Operational AI: Achieving Next-Level Value From AI
Technology research firm Gartner has estimated that as much as 85% of all AI and ML projects fail to produce a return for the business. One major reason that organizations struggle to achieve value from AI projects is that, after having invested in data science to create innovative ML models, they fail to adopt the processes, team, and tools to effectively put models into a production environment where they can deliver value.
To avoid this trap and achieve greater value from AI, businesses must stop thinking of models as an end on their own. A model is just a way to transform data written in the form of a function — similar to software. Making the transition from “artisanal” data science to “industrial” ML at scale requires organizations to adopt an Operational AI mindset along these main dimensions:
- People: An ML Platform team responsible for managing the tooling and process for operationalizing models.
- Process: Standardized processes and best practices for putting models into production.
- Technology: An Operational AI platform that specifically supports the requirements for operationalizing, managing and monitoring models in production.
By adopting a software mindset around ML and putting in place the team, processes and tools to safely and efficiently deploy ML models, businesses can significantly increase AI velocity, reduce MLOps cost of ownership, improve data science productivity, and deliver greater value from their AI innovations.
Get Your Guide to Operational AI
What does it take to become an AI-forward organization? Read the Guide to Operational AI and learn how to adopt an Operational AI mindset, including: A roadmap for Operational AI, the people & technology you need, and a reference architecture for AI.
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