DataConsultant helps organisations discover, classify and document AI models, generative AI applications, embedded vendor capabilities and material automated decisions. We establish ownership, lifecycle status, data dependencies, risk tiers, controls and evidence so governance, technology, risk and business teams can make informed decisions from one maintained source of truth.
Illustrative structure only. Taxonomy, thresholds and fields are tailored to the organisation.
An AI model inventory service creates a governed and maintainable register of AI models, AI systems, applications, agents and embedded capabilities. It links each asset to purpose, ownership, affected users, data, vendors, risk, controls, approvals, testing, monitoring, changes and retirement obligations.
Organisations cannot govern AI reliably when they do not know where it is used, who owns it, what data it depends on or which systems could create material harm, compliance exposure or operational disruption.
We combine business discovery, technical evidence, governance design and implementation support to create a usable inventory rather than a one-time spreadsheet that becomes outdated.
The inventory can support AI policy enforcement, risk assessment, regulatory scoping, audit preparation, third-party oversight, security and privacy reviews, incident response, portfolio decisions and lifecycle reporting.
AI use often grows through business experimentation, embedded software features, cloud services and vendor products. Without a common register, governance teams may miss material systems while owners repeat assessments or cannot produce evidence when decisions are challenged.
The scope is defined by business impact and governance need, not only by the technology label applied by a development team or vendor.
Predictive, statistical, optimisation, machine-learning and decision models developed internally or supplied by third parties.
Foundation-model applications, copilots, retrieval systems, content-generation tools, multimodal services and customised assistants.
AI agents, tool-using workflows, autonomous or semi-autonomous processes and material automated decisions.
AI features inside enterprise applications, SaaS products, cloud platforms, devices and outsourced business processes.
Scope can range from a focused baseline assessment to enterprise implementation and managed operation.
Define what counts as an AI system, which assets are in scope, required fields, lifecycle states, ownership roles, risk dimensions, evidence links, record relationships and minimum quality rules. The design can accommodate internal models, vendor systems, shared services, model versions and composite applications.
Review existing registers, procurement records, application portfolios, model platforms, cloud environments, data catalogues, architecture records, vendor lists, policies and stakeholder knowledge. Discovery findings identify known assets, likely blind spots, duplicate records, ownership gaps and evidence limitations.
Design a proportionate method for classifying systems according to business criticality, impact on people, autonomy, data sensitivity, explainability, security, external exposure, vendor dependence, reversibility and regulatory relevance. Legal conclusions remain subject to authorised legal review.
Define registration, review, approval, challenge, escalation, exception, attestation, incident, change and retirement workflows. Link inventory records to policies, assessments, testing, monitoring, contracts, data lineage, security controls, privacy reviews and model documentation.
Implement the inventory in an appropriate GRC, metadata, model-management, service-management, workflow or controlled-register environment. Configure role-based views, status reporting, dashboards, reminders, quality checks, integrations and exportable evidence based on scale and platform capability.
Create procedures, role guidance, service levels, review calendars, owner training, governance reporting and quality-management routines. Ongoing support can maintain taxonomy, review submissions, coordinate attestations, track remediation and improve the inventory as technology and obligations change.
Final deliverables depend on the agreed scope, available evidence, platform environment and the level of implementation support required.
| Deliverable | What it includes | Primary users | Client input required |
|---|---|---|---|
| Scope and taxonomy | AI-system definition, inclusion rules, categories, lifecycle states and record relationships | AI governance, risk, technology | Policies, technology landscape, governance objectives |
| Inventory data model | Mandatory and optional fields, ownership, risk, controls, evidence and quality rules | Platform owners, governance operations | Existing registers, reporting and platform constraints |
| Discovery findings | Known inventory, suspected shadow AI, gaps, duplicates, missing owners and evidence limitations | Executives, audit, programme teams | Stakeholder access and source records |
| Populated baseline register | Validated records for agreed business units, systems or use cases | Business owners, technical owners, assurance teams | Owner validation and supporting evidence |
| Risk-classification method | Dimensions, thresholds, decision rules, approval points and review requirements | Risk, compliance, legal, AI governance | Risk appetite and regulatory interpretation |
| Operating procedures | Intake, review, approval, attestation, change, incident, exception and retirement processes | Governance operations and owners | Decision rights and service expectations |
| Reporting specification | Coverage, quality, risk, evidence, remediation, change and lifecycle dashboards | Boards, committees, management | Reporting audience and escalation thresholds |
| Remediation roadmap | Prioritised gaps, owners, dependencies, actions and acceptance evidence | Transformation and control owners | Capacity, funding and delivery constraints |
The stages are adapted to organisational scale, evidence quality, regulatory context, platform choices and whether the engagement includes implementation or managed support.
