AI Podcast Interviews Articles and Reports
Our podcast interviews feature the best and brightest executives and researchers in artificial intelligence today, and each episode highlights current and near-term AI use-cases of value for business leaders. Explore our full list of AI podcast episodes below:
COVID-19 did not just bring waves of diseases and contagious variants across the world starting in early 2020 – a tsunami of fraud soon followed the broad sweep of a pandemic that would engulf the global insurance industry.
Episode summary: In this episode, we speak with Alan O'Herlihy, Founder and CEO of Ireland-based Everseen. Alan speaks to us about how machine vision systems can be used to detect theft or mistakes at a checkout counter (including forgetting to scan items, customers intentionally hiding items, and more). Alan not only explains where these technologies are in use today, but he also breaks down some of his own predictions about what these computer vision systems might make possible in the workplace of tomorrow.
As AI automation (aka, “intelligent automation,” or IA) in financial services quickly becomes mainstream, it attracts increased stakeholder interest as firms explore the possibility of unlocking value via increased efficiency, cost reduction, and enhanced predictive capabilities.
While we’ve covered a variety of use-cases in heavy industry over the years on the AI in Business podcast, we are now taking a closer look at what it is like to apply AI on the manufacturing floor on today’s episode.
Finding effective AI projects in the enterprise can be a challenge – but ensuring that AI projects lead to a long-term advantage for the organization can be even harder. Too many dollars in AI investment still end up going to surface-level projects that can't deliver, while a growing number of AI projects are reaching maturity.
In the enterprise world, more and more companies are crossing the chasm to test, and then deploy, their first AI solutions. To navigate this sometimes unfamiliar territory, enterprise leaders increasingly scrutinize the AI project selection process.