Data
Raw facts, records, events, fields, and documents. AI can retrieve, classify, and structure them; people define quality and permitted use.
Interactive Framework · Free
What Information Should AI Trust?
A practical framework for deciding which organizational information AI may use—and where human judgment must remain.
- Audience
- Managers, transformation, operations, PMO, and data leaders
- Outcome
- A practical path to improve source trust
How to use this framework
Choose one source and answer from the evidence available to you. Do not paste documents, names, or sensitive material into this page: the check is button-based, runs only in your browser, and does not store or transmit answers. The result is a practical discussion aid, not a certification or audit. Use it to set a safe first boundary: can AI retrieve from the source, summarize it with citations, help a person compare it, or should the organization first improve the source itself?
Repeat the check for the specific intended use. A source might be suitable for a human-reviewed summary of current facts but not for an automated decision. The right answer depends on decision risk, permitted use, and whether a reviewer can trace an important claim back to the original record.
Data, information, knowledge, and wisdom
AI can organize, summarize, and reason over information. Humans still own wisdom: judgment, accountability, tradeoffs, values, and the decision about what should happen next. That distinction is practical, not academic.
Information
Data organized with context. AI can summarize and explain what happened; people validate meaning and relevance.
Knowledge
Validated patterns, rules, context, and lessons. AI can surface patterns and apply defined rules; people confirm context, exceptions, and applicability.
Wisdom
Judgment under goals, constraints, ethics, and risk. AI can provide evidence and challenge assumptions; people own the decision and accountability.
An AI may answer fluently from a data set, but data alone does not tell it which tradeoff an organization should make. A model cannot quietly inherit the authority to approve an exception, decide a customer impact is acceptable, or choose between competing values.
What makes a source trustworthy for AI
Certified sources beat excellent prompts built on questionable information. A trustworthy source is authoritative for the question, has a business owner, is current enough for the decision, has understood definitions and gaps, and is explicitly permitted for the intended AI use. A usable workflow also makes source references and timestamps visible. When information is absent, stale, or conflicting, the AI should say so rather than guess.
Traceability turns a plausible answer into a reviewable one. A person should be able to follow an important answer to a record, version, date, policy section, or report. Conflict handling matters for the same reason: organizations often have multiple systems or documents that look credible but disagree. Define which source wins, who resolves the disagreement, and when the workflow pauses.
Trust is not determined by file type
A spreadsheet is not automatically untrustworthy, and a database is not automatically safe. A manually maintained Excel file can be useful when it has a known owner, a clear purpose, version history, and a reconciliation process. A system of record can still mislead if fields are poorly defined, refreshes fail, or the AI use exceeds its permitted boundary. The assessment asks about governance and evidence rather than rewarding a particular format.
Start with grounded answers and human review before allowing information to trigger actions or automation. Sensitive or restricted information needs explicit boundaries even when it is accurate. An approved policy may be an excellent source for explaining current rules; it does not necessarily authorize an AI to decide an exception to those rules.
Examples: trusted, conditional, and untrusted sources
Often trustworthy when governed
- System-of-record transactions and databases with clear definitions and owners
- Approved policies, procedures, and controlled standards with a current effective date
- Governed data sets and validated reports with a known refresh cadence
- Approved contract, vendor, risk, issue, or project repositories with source links and ownership
- Enterprise platforms with defined access, retained history, and accountability
Conditionally useful
- Manually maintained Excel files with a known owner and version history
- SharePoint libraries with explicit ownership, review dates, and approved content
- CRM or operational exports that need freshness checks or reconciliation
- Current meeting notes, status reports, and project documents that need validation
- Feedback, survey comments, and transcripts used for themes rather than final facts
Do not treat as authoritative alone
- Old folders with unknown ownership or dates
- Duplicated documents, stale final-v2 files, and uncontrolled personal files
- Email threads and chat messages
- Slide decks used as a substitute for current records
- Unlabeled attachments or copied data with no known source
- An AI model's general memory or an unsupported answer
Three practical ways to handle legacy information
- Improve what exists. Clean, archive, classify, validate, correct, and assign ownership to the useful portion of the estate. This is appropriate where history remains valuable and a clear source of record can be established.
- Create a trusted future state. Build a repository with a clear taxonomy, templates, ownership, and review standards. Move new work into it deliberately instead of trying to make every historical file equally reliable.
- Constrain the AI boundary. Permit AI to use only approved folders, documents, tables, columns, or systems of record, and deliberately ignore the rest. A smaller, known boundary is often safer and more useful than broad access to an uncontrolled archive.
Humans own wisdom
AI can make evidence easier to find and assumptions easier to challenge. It can compare an approved set of options against criteria people set. It cannot own the downstream consequence. A human approves changes, resolves conflicts, and takes responsibility for consequential decisions. That is especially important when the work affects people, money, service, compliance, reputation, or safety.
Make this visible in the workflow. State what AI may use, what it must cite, what uncertainty looks like, who reviews the result, and what happens when sources conflict. These controls help teams learn from real work without treating a polished output as unquestioned truth.
Frequently asked questions
Is this a formal AI governance audit?
No. It is a practical source trust assessment for a single intended use. It does not certify a source or replace legal, privacy, security, records, or compliance review.
Can AI use a source with some gaps?
Often, but only for constrained work such as summarizing, extracting, comparing, or preparing material for human review. Show the source basis, caveats, and uncertainty rather than treating the output as final fact.
Should a trusted source allow autonomous action?
Not by itself. Automation also needs a bounded action, defined rules, an exception path, traceability, and a human owner. Start with grounded answers and review before expanding authority.
What if two trusted sources disagree?
Pause the affected conclusion, expose the conflict, and follow the defined conflict rule. If no rule exists, assign a qualified owner to resolve it before AI output is used as a basis for action.