AI Adopters & Deployers
Responsibility for deployed AI outcomes remains with the deploying organization, even when technology is sourced from a vendor. Vendor reputation does not replace deployer accountability.
You integrate and deploy AI systems that were built by others
CIO, COO, or Head of Digital at an organization that has embedded third-party AI tools into business operations, services, or decision support. You didn’t build the model, but you’re accountable for what it does in your context.
Oversight expectations are increasing. Customers, regulators, and internal stakeholders are starting to ask how accountability is understood, assigned, and communicated across your organization — and right now, you don’t have a structured, documented way to answer.
Is This the Right Track?
👌 Best For
- Enterprises deploying AI in operations, services, or decision support
- Teams that need a documented transparency baseline across a business context
✋ Not For
- Organizations seeking legal representation or legal certification
- Teams expecting one-time disclosure work without internal process ownership
Why Now?
Deploying organizations remain accountable for how AI systems affect employees, customers, patients, citizens, and other stakeholders, regardless of who built the model or platform.
As oversight expectations increase, organizations need to show how accountability is understood, assigned, and communicated in their own operating context.
Choose Your Level of Engagement
DisclosePlan
Flat fee, scope confirmed at intake
Includes:
- AI Deployment Transparency Readiness Assessment
- Open Ethics Canvas Workshop and Workshop Report
- Machine-readable Transparency Disclosure draft (Open Ethics Transparency Protocol)
- Open Ethics Label draft package
- Policy Roadmap
- Disclosure Review Summary (advisory)
Outcomes:
- Clear view of deployment transparency responsibilities
- First stakeholder-ready disclosure package
- Practical roadmap for internal rollout
EmbedPlan
Based on scope and consulting days
Everything in Disclose, plus:
- AI Risk Map using the Really Simple AI Risk Framework
- Guided implementation support for OEMM Level 1 (Awareness) and Level 2 (Transparency)
- Vendor disclosure chain review and recommendations
- Internal policy review and recommendations aligned to applicable requirements
- Machine-readable disclosure hosting implementation support
- Internal team onboarding across participating units
- Maturity roadmap updates and implementation modules
Outcomes:
- Accountabilities are clarified across business and technical teams
- Transparency process is embedded into ongoing operations
- Level 1 and Level 2 maturity activities are implemented with advisory support
Not the Right Track?
Organizations integrating third-party AI systems in business operations and managing accountability at scale.