Find out what AI and automations exist before deciding how to govern them

What is Diagnosis, inventory and roadmap?

The AI Governance Diagnostics, Inventory, and Roadmap is the first essential step of any AI governance initiative in an enterprise. Before defining policies, controls, or operating models, the organization needs a reliable and up-to-date picture of what AI solutions, automations, low-code applications, agents, and data flows actually exist, both those managed by IT and those created by business departments outside of their direct visibility. Many organizations find that their actual digital footprint extends far beyond formally approved solutions. Marketing, finance, operations, and HR teams create Power Apps, Power Automate flows, n8n or Make workflows, conversational agents with Copilot Studio, databases in Supabase, apps generated with Cursor or Lovable, spreadsheets with critical macros, and connections to external APIs using personal accounts, free trials, or corporate cards without purchase evaluation. This phenomenon of shadow IT and shadow AI is not inherently malicious: it often represents valuable innovation from professionals close to the business process. However, without visibility and controls, the organization cannot manage security, continuity, privacy, and cost risks in an informed way. Q2BSTUDIO approaches discovery through a structured methodology that combines interviews with process managers, technical review of tenants and platforms, analysis of identity and access patterns, examination of billing and licensing, and inspection of repositories and connectors. The goal is not to monitor or sanction, but to fully understand: what exists, who uses it, what data it processes, what systems it connects to, who maintains it, what happens if it fails, and what happens when its creator leaves the organization or changes roles. Once the assets have been identified, we classify them using a criticality framework that considers the operational impact on the business process, the sensitivity of the data processed, the regulatory exposure (GDPR, sectoral regulations, AI Act when applicable), the concentration of dependency on people or accounts, technical maintainability and direct and indirect costs. Not all shadow applications deserve the same level of control: a personal productivity prototype is treated differently than automation moving financial data between systems or an agent interacting with customers. The inventory we deliver is designed to be maintained, not archived. We define mandatory minimum fields, data owners, review cadence, and, where appropriate, integration with existing CMDB or GRC tools. The risk map translates the classification into actionable priorities, pointing out immediate containment needs, quick wins and strategic decisions that require architectural or organizational changes. The roadmap distinguishes five categories of intervention for each asset: regularize (incorporate the government with current technology), reinforce (add specific controls while maintaining the platform), migrate (move to a more suitable platform or architecture), replace (replace with an existing corporate solution) or retire (decommission with data and process transition). Each recommendation includes justification, dependencies, effort estimation, and risks. This service is not a course or training on AI governance concepts: it is a professional project that produces tangible and actionable deliverables. It also does not include penetration testing (pentesting) or technical application security auditing; When the diagnosis detects potential vulnerabilities, they are documented in the risk map so that they can be addressed in the appropriate scope. The diagnosis can be limited to a department, a technology platform or a business unit, or cover the entire organization. Q2BSTUDIO recommended starting with the perimeter that concentrates the assets with the highest risk or greatest uncertainty, and expanding systematically.