How to Evaluate Gen AI Platforms?

Amidst all the gen AI noise, are you wondering about an optimal strategy to integrate gen AI into your enterprise?

If you haven’t read already, please read my previous blog on considerations and process of gen AI integration in detail. Here in this blog we dive into challenges and solution criteria when adopting gen AI solutions for your company.

Challenges in adopting gen AI solutions

Gen AI can be leveraged for several use cases across departments such as sales, marketing, support, HR, Operations, and IT. It might be easy to build a prototype for a single user, but integrating gen AI at enterprise scale is a different story. There are hundreds of trade-offs here that impact time to market, quality, and cost. We suggest a platform centric approach for adopting gen AI, instead of point solution, while solving for the following challenges.

Fragmentation

If you deploy point solutions for each business unit, it results in higher cost and lower quality due to multiple AI tools and AI silos.

Integration overload

Continuously syncing multiple data sources across tools, databases, and knowledge bases is tedious especially at scale.

Infrastructure complexity

Deploying enterprise grade applications involves building a concrete end to end pipeline and managing various components across the pipeline from data ETL through LLM tuning.

Use case inflexibility 

Getting the gen AI solution to fit your company specifications, multiple use cases, and performance objectives is time consuming.

Compliance breaches

Ensuring that AI adheres to your security and privacy policies is crucial to avoid risks ranging from giving away your data to potential privacy leaks. 

Usability gaps

Every use case needs a different interface and slapping a chatbot might not be the most effective way. The right user experience might be the difference between success and failure in user adoption.

Without proper planning, you might end up spending millions of dollars and months in time to market to get one use case out of the door. Then spending both time and money to keep getting accurate results. And then keep reinventing the wheel for every use case across your company.

Solution evaluation criteria

Here is our list of criteria when evaluating gen AI platforms.

Interoperability

The platform should be agnostic to tools, input formats, LLMs, or cloud environments. This way you can have a centralized platform that can be leveraged to deploy gen AI apps across multiple business units while avoiding AI fragmentation and vendor lock-ins.

Multiple sources

You should be able to connect and sync from a variety of sources ranging from your existing tools to databases with a click without re-inventing data connectors and ETL.

Modularity

You should be able to pick and choose building blocks that enable you to get the best outcome for your specific use case, instead of building a monolith infrastructure from scratch.

Customizability

The platform should provide customization options to suit your specific use cases, performance objectives, and design.

Security

The platform should support highest grade security and privacy options including private cloud deployments, privacy policy adherence, and AI governance workflows.

Multi-faceted interface

You should be able to choose multiple interfaces such as chatbot, widget, plugin, or API to suit your specific user needs.

Have you found a platform that meets the above criteria? Well, we are building one to empower businesses to build generative AI use cases at 10x speed. Stay tuned for more information!

Conclusion

Gen AI can be leveraged for several use cases across departments such as sales, marketing, support, HR, Operations, and IT. Hence a platform centric approach is a better strategy than a point solution. However when evaluating gen AI platforms, companies need to ensure it is interoperable, supports multiple sources, modular, customizable, secure, and supports multiple interfaces.