Confirm objectives, accountable sponsor, inventory boundary, stakeholders, obligations, reporting needs and success measures.
Primary output: agreed scope and discovery planReview registers, policies, platforms, procurement, architecture, data, vendors, controls and known use cases.
Primary output: current-state findings and source mapDefine taxonomy, required fields, lifecycle, ownership, risk dimensions, evidence and data-quality rules.
Primary output: inventory specificationGather candidate records, interview owners, resolve duplicates, validate scope and record evidence limitations.
Primary output: validated baseline inventoryApply risk tiers, map assessments, approvals, monitoring, contracts, incidents and remediation requirements.
Primary output: governed records and control linksConfigure the chosen register or platform, roles, intake, reviews, reminders, dashboards and exports.
Primary output: operational inventory capabilityProvide role guidance, procedures, quality checks, governance reporting and knowledge transfer.
Primary output: trained operating teamsSupport attestations, change events, issue resolution, taxonomy updates and control effectiveness reporting.
Primary output: maintained source of truthAn inventory becomes useful when it drives proportionate action. DataConsultant can align record fields and workflows to the organisation’s AI policy, risk appetite, control environment and assurance model.
DataConsultant provides consulting, implementation and operational support. The service does not guarantee compliance, certification, cybersecurity, statutory audit outcomes or regulatory approval. Legal and regulatory interpretations should be reviewed by authorised specialists.
Technology choices are based on scale, workflow, integration, reporting, security, licensing, maintainability and operating ownership rather than a predetermined vendor.
GRC systems, metadata catalogues, model-management platforms, service-management tools, application portfolios, workflow products and controlled registers.
Cloud AI services, ML platforms, code repositories, API gateways, data catalogues, procurement systems, vendor records, identity systems and observability tools.
Recognised AI risk, management, security, privacy, model-risk and data-governance frameworks can inform taxonomy and controls where applicable.
Review platforms, evidence sources, integration needs, control ownership and reporting before implementation.
| Model | Best suited to | Typical scope | Commercial basis | Important dependency |
|---|---|---|---|---|
| Assessment and design | Organisations needing a baseline and target approach | Current state, taxonomy, data model, risk method and roadmap | Fixed scope or time used | Access to evidence and stakeholders |
| Inventory implementation | Teams ready to build a governed operational register | Discovery, population, workflows, platform configuration, reporting and training | Phased project | Platform access and client decisions |
| Embedded specialists | Programmes requiring additional governance or technical capacity | Inventory operations, analysis, owner coordination, control mapping and assurance | Monthly specialist or team fee | Clear internal management and decision rights |
| Managed inventory service | Organisations seeking ongoing administration and quality management | Intake, validation, attestations, reporting, issue escalation and improvement | Recurring service fee | Documented accountability and service levels |
A written estimate should follow initial discovery because a simple use-case register and an enterprise inventory with technical discovery, workflow integration and managed operation require materially different effort.
Number of business units, systems, models, vendors, jurisdictions and historical records.
Required technical evidence, risk classification, control mapping, validation and regulatory analysis.
Platform configuration, integrations, data migration, security, workflow and reporting requirements.
Training, remediation, owner coordination, attestations, service levels and managed-service coverage.
Measures should use documented baselines and avoid implying that inventory completion alone proves compliance or control effectiveness.
Representative feedback is presented below to illustrate the delivery qualities organisations value in an AI Model Inventory Service engagement.
“The engagement gave us a practical definition of what belonged in the inventory and connected it to the decisions our governance committee needed to make. The final structure covered internal models, generative AI tools and vendor capabilities without turning the register into an unmanageable compliance exercise.”
“Workshops were well facilitated across technology, procurement, security and business teams. DataConsultant resolved conflicting terminology, documented decisions and made ownership gaps visible. That allowed us to approve a common intake process and move forward without waiting for every platform question to be solved first.”
