Readiness

Question	Example response
Do you have AWS accounts that can be leveraged for these workloads?	Yes or no.
Do you have an existing enterprise agreement with AWS?	Yes or no.
How scalable is your current cloud infrastructure to handle generative AI workloads?	Our cloud infrastructure is highly scalable, with automatic scaling capabilities for compute resources and distributed storage systems that are designed to handle large-scale generative AI workloads efficiently.
Do you have data pipeline capabilities for preprocessing and feature engineering at scale?	Our data pipelines use distributed processing frameworks such as Apache Spark for large-scale data preprocessing and feature engineering, with support for both batch and streaming data processing.
Do you have account provisioning and management capability?	Yes or no.
How would you describe your organization's AI literacy and readiness to adopt generative AI technologies?	Our organization has invested heavily in AI education programs, and most technical staff has completed basic AI/ML training. The organization has a culture of innovation that embraces new technologies, including generative AI.
What AI/ML expertise exists within your organization, and how is it distributed?	We have a dedicated AI Center of Excellence with experienced data scientists and ML engineers. We upskill domain experts across different business units to become AI-literate and to identify generative AI use cases.
Do you have a high-level business case that articulates the cloud program objectives, benefits, and cost?	Yes or no.
What is your time line to take the solution to production?	Weeks, months, and so on.
Has a funding commitment been made by your key stakeholders (for example, CFO, CIT/CTO, COO)?	Yes or no.
How do you ensure compliance with data protection regulations in your generative AI initiatives?	We have a dedicated compliance team that works closely with our AI teams. We conduct regular privacy impact assessments, implement data protection by design principles, and maintain detailed data processing records for all generative AI projects.
How mature are your existing systems that integrate with new generative AI technologies?	Our IT architecture is based on microservices and APIs that allow for flexible integration of new generative AI technologies. These systems are standardized on common data formats and protocols to ensure interoperability.
What experience do you have in operationalizing ML models, and how might this apply to generative AI systems?	We have established MLOps practices, including automated model deployment pipelines, monitoring systems, and A/B testing frameworks. These practices are being adapted to handle the unique requirements of large-scale generative AI models.