WATCH: Democratize AI- Engineering a product mindset for an autonomous platform.

Solve key challenges in adopting AI at your enterprise and discover what’s fueling AI growth industry-wide. Brillio executives Chander Damodaran and Paulami Das discuss how a product mindset leads to building AI at scale.

Data velocity and integration challenges

Chander Damodaran and Paulami Das focus in on the wave of digital transformation that's truly fueling growth for enterprise. Coupled with that enterprise growth is a breakneck velocity of data flooding the organization from inside and out.

Pushing and pulling all of that data both internally and externally is leading to myriad integration and integration related challenges. And all of this requires us to slice through this data, look at all of these in the right lens.

Prejudice and decisioning

Enterprises are doing their best to understand what their data means and how they can improve the internal enterprise experience as well as the customer experience. There’s truly been a fundamentally shift from a “leadership led decisioning process to a data led decisioning.”

Human prejudice plays a big, big role in adoption thus AI is at best has been adopted as a pilot. It is done in smaller silos or it is not something that is truly institutionalized and taken at scale.

The four key steps to democratizing AI

• Examining next generation tools
• Realizing the emergence of standards and common frameworks
• Examining a platform centric approach,
• Conceiving of the dimensions that truly drives AI at scale

9 select key takeaways

• The adoption of open source and open standards is truly the driving force to start building the AI-based enterprise.
• Ease of accessibility, reduced friction and higher adoption of AI leads to your up-scaling opportunities
• Don't get locked into specific tools. Technologies keep evolving- do not base your AI journey based off a single tool.
• Collaboration needs to drive your culture change
• Have a center of excellence that is the true governing body that starts building these models
• CoE experience allows you to deploy AI models at scale in your environments
• Today the quantity or availability of data is not an issue, the issue is figuring out what data is valuable.
• To enable AI at scale, organizations need to change their operating model through the adoption of intelligent architecture, which is geared towards scaling AI, end-to-end management of workflow and an organizational structure that enables understanding and adoption of AI.
• It's essential to see the whole benefit and have an understanding of AI from your C-Level management to the layer that executes the projects.
• Everybody in the organization must know AI at the level that it is relevant to that particular function or execution.