Approved Standard Industrial Classification (SIC) codes:
7371, 7373, 7374, 7375, 7376, 7379, 8711, 8742, and 8748
State of Illinois
State of Indiana
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FDIC
City of Chicago
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Chicago Transit Authority
Chicago Public Schools
Artificial intelligence creates value only when organizations can trust how it is selected, designed, developed, deployed, used, and monitored. AJRA helps organizations move beyond high-level ethical statements and isolated policies to establish an operating model for AI governance—one that supports innovation while protecting people, the enterprise, and the communities the organization serves.
Our approach integrates AI strategy, risk management, ethics, data and knowledge governance, technology architecture, regulatory readiness, organizational change, and independent assurance. Governance is embedded across the AI lifecycle rather than added after a system is already in production.
We connect AI governance to business strategy, operating priorities, risk tolerance, and measurable value—not technology alone.
We address the quality, ownership, provenance, context, and flow of the organizational knowledge used by AI systems, including generative and agentic AI.
Governance begins with ideation and intake and continues through design, testing, deployment, monitoring, change, retirement, and incident response.
Oversight and controls are scaled to the potential impact of each AI use case, system, model, agent, vendor, and decision pathway.
We define the documentation, metrics, testing results, approvals, logs, and monitoring evidence leaders should expect before trusting an AI system.
Accountability remains with people. Human oversight, contestability, accessibility, fairness, privacy, transparency, and safety are designed into governance decisions.
The governance model can be mapped to recognized frameworks and obligations, including the NIST AI RMF, ISO/IEC 42001, IEEE CertifAIEd™, and applicable laws and sector requirements.
AI governance should not become policy theater or an innovation bottleneck. It should create the decision rights, evidence, controls, and accountability needed to use AI responsibly and at scale.
AJRA applies a structured, six-stage lifecycle that translates governance principles into repeatable management practices and operational controls.
Identify AI use cases, models, algorithms, generative AI tools, agents, data sources, vendors, business owners, users, and decision impacts. Assess AI readiness and maturity, surface shadow AI, document dependencies, and establish a fact-based view of current capabilities and risks.
Establish the governance charter, decision rights, roles, committees, escalation pathways, risk ownership, acceptable-use boundaries, and executive and board reporting. Align AI governance with enterprise risk, cybersecurity, privacy, legal, compliance, procurement, data governance, model risk management, and knowledge management.
Apply a risk-tiering method based on purpose, autonomy, affected stakeholders, decision criticality, data sensitivity, legal exposure, model complexity, and potential harm. Conduct AI impact, ethics, privacy, security, bias, transparency, and vendor assessments proportionate to the risk level.
Embed policies, standards, review gates, testing requirements, human oversight, data and knowledge controls, documentation, traceability, access controls, prompt and agent safeguards, approval criteria, procurement requirements, and incident procedures within the AI lifecycle and existing workflows.
Determine whether governance is working through evidence—not policy existence. Review model and system documentation, evaluation results, risk treatment, accountability, monitoring plans, and residual risk before deployment. Where appropriate, conduct independent or IEEE-aligned third-party assessments.
Track performance, drift, bias, hallucination, misuse, security, privacy, incidents, human overrides, vendor changes, regulatory developments, and realized value. Provide leadership with meaningful indicators and continuously improve the AI governance management system.
Effective governance requires more than model controls; it requires coordinated management across eight interconnected domains.
Business alignment, use-case prioritization, risk appetite, value realization, and responsible innovation.
AI strategy, roadmap, use-case portfolio, value and risk criteria.
Board and executive oversight, decision rights, ownership, escalation, and organizational accountability.
Governance charter, RACI, committee model, reporting structure.
Enterprise policies, regulatory mapping, standards alignment, and audit readiness.
AI policy suite, compliance matrix, control library, evidence requirements.
Data quality, provenance, privacy, metadata, knowledge sources, content authority, and retrieval grounding.
