Spreadsheet Governance Checklist for CRM & AI Projects

Avoid costly errors when your CRM and AI start talking to each other

If your team still relies on ad-hoc spreadsheets to document CRM fields, owners and refresh rules, you’re exposing customer data, wasting hours on reconciliation and increasing the risk of leaking personally identifiable information to AI tools. This guide gives you a practical spreadsheet governance checklist and a ready-to-build Excel register designed for CRM & AI projects — tracking data ownership, field definitions, refresh cadence and validation rules so you can adopt CRMs and models like Gemini safely and efficiently in 2026.

Why this matters in 2026: CRM + AI = huge opportunity — and new risks

Late 2025 and early 2026 accelerated a familiar trend: CRM vendors embedded LLM-powered features for summarisation, lead scoring and automated outreach, while consumer-facing assistants (notably, integrations using Google's Gemini) made AI-driven workflows mainstream. That unlocks productivity but magnifies governance gaps:

• AI models consume fields from CRMs and spreadsheets — a single mis-tagged PII field can flow into prompts and external services.
• Automated scoring or recomputed fields become business-critical; poor lineage means no one knows why numbers changed.
• Auditors and GDPR/UK regulators expect data provenance and minimisation for AI use — spreadsheets are often the weakest link.

Practical spreadsheet governance bridges the gap. It documents what data exists, who owns it, how it’s refreshed, and which fields are allowed to be used with LLMs like Gemini.

The core Spreadsheet Governance Checklist (at-a-glance)

Start with these 12 essentials. Each item is expanded below with implementation notes and Excel techniques.

1. Data Ownership: assign clear owners with contact and SLA
2. Field Definition: canonical name, type, format, allowed values
3. Source & Transformation: original system, ETL/Power Query steps, mapping
4. Refresh Cadence: frequency, method, last run timestamp
5. Validation Rules: business checks and formulas with pass/fail status
6. AI Allowed Flag: whether a field may be sent to LLMs (Gemini-safe?)
7. Data Classification: PII, Sensitive, Internal, Public
8. Retention & Archive: retention period and archival location
9. Access Controls & Sharing: who can edit/view and where file is stored
10. Audit Trail: change log, version history, checksum or row counts
11. Incident Management: owner contact for data incidents and escalation
12. Review Cadence: when metadata is reviewed and by whom

Spreadsheet register: columns to build in Excel (with examples)

Create a single workbook (or SharePoint list) as your register. Use a top-level sheet called FieldRegister. Below are recommended columns with sample values and dropdown options to standardise entries.

1. System — source system name (e.g., Salesforce, HubSpot, ManualSheet)
2. Table / Object — logical grouping (e.g., Contact, Opportunity)
3. Field Name (canonical) — agreed name used in reporting (e.g., CustomerEmail)
4. CRM Field — actual field path (e.g., Contact.EmailAddress)
5. Description — short business definition
6. Data Type — text, number, date, boolean, currency (use dropdown)
7. Allowed Values / Format — regex or example (e.g., email format: *@*.com)
8. Data Owner — person or role (e.g., Sales Ops)
9. Data Steward — day-to-day maintainer
10. Source Update — API, Manual Upload, Power Query, ETL tool
11. Refresh Cadence — Hourly / Daily / Weekly / On-demand
12. Last Refreshed — datetime (auto-filled if possible)
13. Validation Rule — short formula or description (e.g., email contains '@' and domain in approved list)
14. Validation Status — Pass / Fail / Warning (calculated field)
15. AI Allowed — Yes / No / Masked (dropdown; determines if field can be sent to Gemini)
16. PII — Yes / No (dropdown)
17. Retention (months) — integer
18. Compliance Notes — GDPR, consent details
19. Change Log Link — link to audit sheet or ticket

Make these columns the canonical metadata for every field you use in reports or AI prompts. Use Excel Data Validation to enforce dropdowns and reduce errors.

How to implement key governance features in Excel — step-by-step

1. Data Validation and controlled vocabularies

Use Excel's Data Validation for consistent entries:

• Create a sheet called Lists with named ranges: DataTypes, RefreshCadence, AIAllowed, PIIFlag.
• On FieldRegister, set Data Validation > List referencing =DataTypes, etc.
• For formats, use Custom validation with a formula. Example for email:

=AND(ISNUMBER(SEARCH("@",[@[CRM Field]])),LEN([@[CRM Field]])>5)

Replace [@[CRM Field]] with the actual cell reference if not using structured tables.

