A structured diagnostic to understand how ready your organization is to adopt and scale AI responsibly.
We assess maturity across leadership alignment, strategy, governance, data foundations, risk management, and organizational capabilities.
The assessment provides a clear view of current gaps, strengths, and priority areas  benchmarked against global best practices.

‍Outcome:
A fact-based maturity profile, clear priority actions, and an objective baseline to guide governance and investment decisions.

A tailored framework defining how AI is governed across its full lifecycle.
We design decision structures, accountability models, policies, and oversight mechanisms that ensure AI initiatives remain aligned with business objectives, ethical principles, and regulatory expectations.
The framework clarifies who decides, who owns risk, and how AI systems are approved, monitored, and reviewed.

‍Outcome:
Clear governance structures, enforceable guardrails, and a shared understanding of responsibilities across the enterprise.

A practical policy foundation for responsible  AI adoption.
We define a coherent set of Responsible AI policies that govern how AI systems are approved, developed, used, and overseen across the enterprise.
Unlike high-level AI ethics statements, this approach is operational by design, embedding risk tiering, accountability, and compliance obligations directly into policy language.

‍Outcome:
A clear and defensible Responsible AI policy that enables AI adoption while maintaining control, compliance, and accountability.

A practical model for running and governing AI across the organization.
We define roles, workflows, escalation paths, and interfaces between business, technology, risk, legal, and compliance teams.
The operating model ensures AI systems are governed consistently from ideation to retirement without slowing innovation.

‍Outcome:
Clear ways of working, reduced ambiguity, and smoother collaboration across all AI stakeholders.

A structured approach to identifying and managing AI risk beyond the model level.
We develop an enterprise-wide AI risk taxonomy covering model risk, data risk, operational risk, compliance risk, and systemic risk.
Risks are tiered, assigned owners, and linked to appropriate controls and review cycles.

‍Outcome:
A shared risk language, prioritized risk treatment, and defensible risk decisions for AI systems.

A set of practical, enforceable policies for responsible AI use.
We translate high-level responsible AI principles into concrete policy requirements that teams can apply in practice.
Policies are aligned with international standards and GCC regulatory expectations, and designed to integrate seamlessly with existing corporate policies.

‍Outcome:
Clear policy guidance that supports innovation while ensuring accountability, fairness, and compliance.

Establish a centralized register of AI systems across the enterprise.
Identify models, datasets, use cases, owners, and purposes.
Capture key governance and risk attributes consistently. Maintain the inventory as systems are added, modified, or retired.
Use it as the foundation for oversight and governance decisions.

‍Outcome:
Clear visibility into where AI is used, by whom, and under which governance conditions.

Define AI risk tiers at use-case and system level.
Classify AI systems based on impact, criticality, and exposure.
Apply consistent risk criteria across the AI portfolio.
Use risk tiers to drive governance intensity and decisions.

‍Outcome:
Clear, consistent risk classification across all AI systems.

Maintain ongoing oversight across the AI lifecycle.
Track AI systems, ownership, and risk status over time.
Monitor changes in scope, usage, and risk exposure. Identify deviations and trigger governance actions when needed. Continuous governance, evidence collection, and monitoring in one platform.

‍Outcome:
Continuous visibility and control as AI systems scale and change.