Data and knowledge controls, source requirements, lineage and provenance rules.
Validation, robustness, bias, transparency, autonomy, human oversight, security, and lifecycle management.
Risk tiering, impact assessment, model and system cards, testing and approval gates.
Vendor due diligence, contractual protections, transparency, performance, monitoring, and exit planning.
Vendor assessment, procurement standards, contract control requirements.
AI literacy, role redesign, competence, responsible use, adoption, and culture.
Training, communications, role-based guidance, change and adoption plan.
Metrics, incidents, drift, control effectiveness, audits, and continual improvement.
Dashboards, KRIs and KPIs, audit plan, monitoring and improvement backlog.
Engagements are tailored to the organization's maturity, regulatory environment, technology portfolio, and business objectives. Services may be delivered independently or combined into an integrated AI governance program.
Evaluate governance, leadership, workforce, process, data, knowledge, technology, risk, and operational capabilities; identify gaps and prioritize an actionable roadmap.
Define how the organization will pursue AI value responsibly, including governance structure, decision rights, funding, accountability, and integration with enterprise management processes.
Create practical policy and control requirements covering acceptable use, generative AI, agentic AI, model and system lifecycle, data and knowledge use, human oversight, procurement, monitoring, and incidents.
Establish an authoritative inventory; classify systems and use cases; assess ethical, legal, operational, privacy, security, workforce, and societal impacts.
Evaluate whether governance controls are designed appropriately, operating effectively, and supported by defensible evidence. Provide findings, risk ratings, remediation actions, and leadership-ready reporting.
Perform third-party assessments aligned with the IEEE CertifAIEd™ framework to examine accountability, transparency, privacy, algorithmic bias, and other ethical criteria applicable to the system.
Assess vendor claims, model transparency, data practices, security, performance, contractual risk, monitoring, and organizational fit before purchase or renewal.
Build role-based competence for boards, executives, governance bodies, risk and compliance teams, technology teams, business users, and AI system owners.
Translate recommendations into workflows, review gates, templates, dashboards, control evidence, governance routines, and continuous-monitoring practices.
AJRA does not force every organization into a single framework. We create an integrated control environment that uses the most relevant standards, laws, and industry practices for the organization's context.
Supports a structured approach to GOVERN, MAP, MEASURE, and MANAGE AI risk and trustworthiness across the lifecycle.
Provides management-system requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System.
Provides an applied ethics assessment approach addressing accountability, transparency, privacy, algorithmic bias, and related ethical criteria.
Informs risk classification, prohibited and high-risk practices, transparency, documentation, human oversight, AI literacy, and other obligations where applicable.
Integrates relevant financial services, insurance, healthcare, public-sector, cybersecurity, privacy, procurement, records, and model-risk obligations with existing governance.
A clear and current inventory of AI systems, use cases, models, agents, vendors, owners, data and knowledge dependencies, and risk classifications.
A governance operating model with accountable roles, decision rights, review forums, escalation paths, and executive and board oversight.
Policies and controls that are usable within real development, procurement, deployment, and business workflows.
Risk and impact assessments that distinguish acceptable experimentation from unmanaged enterprise exposure.
Documented evidence that supports regulatory inquiries, internal audit, customer due diligence, third-party assurance, and leadership decisions.
Metrics that show whether AI is trustworthy, compliant, adopted, controlled, and producing intended business value.
A prioritized improvement roadmap that balances immediate risk reduction with long-term governance maturity.
An AI governance capability that helps the organization innovate faster, because leaders, employees, customers, regulators, and partners can see how AI decisions are governed and why the organization's controls can be trusted.
Whether your organization is beginning its AI journey, scaling generative AI, deploying autonomous agents, evaluating third-party solutions, or preparing for regulatory and assurance requirements, AJRA can help you establish governance that is practical, proportionate, and aligned with your mission.
Schedule an AI Governance Consultation