360° AI Maturity Audit Case Study
A privacy-first audit built from 749 documented evidence items, conducted for the Business Career Hub at Ted Rogers School of Management, Toronto Metropolitan University.
Book a CallThe business case for a 360° AI Maturity Audit.
The Business Career Hub at Ted Rogers School of Management, Toronto Metropolitan University engaged thinkCircle to conduct a 360° Complete Readiness AI Maturity Audit. The department provides career services for approximately 13,000 undergraduate students and a growing alumni network of more than 60,000, including work-integrated learning programs, individual and group coaching, alumni relations, and executive education for industry professionals. It serves students, alumni, employers, and faculty across three functional areas with approximately 45 staff. The co-op program alone supports over 3,400 students and more than 1,000 active employers, with a mandate to scale co-op-like outcomes to the broader student population.
The department is navigating significant growth expectations with constrained capacity. Leadership's top priority is creating capacity for growth through process improvement, with specific pressure to expand the co-op program and increase employer partnerships. Staff had already begun using AI tools independently across communications, research, and content creation. Leadership needed a clear evidence base before committing resources, expanding AI use, or setting governance priorities.
The engagement ran for approximately eight weeks from early February to early April 2026.
Client findings, scores, and recommendations remain confidential to the client.
Even as leaders in the integrated use of technology within our business school, we knew we would benefit from expert insight into navigating the AI landscape. The audit conducted by thinkCircle confirmed exactly where we stood and provided the clear roadmap we needed for our next phase of growth.
Dr. Seung Hwan (Mark) Lee
Associate Dean, Engagement and Inclusion, Ted Rogers School of Management, Toronto Metropolitan University
A wide base of evidence, tested across multiple sources.
Discovery combined interviews, a workshop, staff survey data, policy documents, workflow documentation, technology review, and operational evidence.
The audit gathered evidence from the people leading the work, the people doing the work, the systems supporting it, and the documents governing it. This gave the audit enough breadth to compare perspectives before findings were used in scoring, analysis, and recommendations.
10 interviews, one workshop, staff survey data, policy documents, workflow documentation, technology review, and operational evidence.
Capturing AI usage, training needs, adoption readiness, and policy awareness across roles and functions.
Documented and used to support scoring, analysis, and recommendations.
Not a survey. An evidence base.
The audit did not rely on a single source of input. Leadership views, staff experience, workflow evidence, policy context, and the technology environment were compared before findings were used in the final analysis.
A picture built from evidence, not assumptions.
The result is a documented view of AI readiness that leadership can use to discuss priorities, training, governance, tools, and next steps with confidence.
Privacy as the precondition for honest evidence.
Identifying information was removed before analysis began, and findings were reported as departmental observations rather than individual attribution.
Removed before analysis.
Identifying information was stripped from the working record before analysis, so the audit reasoned about the department, not about individuals.
Departmental, not personal.
Findings were written as observations about the department's operating reality, not statements attributed to specific staff members.
Confidential to the client.
This case study describes the engagement. Client findings, scores, and recommendations remain confidential.
Privacy is not a postscript. It is a precondition.
When staff trust that their input will not be tied back to them, they speak plainly. When the report reasons about the department instead of individuals, leadership can act on the evidence directly.
41 workflows and 50 technologies, mapped to how the work actually runs.
The audit documented priority processes, recurring workflows, and supporting activities, along with the platforms and tools the department uses to deliver them.
Captured across three levels of operational depth.
Platforms, extensions, and tools currently in use across the department.
Priority processes, recurring workflows, and supporting activities.
As a participant in the AI Audit process, I felt it was inclusive and broad in its scope, ensuring diverse perspectives were integrated. The final report was very comprehensive, serving as a powerful tool for our leadership team moving forward.
Jacob Alajajian
Senior Manager, Alumni Relations, Ted Rogers School of Management, Toronto Metropolitan University
Scored using TC-AIMM and aligned to recognized AI governance standards.
Maturity was scored using TC-AIMM, the thinkCircle AI Maturity Model, with scored areas aligned to recognized standards for AI management, AI risk, and responsible AI.
TC-AIMM
The thinkCircle AI Maturity Model. Proprietary to thinkCircle.
ISO/IEC 42001
International standard for AI management systems.
NIST AI RMF
AI risk management guidance.
OECD AI Principles
International principles for trustworthy and responsible AI.
Traceability without exposing the method.
The full client report included scoring detail, standards alignment, and a traceable evidence trail from discovery inputs to pillar findings, risks, opportunities, and recommendations. This public version protects proprietary scoring methodology while showing the rigor behind the engagement.
12 scored opportunities. 11 documented risks.
Each opportunity was assessed for context, value, feasibility, and risk.
Scored opportunities
Assessed and prioritized across workflow automation, data management, communications, reporting, governance, and training.
Documented risks
Identified, characterized, and reported so risks could be considered alongside opportunities.
Recommendations accounted for the department's role inside a larger publicly governed institution, including the constraints that shape what it can act on independently.
This AI Audit was absolutely critical for our team. We had an immediate need to better understand how to move forward with governance, policy, training, and the effective use of AI tools. The insights gave us the clarity and roadmap we needed to proceed confidently.
Donna Muirhead
Director, Cooperative Education and Career Services; Managing Editor, Early Talent Insights; Ted Rogers School of Management, Toronto Metropolitan University
Want similar clarity for your organization?
Book Discovery Call11 core report sections, 7 supporting appendices, and an 8-document Governance Foundation Pack.
The final package was built for leadership, operational, technical, and governance audiences. Detailed client findings, scores, recommendations, and appendix contents remain confidential.
11 sections.
Methodology, executive findings, maturity scoring, gap analysis, opportunity prioritization, roadmap, readiness, technology, process inventory, and executive action.
7 appendices.
Scoring detail, opportunity profiles, survey data, process detail, standards alignment, technology inventory, and privacy and retention documentation.
8 documents.
Supporting governance roles, acceptable use, data handling, tool registration, vendor risk, incident response, review cadence, and role-based training.
Enablement materials.
Case study, team presentation, and chatbot, agent, and agentic AI explainer.
sections
appendices
Pack documents
The engagement produced a complete client package across reporting, evidence, governance, and enablement.
Chris Meunier
Founder and Chief Strategist, thinkCircle
thinkCircle is a strategic consultancy specializing in AI audits, assessments, implementation, and advisory. Its work is powered by TC-AIMM, the thinkCircle AI Maturity Model, a proprietary model designed and built by Chris Meunier.
Chris built TC-AIMM and leads the discovery, analysis, scoring, and decision framing behind every engagement. He has completed 17 AI programs across MIT Sloan, Vanderbilt, and DeepLearning.AI.
He serves as Co-Chair of the Marketing Advisory Council at the Ted Rogers School of Management, lectures there, recently delivered AI guest lectures at McGill Desautels Faculty of Management, and received the TRSM 2025 Alumni Volunteer Leadership Award.
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