AI tools can appear highly capable, but responsible adoption means looking beyond the demo to understand fit, risk, and control. Assess each of the seven areas below before making any commitment.
Problem–Solution Fit
AI is not the right answer to every question. Start by asking whether AI is actually solving the root problem or adding complexity to avoid it.
- Is AI solving the real issue, or could a simpler approach work?
- What does success look like if this tool is implemented?
Data Implications
Every AI tool needs data to function. Understanding what data is required and who controls it is non-negotiable before adoption.
- What data does this tool require?
- Who controls the data both technically and contractually?
- Will data be stored or reused outside our control?
Risk Profile
AI introduces risks to people, communities, and relationships. Identify and name the worst-case scenarios before you proceed.
- Could the system produce biased or harmful outputs?
- What are the privacy and security risks?
- What happens if something goes wrong?
Security and Architecture
Many AI platforms aggregate data access across multiple systems, which creates significant exposure if permissions are not carefully designed. The principle to apply is source-level permission inheritance: an AI system should only surface information that the user requesting it is already authorized to see — not create new or expanded access pathways. Certifications like SOC 2 Type II and ISO 27001 are a useful baseline signal, but they do not replace a thorough evaluation of how the specific tool handles your data.
- Does the system respect existing access controls?
- Are SOC 2, audit logging, and SSO (Single Sign-On) in place?
- Does the vendor use our data to train their models?
Cultural Harm and Appropriateness
Not all data or knowledge should enter AI systems. Some knowledge carries cultural protocol obligations that override technical capability.
- Could this tool misinterpret or expose cultural knowledge?
- Does this use align with community values and protocols?
- Are there risks to relationships or trust?
Vendor Practices and Accountability
Vendors shape how AI systems operate. Their transparency and contractual commitments directly affect your ability to govern the system.
- Do contracts clearly define data ownership and control?
- Are there limits on how our data can be used?
- Who is accountable if harm occurs?
Transparency and Control
In high-impact contexts, AI must never be a black box. Your organization must be able to understand, guide, and stop how the tool operates.
- Can we understand how outputs are generated?
- Can we pause or stop use if concerns arise?
- Can we audit and review how the system is being used?