
This episode discusses the transition of applied AI from experimental to essential infrastructure in business operations.
This is your Applied AI Daily: Machine Learning & Business Applications podcast. Applied artificial intelligence is moving from experiments to essential infrastructure, and the most successful companies are treating it as an operations and revenue engine rather than a science project. McKinsey estimates that applied artificial intelligence could generate trillions of dollars in annual value, with the largest gains in marketing, supply chain and manufacturing, and software engineering productivity, and those gains are increasingly coming from very specific use cases rather than generic platforms, according to recent McKinsey Global Institute research. In predictive analytics, retailers are using demand forecasting models to cut stockouts and excess inventory by double digit percentages, while banks use machine learning risk models to reduce default rates and speed up credit decisions, as reported by Deloitte and Accenture. In natural language processing, contact centers deploying conversational agents and call summarization are seeing call handling time reductions of ten to thirty percent and measurable boosts in customer satisfaction, according to Salesforce and Gartner. In…
Explore listener stats, chart rankings, contacts and more on the Applied AI Daily: Machine Learning & Business Applications podcast page.