“The inventory design clarified who owns the business purpose, technical operation, risk review and evidence for each system. The RACI and escalation model were especially useful because they separated accountability from day-to-day administration and helped our committee challenge incomplete records consistently.”
“The risk-classification method was proportionate and understandable. It gave us practical decision criteria for impact, autonomy, data sensitivity, external exposure and vendor dependence, while clearly identifying where legal interpretation was still required. This improved consistency without pretending every AI use case carries the same obligations.”
“The implementation guidance went beyond a populated spreadsheet. We received field definitions, workflow rules, dashboard requirements, owner guidance and a prioritised remediation backlog. Knowledge transfer enabled our operations team to maintain the register and add new systems using the same evidence and quality standards.”
“Communication and documentation remained clear throughout discovery and validation. Questions were tracked, revisions were controlled and limitations were recorded rather than hidden. The final inventory and assurance notes gave internal audit a traceable view of source evidence, owner confirmation and unresolved actions.”
An AI model inventory is a governed register of AI models, AI systems, embedded AI capabilities and material use cases. It records ownership, purpose, lifecycle status, data dependencies, vendors, risk classification, controls, approvals, monitoring and evidence needed for oversight.
An inventory gives decision-makers a reliable view of where AI is used, who is accountable, which systems require assessment, what obligations apply and whether controls remain effective. It supports governance, regulatory readiness, auditability, incident response, investment planning and reduction of unmanaged shadow AI.
Scope may include predictive models, machine-learning services, generative AI applications, foundation models, copilots, agents, decision systems, vendor-embedded AI, internally developed algorithms, material automated rules and retired systems that still require retention or evidence.
Typical fields include business purpose, owner, developer, operator, users, affected parties, deployment status, model type, data sources, outputs, integrations, vendor, hosting, jurisdictions, risk tier, impact assessment, approvals, testing, monitoring, incidents, change history and retirement requirements.
Discovery can combine stakeholder interviews, surveys, procurement and vendor records, architecture and application inventories, cloud and platform evidence, data-flow review, software catalogues, policy attestations and targeted technical analysis. Findings are validated with accountable owners before inclusion.
A well-designed inventory can organise evidence needed for regulatory scoping and governance, including intended purpose, provider and deployer roles, risk classification, affected jurisdictions, controls and documentation status. Applicability and legal interpretation must be confirmed by qualified legal or regulatory specialists.
DataConsultant can design a proportionate classification method using business impact, affected people, autonomy, criticality, data sensitivity, explainability, external exposure, regulatory relevance, security risk, vendor dependency and reversibility. Final thresholds and approvals are agreed with the client.
Yes. The inventory can be implemented in an existing governance, GRC, metadata, service-management, model-management or workflow platform, or delivered initially through a controlled register. Integration decisions depend on scale, workflow needs, APIs, licensing, security and operating ownership.
The inventory should be updated when an AI system is proposed, materially changed, deployed, suspended, transferred or retired. Organisations commonly add scheduled owner attestations and risk-based review cycles, but the frequency should reflect system criticality, change rate and regulatory obligations.
Accountability often sits with an AI governance, risk, data, technology or model-risk function, while individual business and technical owners remain responsible for their entries. A clear operating model should define registration, review, approval, challenge, escalation, reporting and evidence ownership.
Deliverables can include scope and taxonomy, inventory data model, discovery findings, populated register, ownership map, risk-classification method, workflow design, control and evidence requirements, dashboard specifications, operating procedures, remediation backlog, training and implementation roadmap.
A reliable duration cannot be set without discovery. Timing depends on organisation size, number of business units and jurisdictions, availability of system records, stakeholder access, tool integration, inventory depth, validation cycles and whether remediation or managed operation is included.
Cost is influenced by the number and diversity of AI systems, business units, vendors and jurisdictions; the depth of technical and control assessment; evidence quality; platform integration; workshops; regulatory mapping; reporting; training; remediation support and the chosen engagement model.
Yes. Managed support can include intake, quality review, owner follow-up, scheduled attestations, change monitoring, evidence checks, dashboard reporting, issue escalation, taxonomy maintenance, policy alignment and periodic improvement. Client accountability and approval rights remain documented.
