AI readiness checklist

AI • AWS March 18, 2026

following is the AWS ai readinees checklist for the orgranization

DomainQuestion IDOfficial AWS Assessment QuestionAWS Example Target / Guardrail ResponseStatus (Drop-down)Priority (H/M/L)Action Item / Owner
1. Readiness & OrgRE-01Do you have AWS accounts that can be leveraged for these generative AI workloads?Yes / No
1. Readiness & OrgRE-02Do you have an existing enterprise agreement with AWS?Yes / No
1. Readiness & OrgRE-03Do you have account provisioning and management capability?Yes / No
1. Readiness & OrgRE-04How scalable is your current cloud infrastructure to handle generative AI workloads?Our cloud infrastructure features automatic scaling capabilities for compute resources and distributed storage systems designed to handle large-scale workloads efficiently.
1. Readiness & OrgRE-05How would you describe your organization’s AI literacy and readiness to adopt generative AI technologies?Organization has invested in AI education programs; most technical staff completed basic AI/ML training. Culture of innovation embraces generative AI.
1. Readiness & OrgRE-06What AI/ML expertise exists within your organization, and how is it distributed?Dedicated AI Center of Excellence (CoE) with data scientists/ML engineers, alongside upskilled domain experts across business units.
1. Readiness & OrgRE-07Do you have a high-level business case that articulates the cloud program objectives, benefits, and cost?Yes / No
1. Readiness & OrgRE-08What is your timeline to take the solution to production?e.g., Weeks, months, or quarters.
1. Readiness & OrgRE-09Has a funding commitment been made by your key stakeholders (for example, CFO, CIO/CTO, COO)?Yes / No
1. Readiness & OrgRE-10What experience do you have in operationalizing ML models, and how might this apply to generative AI systems?Established MLOps practices (automated deployment pipelines, monitoring, A/B testing) being adapted to handle unique LLM requirements.
2. Use CasesUC-01What are the primary use cases or scenarios for the generative AI solution?e.g., Customer service chatbot, dynamic product copy generation, feature recommendations.
2. Use CasesUC-02What are the target users or personas for the generative AI system?e.g., Customer service agents, marketing team, end users.
2. Use CasesUC-03What are the key performance requirements (for example, response time, throughput, accuracy)?e.g., 95%+ accuracy; < 500 ms response time; ability to handle 1000 requests/sec.
2. Use CasesUC-04Do you have any other KPIs to measure the success of this use case?e.g., Reduction in support tickets, lift in conversion rate, user satisfaction score.
2. Use CasesUC-05Do you have an allocated budget for developing and maintaining the generative AI solution?Explicit initial development budget and recurring annual maintenance tracking.
2. Use CasesUC-06What is the projected return on investment (ROI) and payback period for this use case?Calculated target ROI percentages and target payback duration (e.g., 12–18 months).
2. Use CasesUC-07Are there any hidden costs or potential savings that should be considered?e.g., Savings on manual operations vs. hidden costs of continuous token consumption and developer upskilling.
2. Use CasesUC-08What are the scalability and future expansion possibilities of this generative AI solution?Designed to scale with operations, with modular expansion capacity for other product branches.
3. Data StrategyDS-01Do you have data pipeline capabilities for preprocessing and feature engineering at scale?Our pipelines use distributed frameworks (e.g., Apache Spark, AWS Glue) supporting batch and streaming.
3. Data StrategyDS-02What data formats do your generative AI models require as input?e.g., Structured (CSV, JSON, SQL); text (plain text, PDF, HTML); image (JPEG, PNG); audio/video.
3. Data StrategyDS-03What are your key data quality concerns for generative AI workloads?Focus areas: Completeness, accuracy, consistency, timeliness (freshness), and relevance.
3. Data StrategyDS-04How do you ensure data quality and consistency across different sources for generative AI training/context?Maintained via automated data profiling tools, regular data audits, a centralized data catalog, and data lineage tracking.
4. Architecture & StorageAR-01How mature are your existing systems that integrate with new generative AI technologies?IT architecture is standardized on microservices and APIs with common data formats to ensure interoperability.
4. Architecture & StorageAR-02Which model hosting strategies are under consideration?e.g., Managed foundational APIs (Amazon Bedrock) vs. self-hosted custom models (Amazon SageMaker).
4. Architecture & StorageST-01What storage mechanisms are selected for transactional, historical, and vector data?e.g., Amazon OpenSearch Service / pgvector for embeddings; Amazon S3 for unstructured data lakes.
5. Regulations & ComplianceCO-01How do you ensure compliance with data protection regulations in your generative AI initiatives?Dedicated compliance team conducting regular privacy impact assessments, implementing data protection by design, and audit logging.
5. Regulations & ComplianceCO-02Have you reviewed the LLM provider’s data governance policies?Confirmed with vendor (e.g., AWS Bedrock) that application data/prompts are not used to train base models.
5. Regulations & ComplianceCO-03How do you manage ethical concerns, fairness, and mitigate bias in your generative AI models?Managed through an established AI incident response plan, regular ethical risk assessments, anonymous reporting, and post-deployment monitoring.
6. Integration & DeploymentIN-01How will the generative AI features interact with your core application backend?Via API gateways, message queues, or event-driven layers to prevent user latency spikes.
6. Integration & DeploymentDE-01What automated processes are required for model deployment and operations (LLMOps)?CI/CD automation pipelines for prompt versioning, continuous integration, and safe canary deployments.
7. Testing & EvaluationTE-01How will the generative AI model be evaluated and validated?By utilizing a holdout golden dataset, automated evaluation metrics, and A/B testing.
7. Testing & EvaluationTE-02What are the criteria for evaluating the performance and accuracy of the generative AI model?Evaluation via precision, recall, F1 score, perplexity, and mandatory human-in-the-loop validation.
7. Testing & EvaluationTE-03How will edge cases and corner cases be identified and handled?Comprehensive adversarial test suites (red teaming) and automated fallbacks to static applications.
7. Testing & EvaluationTE-04How will you test for potential biases in the generative AI model?Utilizing demographic parity analysis, equal opportunity testing, adversarial de-biasing, and counterfactual testing.
7. Testing & EvaluationTE-05Which process will be implemented for ongoing monitoring of model fairness post-deployment?Regular fairness audits, automated bias detection systems, and user feedback analysis loops.
7. Testing & EvaluationTE-06How will you address intersectional biases in the generative AI model?Intersectional fairness analysis and subgroup testing during pre-release validation.
7. Testing & EvaluationTE-07How will you test the model’s performance across different languages and cultural contexts?Multilingual validation test sets and cross-cultural comparison studies.

and Calculate your score by this,

Assessment FieldInput / FormulaNotes
Product / AI Feature Name:[Enter Feature Name]e.g., “AI Copilot Feature Launch”
Total Questions:40Total questions across all categories
Questions Answered:=COUNTA(‘Assessment Questionnaire’!E2:E41)Tracking progress
Overall Readiness Score:=AVERAGE(‘Assessment Questionnaire’!F2:F41)Calculates overall maturity (Scale 1-5)
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