Operational AI Governance
Credo AI governs models. Dust.tt enables workflows. Nobody governs how your people actually work with AI.
Between model governance and workflow enablement sits an empty layer -- the operational layer where AI usage policies, tool provisioning, and cross-platform visibility should live. We fill that gap.
Cross-Platform Visibility
See every AI tool your people use across every department, every day -- not just the ones IT approved
Enforced AI Policies
Manager-defined rules the system enforces -- not guidelines people ignore in PDF documents
Measurable AI ROI
Track adoption, productivity impact, and cost per department -- prove AI investment returns
The Three Layers of AI Governance
Every organization needs all three layers. Two are crowded with vendors. One is completely empty.
Model Governance
Ensuring AI models are safe and compliant
Audits model behavior, bias detection, fairness testing, model cards, and regulatory compliance for AI systems themselves.
Established Players
- Credo AI -- AI governance and risk management
- IBM OpenPages -- Enterprise AI risk and compliance
- ModelOp -- ML model management and monitoring
- Arthur AI -- Model performance and fairness
These tools answer: "Is this model safe to deploy?" They do NOT answer: "Are people using AI correctly?"
Operational AI Governance
Governing how people work with AI
Controls AI usage at the operational level: who gets which tools, what policies apply per role, how AI work is tracked, and how compliance is evidenced -- for people, not models.
What This Layer Covers
- AI usage policies per role and department
- Approved tool provisioning and enforcement
- Cross-platform AI usage visibility
- AI ROI tracking per team and workflow
Neomanex is the first company to build a dedicated system for this layer.
Workflow Enablement
Embedding AI into business processes
Connects AI to business workflows -- automations, integrations, AI assistants embedded in daily work.
Established Players
- Dust.tt -- AI assistants for business teams
- Zapier -- Workflow automation with AI
- n8n -- Open source workflow automation
- Microsoft Copilot Studio -- Enterprise AI assistants
These tools answer: "How do we use AI in our workflows?" They do NOT answer: "Are people following AI policies?"
The Governance Gap
Model governance ensures AI is compliant. Workflow enablement ensures AI is useful. But neither ensures people use AI correctly. That is operational AI governance -- and until Neomanex, the market had no solution for it.
What Operational AI Governance Covers
Manager-defined rules that the system enforces. Not guidelines in a PDF -- actual guardrails built into how your teams work with AI.
AI Usage Policies per Role
Different roles, different rules
Define what each role can and cannot do with AI. Developers get code assistants. Marketing gets content tools. Finance gets analysis models. Nobody gets unrestricted access.
- Role-based AI tool access with department-level granularity
- Data sensitivity classifications that restrict AI input by context
- Escalation paths when AI output requires human review
- Usage limits and guardrails that prevent misuse before it happens
Approved Tool Provisioning
Give people what they need so they stop finding workarounds
When approved tools are provided, unauthorized AI use drops 89%. The solution to shadow AI is not more restrictions -- it is better provisioning with governance built in.
- Curated AI tool catalog vetted for security and compliance
- One-click provisioning with SSO and policy enforcement baked in
- Automatic deprovisioning on role change or offboarding
- License management and cost tracking per tool, per team
Cross-Platform Visibility
See everything, across every tool
Your teams use Claude, ChatGPT, Copilot, Midjourney, and a dozen more tools. You have zero visibility into any of it. Operational governance changes that.
- Unified dashboard across all AI platforms and tools
- Usage patterns by team, role, and workflow
- Shadow AI detection -- identify unsanctioned tools before they become risks
- Compliance evidence generation for audits and regulatory reviews
AI ROI Tracking
Prove AI investment returns with real data
Most organizations cannot answer "what is our AI ROI?" because they have no operational data. Governance gives you the measurement framework to answer that question.
- Cost per AI interaction by tool, team, and workflow
- Productivity impact measurement linked to AI adoption metrics
- Department-level AI spend visibility with optimization recommendations
- Executive reporting dashboards for board-level AI governance evidence
Operational Governance vs Model Governance
They govern AI. We govern how you work with AI. Two completely different problems that require completely different systems.
| Dimension | Model Governance | Operational AI Governance |
|---|---|---|
| Focus | Is the AI model safe and fair? | Are people using AI correctly? |
| Subject | Models and algorithms | People and processes |
| Controls | Bias testing, model cards, fairness audits | Usage policies, tool provisioning, access controls |
| Compliance | EU AI Act model requirements | EU AI Act deployment requirements, employment law, data protection |
| Question | "Can we deploy this model?" | "How should our people use this model?" |
| Audience | Data science and ML teams | Every employee using AI |
| Failure Mode | Biased or unsafe model in production | Shadow AI, data leaks, governance theater |
The Enemy: Governance Theater
Only 37% of organizations have formal AI governance policies. Of those that do, most are PDF documents that nobody reads and nothing enforces. This is governance theater -- the illusion of control.
Real governance is not a policy document. It is a system that enforces rules automatically, tracks compliance continuously, and generates evidence for auditors on demand.
The AI Operations Hub
A single entry point for your entire organization's AI operations. Log in once. Work AI-governed.
Single Sign-On Entry Point
Employees log in once through your existing identity provider. The hub recognizes their role, department, and permission level automatically.
Role-Based Tool Access
Developers see code assistants. PMs see project tools. Marketing sees content generators. Each role gets exactly the AI tools they need -- nothing more, nothing less.
Enforced Workflows
AI usage flows through governed pathways. Sensitive data triggers review gates. Policy violations are caught in real time, not discovered in quarterly audits.
Continuous Visibility
Every AI interaction is logged. Every policy decision is recorded. Managers see real-time dashboards. Compliance teams get audit-ready evidence on demand.
Hub Capabilities
Everything in one governed system
For Employees
- One place to access all approved AI tools
- Clear guidance on what is and is not allowed
- No friction -- governed tools are easier than workarounds
For Managers
- Real-time visibility into team AI adoption
- Policy configuration without IT involvement
- ROI data to justify AI investments to leadership
For Compliance
- Complete audit trails for every AI interaction
- Automated compliance reporting for regulators
- Policy enforcement evidence -- not just policy existence
Compliance That Works
Governance for operations, not just compliance. Addressing regulatory requirements at the usage level -- where enforcement actually matters.
EU AI Act
Deployment requirements
The EU AI Act has deployment obligations that go beyond model compliance. Deployers must ensure human oversight, transparency, and usage monitoring -- all operational concerns.
- Human oversight mechanisms for high-risk AI systems
- Transparency obligations for AI-generated content
- Usage monitoring and incident reporting systems
- Staff competency requirements and training evidence
Employment Law
AI in the workplace
AI usage in the workplace creates employment law obligations around monitoring, fair treatment, and worker consultation. Operational governance addresses these directly.
- AI monitoring disclosure compliant with workplace privacy laws
- Fair AI-assisted decision-making in HR processes
- Works council and union consultation evidence
- Anti-discrimination compliance for AI-augmented roles
Data Protection
AI usage-level controls
GDPR and data protection laws apply to how people use AI with personal data -- not just how models process it. Operational governance prevents data leaks at the usage point.
- Data classification gates before AI input
- PII detection and redaction in AI prompts
- Data processing agreement evidence for third-party AI tools
- Cross-border data transfer controls per AI platform
Ready to Close the Governance Gap?
Find out where your organization stands on operational AI governance. Get a clear assessment of your governance gaps and a practical roadmap to close them.
They govern AI. We govern how you work with AI. See the difference in a 30-minute discovery session.