This report identifies the specific gaps in your AI deployment lifecycle based on your responses — designed to surface where your next initiative is most likely to stall.
Deployment readiness across three phases
2.1
Use Case &
Data Readiness
Critical
3.0
Deployment &
Testing
Significant
2.2
Governance &
Monitoring
Critical
Your three highest-risk deployment gaps
The gap
Use cases are approved and builds begin before confirming whether the data exists, is of sufficient quality, or carries valid consent. Problems surface mid-build — after resources and timelines are already committed.
Consequence
Builds stall or proceed on unsuitable data, producing unreliable models. This is the most common reason AI pilots at Indian banks do not reach production — not the model, the data layer beneath it.
What good looks like
A formal data readiness gate — availability, quality, lineage, and consent confirmed — is mandatory before any use case enters build. No exceptions.
The gap
Horizon Bank cannot list all AI models currently in production — their owners, last validation dates, or data sources. Regulatory requirements are addressed only when an audit or incident forces a review.
Consequence
Models degrade silently and regulations update without anyone tracking the exposure. Without an inventory the organisation cannot identify which models are at risk. Post-hoc remediation after a regulatory finding costs significantly more than designed-in governance.
What good looks like
A maintained model inventory with named owners and review dates, and a proactive regulatory calendar aligned to DPDP Act and RBI FREE-AI update cycles.
The gap
Testing practices are reasonable but the handoff from build team to operations is verbal and undocumented. The build team exits at go-live leaving operations without a runbook, monitoring setup, or escalation path.
Consequence
Technical successes at go-live fail quietly six months later. Model drift is invisible until it becomes a customer complaint or operational incident. The 33% of organisations that reach production but cannot sustain deployment share this exact profile.
What good looks like
A formal handover document — technical, governance, and monitoring responsibilities — with a named accountable owner signed off before every go-live.
What these gaps mean together
These three gaps form a single connected failure. Horizon Bank is committing to AI builds without confirming the data is ready. When models do reach production, the handoff to operations is informal and the governance infrastructure needed to sustain them does not exist. Nobody owns the deployment end to end. Nobody can list what is running or when it was last validated.
For a bank operating under RBI FREE-AI and DPDP Act obligations, these are not just operational gaps — they are live regulatory exposures. A model risk review conducted today would produce findings across all three clusters. Addressing these now, before the next deployment cycle, costs significantly less than addressing them after a regulatory event.
How DeployReady addresses this
1
About this diagnostic
This is DeployReady's AI Deployment Readiness Check — a 15-question quick diagnostic that surfaces critical gaps in your deployment lifecycle. A more comprehensive facilitated assessment is available for organisations that want deeper analysis across all deployment dimensions.
2
Capability Building — 8 to 12 weeks
DeployReady trains practitioners responsible for each of these gaps — use case selection, data readiness, deployment, testing, governance, and change management — simultaneously. India-specific: DPDP Act, MeitY Guidelines, and RBI FREE-AI obligations embedded at practitioner level throughout. The goal is the capability to move an initiative from pilot to production.
3
Post-Programme Monitoring Support
Deployments do not end at go-live. Post-programme support covers model drift monitoring, governance currency as regulations update, and a scaling playbook that prevents these gaps from reappearing in subsequent initiatives.
To discuss these findings — book a 30-minute conversation at deployready.ai. We will map your specific deployment context and tell you where to start.