A growth-stage SaaS company shipping AI features faster than it can govern them — the roadmap has outrun the accountability structure around it.
A healthcare provider or health-tech company deploying AI in a setting where a compliance failure is not just reputational — it needs governance and infrastructure risk assessed from a clinical-data standpoint from day one.
What You Can Expect
A defensible, evidence-based inventory of AI use across the organization; a prioritized risk register leadership can act on immediately; a roadmap that sequences governance investment against actual exposure rather than generic best practice; and a document the Board can be shown without embarrassment.
When This Applies
A director asks, in a Board meeting, "where exactly are we using AI, and what could go wrong?" — and no one in the room has a confident, evidence-based answer.
An enterprise customer's procurement or security team sends a questionnaire asking about AI governance controls, model risk management, and vendor AI oversight — and the answers need to be real, not aspirational.
Leadership suspects employees are using ChatGPT, Copilot, or other AI tools on sensitive data without sanction, but has no inventory or evidence to act on.
In Their Own Words
"As a Chief Risk Officer, I need a defensible AI inventory and risk register so that I can answer the Board's questions with evidence instead of reassurance."
"As a VP of Compliance, I need to know which AI tools touch patient data before our next accreditation review, not after."
How We Work
Deliverables
AI inventory (spreadsheet + narrative); risk register (scored and prioritized); executive workshop (half-day, in-person or remote); final executive report (15-25 pages); prioritized 12-month roadmap; Board presentation deck.
Technology & Tools
Technical inventory of AI-enabled SaaS tools and browser extensions in use
Gap analysis against internationally recognized AI management standards
Scored, prioritized risk register delivered in a reusable format
Frameworks Applied
Artificial intelligence management systems — organizational accountability, AI policies, lifecycle governance, risk management and continuous improvement.
The Govern, Map, Measure and Manage approach for identifying and controlling risks arising from AI systems.
How Success Is Measured
Representative Scenarios
Situation: A regional financial services firm suspected AI tools were in use beyond IT-approved software but had no way to confirm it.
Approach: A four-week AI Governance Assessment combined structured interviews with a technical discovery scan across departments.
Outcome: Leadership received an evidence-based inventory revealing several unsanctioned AI tools processing customer data, with a prioritized remediation roadmap delivered to the Board within the same quarter.