We are joined today by Veritone’s own Head of Engineering & Product, Chris Doe, to discuss AI applications in enterprise and what is top of mind for companies when it comes to incorporating these emerging technologies. In our conversation, Chris breaks down how Veritone is helping its clients navigate the expansive landscape of AI cognitive engines and why now is the ideal time to test and define proof of concept for generative AI.
Creating AI workflows can be a challenging process. And while purchasing these types of technologies may be straightforward, implementing them across multiple teams is often anything but. That’s where a company like Veritone can offer unparalleled support. With over 400 AI engines on their platform, they’ve created a unique operating system that helps companies orchestrate AI workflows with ease and efficacy.
Chris discusses the differences between legacy and generative AI, how LLMs have transformed chatbots, and what you can do to identify potential AI use cases within an organization. AI innovations are taking place at a remarkable pace and companies are feeling the pressure to innovate or be left behind, so tune in to learn more about AI applications in business and how you can revolutionize your workflow!
Key Points From This Episode:
- An introduction to Chris Doe, Product Management Leader at Veritone.
- How Veritone is helping clients orchestrate their AI workflows.
- The four verticals Chris oversees: media, entertainment, sports, and advertising.
- Building solutions that infuse AI from beginning to end.
- An overview of the type of AI that Veritone is infusing.
- How they are helping their clients navigate the expansive landscape of cognitive engines.
- Fine-tuning generative AI to be use-case-specific for their clients.
- Why now is the time to be testing and defining proof of concept for generative AI.
- How LLMs have transformed chatbots to be significantly more sophisticated.
- Creating bespoke chatbots for clients that can navigate complex enterprise applications.
- The most common challenges clients face when it comes to integrating AI applications.
- Chris’s advice on taking stock of an organization and figuring out where to apply AI.
- Tips on how to identify potential AI use cases within an organization.
“Anybody who’s writing text can leverage generative AI models to make their output better.” — @chris_doe [0:05:32]
“With large language models, they’ve basically given these chatbots a whole new life.” — @chris_doe [0:12:38]
“I can foresee a scenario where most enterprise applications will have an LLM power chatbot in their UI.” — @chris_doe [0:13:31]
“It’s easy to buy technology, it’s hard to get it adopted across multiple teams that are all moving in different directions and speeds.” — @chris_doe [0:21:16]
“People can start new companies and innovate very quickly these days. And the same has to be true for large companies. They can’t just sit on their existing product set. They always have to be innovating.” — @chris_doe [0:23:05]
“We just have to identify the most problematic part of that workflow and then solve it.” — @chris_doe [0:26:20]