The AI Lab Accelerator

Getting started on an enterprise Artificial Intelligence (AI) journey, experimenting with speed, scaling AI responsibly, and ensuring long-term meaningful adoption is challenging for all organizations. For many of our clients, Generative AI and Agentic AI bring unforeseen risks and confusion as the technology evolves. Successful AI implementations with AWS require educating stakeholders about AI’s potential and risks, developing a strong AI risk assessment framework with the best practices included, ensuring the right AI use-cases are identified and prioritized, and a clear path to production with AWS is established. This is what it takes to ensure successful organizational adoption.

With over a decade of experience in AI (Artificial Intelligence), and being a strong AWS partner, we combine our end-to-end capabilities in AI, our deep domain knowledge and technology engineering, to introduce our AI Lab accelerator. It is designed to be adaptable, flexible and robust to help you move fast, yet have a structured AI governance approach throughout.

It is designed to be a set of nine steps where CGI will be a strong partner by your side to help you:

• Choose your AI platform of choice - AWS and/or hybrid cloud
• Create Data Security and AI Risk assessment frameworks
• Create an AI Risk assessment framework
• Vet third-party and cloud AI services against these frameworks to create approved services
• Create an intake process for AI use-cases and use the AI Risk assessment framework to identify top AI use-cases
• Rapidly experiment and responsibly scale the ones with most value
• Create standardized AI and GenAI architectures and re-use them across similar use-cases
• Establish an AI Governance council comprising of key roles from within your organization

Clear benefits of this engagement:

1. Structured, Comprehensive and Collaborative All enterprise stakeholders collaborate successfully to deliver value Defines a clear and structured path to production and ensures higher chances of AI success

2. Scale Responsibly and Re-use Responsible AI, baked in, not bolted on Build and reuse models and zero-trust architectures to minimize time to market and enabling scale

3. AI Governance-first Approach Confidently plan and understand who owns what responsibilities Less friction and faster pace of AI delivery

4. Higher Business Adoption and Lower Risks Use-case prioritization frameworks help select highest value use-cases Privacy, Responsible AI, Legal and Compliance review frameworks help lower risks

In a typical engagement, a CGI squad of AI experts collaborates with your key stakeholders to help establish this AI Lab using AWS and CGI frameworks, following our AI best practices for responsible and ethical development.