91ÖÆƬ³§

The lightning-fast evolution of the AI landscape makes it a challenge for IT to integrate new AI systems and use cases into existing infrastructure. Our research provides a foundational framework to help you select the right architectural components for your AI solutions. Build an AI architecture that is flexible enough to handle the ever-changing demands of AI, scales up to the demands of the organization, and ensures your AI architecture is set up for success.

Though organizations have embraced the potential of AI to deliver transformational solutions, they must be cognizant of their unique problems and needs, or risk poor or improper implementation that would be harmful and costly to fix. When deciding what AI solutions to build or buy, organizations must relate that choice back to the kind of architecture that would be needed to support them and ensure each stage of building that architecture is given due care.

1. Don’t build it all in one piece.

Rather than treating their AI platform as a monolith requiring major overhauls with each new implementation, architects must think of the system in terms of its individual building blocks and the way they relate to each other. The result will be an adaptable platform that scales easily and allows seamless integration of components as new use cases arise.

2. AI is not always the answer.

Though AI is often presented as an all-encompassing revolution, the truth is that not all problems require an AI solution. First determine how the AI use case will generate organizational value before committing to full-scale implementation.

3. Take the time to do it right.

AI implementation can be a complex undertaking – rushing any part of it raises the risk of costly mistakes. Plan to build your target state architecture in phases, with clear milestones and regular check-ins on your AI system, using KPIs like model accuracy, deployment success rates, and system uptime.