Infosys' Anant Adya: Cloud data and AI pipelines speed development as BFSI, healthcare, and government chase scale and innovation

Cloud-based data and AI pipelines are making development easier - here's how to use them now

Infosys' Anant Adya put it simply: cloud-based data and AI pipelines are making development easier. BFSI, healthcare, and government are out front because scale and constant innovation aren't optional-they're survival.

If you build or run software, this isn't theory. It's a practical shift in how teams ship faster with fewer brittle parts. Below is a playbook you can lift and adapt.

What "cloud data + AI pipelines" looks like in practice

• Unified data layer: A lakehouse pattern across object storage plus a query engine. One place to land raw, curated, and feature data with clear zones.
• Managed services first: Use built-in storage, queues, streams, schedulers, registries, and vector indexes before rolling your own.
• DataOps/MLOps as CI/CD: Version everything (schemas, transforms, features, models). Automate testing, rollout, and rollback.
• Feature and prompt assets: Centralize feature definitions, embeddings, and prompt templates to avoid one-off pipelines.
• Observability: Lineage, freshness, drift, and cost telemetry baked in from day one.
• Governance-by-default: PII tagging, encryption, access policies, and audit trails at the platform layer.

Why BFSI, healthcare, and government are ahead

• Scale volatility: Payments, claims, and citizen services spike. Elastic data + compute smooths demand without rewrite cycles.
• Compliance pressure: Rules force automation, traceability, and least-privilege access-exactly what good pipelines deliver.
• Legacy constraints: You can't freeze the core. Pipelines let you wrap, stream, and extend without high-risk big-bang swaps.
• User expectations: Real-time status, faster decisions, and accurate answers-AI-powered services meet those bars.

A reference blueprint you can ship

• Sources: Core apps, logs, relational DBs, EHR/claims, payments, forms, partner APIs.
• Ingestion: CDC and event streams; enforce data contracts and schema registry.
• Storage: Lakehouse tiers (raw, refined, feature). Keep compute separate from storage.
• Transform: Declarative pipelines (SQL + code). Unit tests on data quality and SLAs on freshness.
• ML/RAG: Training pipelines, model registry, feature store, and vector index for retrieval.
• Serving: Real-time APIs, batch jobs, and LLM endpoints behind gateways with quotas.
• Feedback: Capture labels, ratings, and errors to auto-create retraining/retuning tickets.
• Observability: Data lineage, model metrics, prompt tokens, latency, and per-team cost.

90-day rollout plan (small team, big impact)

• Weeks 1-3: Pick one use case with clear ROI (e.g., KYC document triage, claims routing, citizen query bot). Set up VPC, IAM, secrets, and data contracts. Land 2-3 critical sources.
• Weeks 4-6: Build transforms with tests. Stand up a feature store and a vector index. Define model registry and promotion rules.
• Weeks 7-9: Ship a baseline model or RAG pipeline behind an API. Add monitoring for drift, latency, and cost. Wire feedback loops.
• Weeks 10-12: Harden RBAC, key rotation, retention, and backup. Add canary deploys and rollback. Document runbooks. Review costs and set budgets.

Compliance and security, simplified

• Classify and tag PII/PHI: Automate detection; block unsafe egress.
• Encrypt everywhere: In transit and at rest with managed KMS; rotate keys.
• Data retention: Partitioned deletes and TTLs; prove erasure on request.
• Lineage and audit: Who touched what, when, and why-kept for the required period.
• Model governance: Document datasets, tests, risks, and approval gates before production.
• External AI use: Gate third-party LLMs through a proxy with PII scrubbing and spend limits.

Helpful frameworks: the NIST AI Risk Management Framework and India's Digital Personal Data Protection Act.

Metrics that matter

• Data: Freshness SLA, validation pass rate, lineage coverage.
• Models/LLMs: Latency p95, quality score vs. golden sets, drift rate, token spend per request.
• Pipelines: Success rate, time to recover, change failure rate.
• Business: Cost per decision, straight-through processing rate, deflection rate for support.

Common pitfalls to avoid

• Tool sprawl: Standardize on a minimal stack; add tools only for clear gaps.
• No data contracts: Schema breaks ripple. Contracts and versioning prevent surprise outages.
• Mixed environments: Separate dev/stage/prod with clear promotion paths.
• Unbounded LLM spend: Rate limits, caching, and prompt budgets protect wallets.
• Ownerless platforms: Assign a platform team and product manager with a backlog.

Where to upskill your team

If you're building pipelines, upskilling pays for itself. Curated paths for engineers and data teams can help you move faster without guesswork. Explore focused tracks by role here: AI courses by job.

Bottom line: Adya's point holds. Move your data and AI lifecycle onto the cloud, enforce contracts and governance, and ship small, fast iterations. You'll spend less time firefighting and more time delivering features users actually feel.