درباره این اپیزود
Enterprise AI is moving past retrieval and into prediction. In this episode of AI Cloud Essentials, host Ritu Jyoti sits down with Ben Turtel, CEO and founder, to unpack why the next generation of enterprise models needs to understand which signals actually lead to which outcomes - not just memorize company information.
Ben explains how predictive analytics reframes everyday business choices: every enterprise decision is a prediction about what will happen next. From financial services use cases like forecasting earnings surprises from SEC filings to private equity and sales predictions, the conversation explores why outcome-oriented models require the right data, the right training approach, and infrastructure that can move as fast as the experiments demand.
The episode also gets practical about what slows AI teams down: access to proprietary data, the need to containerize full stacks, the cost of training runs, and the importance of being able to spin GPUs up and down quickly. This conversation is for enterprise AI leaders, technical founders, and infrastructure teams who are deciding whether their AI strategy is still searching for information - or starting to reason like an expert.
What you'll take away:
Why every enterprise decision can be understood as a prediction
How models can learn which signals and factors lead to which outcomes
Why access to proprietary data is often the biggest friction point for enterprise AI
How containerized small-model architectures can help teams run closer to customer data
Why the enterprise needs to move from retrieval toward trained experts that know what matters
Learn how predictive AI changes the enterprise question from "where is the information?" to "what is likely to happen next?"
