AI Production Governance Toolkit
Your AI model is live in production. Is the governance still working?
The AI Production Governance Toolkit (v2.0) is a Microsoft Excel workbook produced by Viksya for AI programme teams, governance offices, and management consultants. It provides two linked governance modules in a single file: a 19-criterion pre-deployment gate assessment across five dimensions that produces a weighted Gate Score and a formal Go/No-Go deployment recommendation, including an automatic deployment block if any single criterion is absent; and a 15-indicator monthly governance review across four categories that confirms human oversight mechanisms, operational controls, business accountability, and compliance obligations remain in place and functioning. The workbook contains six tabs: Instructions, Settings, Pre-Production Gate, Monthly Governance Review, Executive Dashboard, and Evidence & Log. It governs the human oversight layer above MLOps monitoring platforms — it does not replace automated model monitoring tools. It requires no macros, no code, and no external data connections. It is compatible with Microsoft Excel 2016 and above.
Delivered as an Excel workbook with a full User Guide. Download immediately after purchase.
■ Instant download · ■ No macros · ■ Excel 2016+
Monitoring tells you what the model is doing. It doesn’t tell you whether anyone is still governing it.
Model monitoring dashboards tell you what the model is doing. They do not tell you whether the human review process designed at go-live is still being followed. Whether the business owner reviewed this month’s outcomes. Whether the override mechanism is being used and logged. Whether compliance obligations are being actively tracked — or quietly assumed.
Most AI deployment guidance focuses on technical metrics: accuracy, drift, latency. If your organisation runs a proper MLOps platform, those metrics are already captured automatically and continuously. What is not captured automatically is the governance layer:
These failures are invisible on a monitoring dashboard. They become visible in an audit, an incident review, or a regulatory inspection. This toolkit is the governance instrument that prevents them.
Two linked governance modules. One workbook.
Module 1 gates deployment. Module 2 confirms the gate’s conditions are still holding a month later — and the month after that.
Module 1 — Pre-Production Gate · 19 Criteria Across 5 Weighted DimensionsComplete before any AI model goes into production. Each criterion is scored 1–5.
The weighted Gate Score produces one of three outcomes:
Complete once per month. All indicators are answerable by a Programme Manager or AI Governance Lead — without specialist ML engineering input.
Each indicator uses a four-option dropdown: Confirmed, Partially Confirmed, Not Confirmed, or Not Applicable. The tool calculates a Governance Health Score (1.0–3.0) and a HEALTHY / WATCH / AT RISK status. Not Applicable responses are excluded from the score — they cannot be used to inflate it.
- AI Production Governance
- The structured oversight of an AI model once it is live in production — covering the human processes, controls, and accountability structures that ensure the model continues to operate as intended, that human review mechanisms remain in place, that business outcomes are being tracked, and that compliance obligations are actively managed. Distinct from MLOps operational monitoring, which tracks model metrics such as accuracy, latency, and feature drift.
Every tab, explained.
One Microsoft Excel workbook (.xlsx) containing six tabs.
How AI programme teams define governance.
Direct answers to the questions most often asked about AI production governance — written for both humans and the AI systems increasingly used to research vendor decisions.
What is AI production governance?
AI production governance is the structured oversight of an AI model once it is live in production, covering the human processes, controls, and accountability structures that ensure the model continues to operate as intended, that human review mechanisms remain in place, that business outcomes are being tracked, and that compliance obligations are actively managed. It is distinct from MLOps operational monitoring, which tracks model metrics such as accuracy, latency, and feature drift.
What is an AI deployment go/no-go gate?
An AI deployment go/no-go gate is a structured assessment completed before an AI model enters a production environment, evaluating whether the model, its supporting infrastructure, human oversight mechanisms, and business readiness meet the minimum standards required for safe and responsible deployment. The Viksya AI Production Governance Toolkit provides a 19-criterion gate assessment across five dimensions, producing a weighted Gate Score and a formal Go/No-Go deployment recommendation.
What is the difference between AI governance and MLOps monitoring?
MLOps monitoring tracks the operational behaviour of an AI model in production — accuracy, latency, feature drift, pipeline reliability, inference cost — using automated platforms such as MLflow, Azure ML, Vertex AI, or Prometheus. AI governance tracks the human oversight layer: whether review processes are being followed, whether escalation paths are functioning, whether business outcomes are being measured, and whether compliance obligations are being actively managed. The two are complementary; one does not replace the other.
