AI-Assisted Cloud Migration Planning

Use AI-powered discovery tools: AWS Application Discovery Service, Azure Migrate, CloudScape. Scan network to identify: servers, databases, applications, dependencies, traffic patterns. Generate complete infrastructure map. Run agent-based discovery for at least 2 weeks to capture intermittent workloads and batch jobs that only run monthly. Cross-reference automated discovery with manual interviews — IT teams in ASEAN often maintain undocumented shadow systems that discovery tools miss. Document licence dependencies (Oracle, SAP) that affect migration economics.

AI analyzes each workload and recommends strategy: Rehost (lift-and-shift), Replatform (optimize for cloud), Refactor (rebuild cloud-native), Retire (decommission), Retain (keep on-prem). Scores by effort, risk, and TCO savings. Apply the 6R framework (Rehost, Replatform, Refactor, Repurchase, Retire, Retain) but weight decisions by business criticality, not just technical complexity. For ASEAN operations, factor in data residency requirements — Singapore's PDPA, Indonesia's PDP Law, and Thailand's PDPA all have cross-border data transfer restrictions that affect architecture choices.

AI suggests target architecture: serverless vs. containers vs. VMs, database choices (RDS, DynamoDB, Aurora), networking (VPC setup), security (IAM, encryption). Generates IaC templates (Terraform, CloudFormation) as starting point. Request AI-generated architectures for at least two cloud providers to avoid vendor lock-in analysis paralysis. For cost-sensitive ASEAN mid-market companies, compare managed Kubernetes vs. serverless — serverless often wins on TCO for variable workloads under 10,000 requests per minute. Review IaC templates thoroughly; AI-generated Terraform often misses IAM least-privilege principles.

AI prioritizes migration waves: start with low-risk, low-dependency workloads. Identifies blockers and prerequisites. Estimates timeline and costs for each wave. Suggests parallel vs. sequential migrations. Flags high-risk areas for extra validation. Schedule migration waves around business cycles — avoid migrating during month-end close, year-end reporting, or regional holiday peaks (Ramadan, Chinese New Year, Songkran). Assign a rollback owner for each wave with pre-tested rollback procedures. Budget 30% contingency on both timeline and cost for the first two waves.

Use AI to simulate migration: test failover scenarios, estimate downtime, identify rollback points. Run pilot migrations in isolated environments. Validate cost estimates against actual usage. Refine plan based on learnings. Run load tests simulating 2x peak traffic to validate that the cloud architecture handles growth, not just current demand. Test failover and disaster recovery scenarios during simulation — a migration plan without a tested rollback is a one-way door. Validate latency from ASEAN user locations to the target cloud region; a 200ms increase in response time can significantly impact user experience.