Monetization Strategies for AI Engines and Applications
Monetization Strategies for AI Engines and Applications


The explosion of narrowly focused, highly specialized cognitive engines combined with ever increasing sources of unstructured data will require novel approaches to fully realize the future of artificial intelligence. We believe the best business strategies, applicable to virtually any industry, organization, or input type will include the following key components:

Aggregation of specialized cognitive engines in a diverse ecosystem
Global standards for structured metadata formats
Openness of large data sets to enable more efficient and effective model training
Simplicity of access and use for end users via SaaS platforms
Continuously learning orchestration systems
Mainstreaming of data science methods, best practices, and frameworks
A spectrum of deployment from centralized (cloud) to highly decentralized (Edge/IoT)
The melding of hardware and software
Engines building engines
Narrow accuracy to contextual accuracy to understanding of intent

What does this mean to Data Scientists and Algorithm developers? Now you have the ability to propel AI forward faster than ever, addressing specific market needs and identifying new opportunities for monetization.


Allen Kim is Senior Director of Product Management at Veritone, where he’s responsible for the Veritone Developer application. Veritone Developer allows developers to build artificial intelligence powered engines, applications, and data products for the enterprise and government markets. Prior to joining Veritone, Allen built data science tools and teams to solve problems in the higher education, finance, and defense industries. Allen studied at Carnegie Mellon University and builds robots with his kids in his spare time.

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