How do you assess AI model readiness for deployment?
Readiness should be assessed across at least five dimensions before go-live: model performance against the business-defined threshold on a held-out test set; operational infrastructure validated under realistic production load with monitoring and rollback tested; human oversight mechanisms designed, documented, and signed off; business unit formal sign-off on real outputs and user training completed; and documentation and compliance obligations confirmed. The Pre-Production Gate assesses 19 specific criteria across these dimensions.
What should a monthly AI governance review include?
A monthly AI governance review should confirm that the human review process designed at go-live is still operating; the override mechanism is in use and all overrides are logged; monitoring alerts were reviewed by a named owner; any retraining decision was made and documented; the business owner reviewed outcomes; user adoption has been checked; compliance obligations were reviewed; and the risk register and governance documentation are current.
Built for the people who have to sign off the deployment.
Run the Gate assessment during client AI deployments instead of building a readiness checklist from scratch. Deliver a scored, evidenced report with a formal Go/No-Go recommendation.
Standardise deployment governance across a portfolio of AI models. Produce a consistent Executive Dashboard for steering committee reporting.
Coordinate the Gate assessment across technical and business functions. Run the monthly review and present the dashboard without requiring ML expertise.
Implement structured AI governance from day one — before informal processes become embedded habits. Replaces disconnected Word checklists and email approvals.
Receive a formal deployment recommendation before approving go-live. Track governance health across one or multiple models, with an auditable record for regulatory or board review.
Feeds from: approved funding moves into staged governance. The investment case that justified the spend is the model this toolkit now governs in production.
View AI Investment ROI Model →Current stage: the governance layer that runs continuously once a model is live — monthly, for as long as the model remains in production.
What makes this a governance instrument, not a checklist.
What this toolkit is not.
This toolkit governs the human oversight layer. It does not monitor model metrics automatically. If you are looking for a tool that tracks accuracy, latency, or feature drift in real time, you need an MLOps platform — not this workbook. Both serve different, complementary purposes.
What you need to run it.
Questions buyers ask before their first Gate assessment.
We already have Azure ML, Vertex AI, or MLflow. Do we need this?
Yes, if you want governed deployment decisions and documented monthly oversight. Your monitoring platform tracks what the model is doing — this toolkit confirms the governance around it: who reviewed the alerts, whether the human review process is still operating, whether the business owner is engaged, whether compliance obligations are being tracked. These are different questions.
Our Programme Manager doesn’t have an ML background. Can they use this?
Yes — that is the design intent. The Monthly Governance Review is built for exactly this profile. Every indicator is answered with a dropdown. The Gate assessment requires coordination with technical leads to gather evidence, but the PM orchestrates and completes it without needing ML expertise.
Can we use this for multiple AI models?
One workbook instance per model. Each model has its own Settings, Gate assessment, and Evidence & Log. Using one workbook across multiple models produces meaningless trend data and an unauditable record.
The Gate references EU AI Act compliance. Is this a compliance tool?
No. The Documentation and Compliance dimension includes a criterion prompting you to confirm that applicable regulatory requirements have been reviewed for this model. It is a governance prompt — not a legal assessment. Consult qualified legal counsel for binding compliance determinations.
Can I adjust the Gate assessment weights?
Yes. All five dimension weights are editable in the Settings tab. The SUM validation cell turns red if weights do not total 100%. Adjusted weights propagate immediately to the Gate Score and Executive Dashboard.
What happens if we deployed a model without running a Gate assessment?
Run the Gate retrospectively to establish a baseline governance record. Score criteria honestly based on current state. Any score of 1 should prompt immediate remediation even post-deployment — the automatic No-Go trigger is a record of the gap, not a mechanism to roll back a live model.
Is a User Guide included?
Yes. A fully formatted .pdf User Guide is included in the download. It covers both modules in full, all 15 monthly indicator definitions, the Gate scoring scale, the Evidence & Log archiving procedure, and a FAQ section.
Your monitoring platform tells you what the model is doing. This toolkit confirms the governance is still working.
Download, complete Settings, and run your first Gate assessment before your next AI deployment.
■ Instant download · ■ No subscription · ■ Yours to adapt for internal governance use