SmartSuite for the AI Governance Lead

Each AI use case carries its risk tier, model owner, business owner and independent reviewer, so inventory coverage is measured against named accountability rather than a list of system names.

See which models still need an assessment, approval, control or monitoring cycle, and chase the gaps through automations instead of a quarterly spreadsheet reconciliation across the estate.

Dashboards read directly from inventory, assessment and control records, so program maturity is a current number you can defend, not a self-assessment compiled for the annual review.

Approval, conditional approval and exception decisions move through workflows that bring in the Model Risk Manager, Privacy Program Manager and IT Compliance Manager at the point their sign-off is needed.

Every model is connected to the business processes, applications and datasets behind it, so a privacy or security question about one dataset shows which models it touches.

AI controls and findings sit in the same structure as the enterprise risk, control and compliance programs you depend on, so the AI Risk Officer and audit teams read the same records.

Issues Management links exceptions and findings to owners, deadlines and evidence of resolution, so systemic patterns across models are visible to you before they become a regulator's observation.

Model documentation, testing artifacts and review logs are stored against the obligation they satisfy, using CRI AI RMF mappings where relevant, so examiners receive records rather than reconstructed narratives.

Who approved what, when and why is kept with every version, so your governance maturity claims and Policy Management attestations hold up when the people who made them have moved on.

Chief Data and AI Officer

The AI Governance Lead runs the AI governance program end to end: its policies, inventory coverage and risk tiering of AI use cases, the mapping of models to obligations, controls and standards, and the tracking of exceptions, systemic issues and governance maturity. In SmartSuite that program lives in AI Governance, a connected ecosystem linking models, risks, controls, obligations and evidence, so the lead can see which use cases are inventoried, which carry open exceptions and how mature each part of the program is.

SmartSuite keeps every model and AI use case in AI Governance, where each record links to the risks, controls, obligations and evidence that apply to it. Inventory coverage becomes measurable: the lead can see which use cases are registered, which have a risk tier and which still lack an owner or a review. Framework mapping sits alongside the inventory, so standards are applied to the registered models rather than tracked in a separate spreadsheet, and CRI AI RMF is available for teams aligning to that profile.

SmartSuite maps each model to the obligations, controls and standards that apply to it and tracks evidence per requirement, so the lead can show for any model which laws, internal standards and controls cover it and what proof exists. Policy Management holds the AI policies those controls implement, and because the mapping is recorded against the inventory, the lead can see at once which models a new obligation touches rather than reassessing them one by one.

Exceptions and repeat issues are tracked in Issues Management, which records each exception against the model and control it relates to and follows it to closure. Because repeat issues are visible across the whole inventory, the lead can identify enterprise risk themes, for example the same control failing across several use cases, rather than treating each exception in isolation. Those themes feed the maturity view and the coordinated reviews with model owners, validators, privacy and security.

The AI Governance Lead reports coverage, exceptions and governance maturity to the Chief Data and AI Officer from the same records the program runs on. SmartSuite reports inventory coverage, open exceptions and maturity by area, so the upward report is a view of live data rather than a compiled deck. The same view serves the lead's work with the AI Risk Officer, Model Risk Manager and Privacy Program Manager, who see the models and issues they share.

You need a starting list of AI use cases and models, an owner for each and the obligations and standards you intend to map against; the inventory can begin incomplete and grow as coverage improves. New customers can start with SmartSuite Onboarding, a guided four-week program around one or two priority workflows, such as the AI use-case inventory. The AI governance work shares its issue, policy and evidence records with the compliance, privacy and risk teams, so model owners and validators work in the same system rather than sending documents to the lead.