It is possible to deliver an AI pilot, but when it comes to delivering it at scale, it often fails. We come face to face with multiple challenges right from signing the deal to not having adequate data to run the project successfully. So, what are those three important pillars to move projects from AI pilots to AI @ scale?
In this podcast, Sankar Narayanan, talks in-depth about the principles you need to build AI @ Scale at any enterprise.
- Regulatory normsThe first thing is to understand what information is available and what amount of information can be used from it. This is always a complicated platform. One approach could be to have a regulator in the room for every meeting when an AI project gets deployed, but then that’s not practical. However, what can be done is, that the project can be constructed in such a way that the person involved in regulatory management can understand it and quickly respond to it, wherever required.As the project reaches set milestones, a quick review with the regulatory team will help to keep them abreast of the progress of the project.
- Technology choicesTechnology choices are not limited to just information technology. It encompasses all the points of connection that supports the project. On one side there could be the opportunity to understand all the data considerations, building the right data pipelines, what data is available, accessible, accurate, and enterprise-ready. Getting this aspect of technology right becomes critical to make a project successful at scale.On the other side, what’s equally important is how does this data get consumed within the workflow of users that are the recipients of the project. Often, the biggest roadblock to scale AI projects and creating ROI of AI is this adoption path. This is a very important part and figuring out the adoption dynamics is to a large extent responsible for the project to be successful at scale.
- The true value of AIThere are two ways to look at this challenge. First to identify how to get the ROI of AI. The other is how to adapt AI in the multiple functions of the organization.While reducing costs of claims, understanding functional metrics is a route that we are aware of, it is the learning of AI that requires work. When an organization adapts AI in its culture and way of working, then the true value of AI is received.To find more on scaling AI projects, hear this podcast and transform your business with AI solutions @scale.