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Effective AI Scaling Starts With AI Governance

David Talaga

One of the primary challenges organizations face on their scaling AI journeys is a lack of operationalization and business impact. An O’Reilly survey revealed that only one quarter of respondents described their use of AI as “mature” and, often, this has to do with a significant number of machine learning models not making it into production.

As we mentioned in our webinar on safely scaling AI successfully, organizations can embed risk management and responsible practices, ensure those are translated into effective operations, and instill confidence across an organization that management and operations are effectively overseen and monitored. Well, that’s probably much easier said than done. So what does it look like in practice?

Download Now: How to Safely Scale AI With Oversight

The Road to Operationalized Analytics & AI Project Governance

Let’s back up: AI can very much be like Pandora’s box if organizations don’t know what they’re doing and it’s only with AI Governance, Responsible AI, and MLOps that they will effectively be able to de-risk what comes out of that box. These risks might be deviations from internal policies — think ethical frameworks or business strategies — or external standards and regulatory frameworks. But if organizations aren’t prepared to conform to these requirements, they can quickly face massive risk in the form of pulled deployments, monetary penalties, or PR backlash. 

So, at Dataiku, we believe that the only way to scale AI is to build an effective AI Governance program, where “effective” means that individuals building AI projects aren’t encumbered by or worrying about risk themselves because, by definition, they’re working in a governed way. A strong AI Governance framework should:

  • Centralize, prioritize, and standardize rules, requirements and processes aligned to an organization’s priorities and values
  • Inform operations teams, giving build and deployment teams the right parameters to deliver
  • Grant oversight to business stakeholders so they can ensure that AI projects (with or without models) conform to requirements set out by the company

3 steps to AI Governance

Ultimately, because projects are governed, they are controlled, approved, and explained, freeing stakeholders from concerns about risk exposure and the questions-as-impasse, “Can I do this? Did I do this right?” They will now be able to focus on researching and developing efficient models without sacrificing breach of internal or external requirements and concerns about related penalties that are counterproductive to scaling broadly (and use case deployments in particular). 

For a quick and dirty overview of the differences between AI Governance, MLOps, and Responsible AI adoption, check out the video below: