data quality to managing and wrangling data for meaningful insights to labeling and model building," she said. "In the beginning, it seems easy, but as you look to grow at scale, change models, manage and ensure control over the system, it gets tricky." Snell shared a painful statistic: While 59% of executives believe AI could improve their use of big data, 85% of big data or AI projects fail.

Veritone's Aaron Edell explains how data scientists can best harness artificial intelligence and machine learning. Data science, artificial intelligence (AI) and machine learning (ML) are all massive areas that are undergoing growth in the tech industry and attracting increasing amounts of attention. But what about the jobs of the future that will combine all three? Although there are plenty of possibilities to consider in this area, his main advice is to always maintain sight of the problem that needs to be tackled and keep the customer in mind. With machine learning, business process scalability has made leaps and bounds, but it's important not to get side-tracked by that, according to Edell.

Siliconrepublic.com spoke to Veritone's Aaron Edell to learn how data scientists can harness AI and machine learning. Data science, artificial intelligence (AI) and machine learning (ML) are all massive areas undergoing further growth and attracting increasing amounts of attention. But what about the jobs of the future that will combine all three? Edell's prevailing advice for data scientists working with AI and machine learning technologies is to always maintain sight of the problem they're tackling. With machine learning, business process scalability has made leaps and bounds, but it's important not to get side-tracked by that.