2. Validation Status column (auto-checks)

Build a column that evaluates your rule and returns Pass/Fail. Example formula (simplified):

=IF(AND(NOT(ISBLANK([@[Field Name]])),[@[PII]]<>"",[@[Data Owner]]<>""),"Pass","Fail")

For field-level business checks (e.g., email format or non-negative values) use a helper sheet that runs actual record-level checks via Power Query or formulas and writes a summary back into FieldRegister (row counts, errors).

3. Last Refreshed and automated refresh signalling

Track when upstream ETL or Power Query runs:

• If using Power Query: add a query step to write the load timestamp into a small CSV or use Power Automate to update the Last Refreshed cell via the Graph API.
• For manual uploads, use a macro to prompt the uploader to record the timestamp and username.
• Store a simple row count and checksum (e.g., concatenation or HASH) per load to detect silent failures.

4. Audit trail and change logging

Spreadsheets lack native field-level auditing. Combine these approaches:

1. Store your register on SharePoint/OneDrive to use version history for file-level tracking.
2. Use a Power Automate flow or a small VBA macro to capture edits and append rows into a separate ChangeLog table with timestamp, user, sheet, cell, old value, new value and change reason.
3. For every automated refresh, append a row to a LoadHistory sheet with rowcount, file hash (optional), runtime, and success/failure status.

Minimal VBA example to log edits (paste into ThisWorkbook):

Private Sub Workbook_SheetChange(ByVal Sh As Object, ByVal Target As Range) Dim wsLog As Worksheet Set wsLog = ThisWorkbook.Sheets("ChangeLog") wsLog.UsedRange.Clear Application.EnableEvents = False wsLog.Cells(Rows.Count, 1).End(xlUp).Offset(1, 0).Value = Now wsLog.Cells(Rows.Count, 1).End(xlUp).Offset(0, 1).Value = Application.UserName wsLog.Cells(Rows.Count, 1).End(xlUp).Offset(0, 2).Value = Sh.Name wsLog.Cells(Rows.Count, 1).End(xlUp).Offset(0, 3).Value = Target.Address wsLog.Cells(Rows.Count, 1).End(xlUp).Offset(0, 4).Value = Target.Value Application.EnableEvents = True End Sub

Note: adapt this for your environment and consider Power Automate for cloud-hosted workbooks to avoid macro risks.

Controls for AI integration — how the register should guard Gemini and other LLM usage

LLMs create a new axis of risk. Use the register to decide what can be shared with a model and how. Implement these controls:

• AI Allowed flag: explicit Yes / No / Masked for every field.
• PII Masking rules: where AI is allowed but must receive masked data (e.g., replace email with domain only).
• Prompt sanitisation: centralised routine that pulls fields permitted by the register and redacts any disallowed fields before building prompts.
• Prompt & Output Logging: store inputs and outputs in a secure log with timestamps, model version (Gemini vX), and privacy tags for audits.
• Consent & Purpose: link fields to consent records and allowed AI use-cases (e.g., automated summary for account manager only).

Example rule implemented in processing layer (pseudo logic):

for each field in record: if FieldRegister.AIAllowed = 'Yes' and FieldRegister.PII = 'No' then include raw value else if AIAllowed = 'Masked' then include masked(value) else skip end

This pattern prevents accidental PII disclosure to Gemini or third-party endpoints. As The Verge and industry coverage showed in 2024–2026, large platform integrations make it trivial to send data to LLMs — governance must be explicit.

RACI and ownership — who does what

Define a simple RACI for governance tasks tied to your FieldRegister:

• Data Owner (Responsible): approves definitions, retention and AIAllowed flags.
• Data Steward (Accountable): maintains FieldRegister and runs validation checks.
• System Admin (Consulted): configures refresh and connectors (Power Query, API keys).
• Security & Privacy (Informed): approves PII, masking and AI usage, manages logging.
• Business Users (Responsible for data input): follow rules and report incidents.

Attach SLAs to key actions: e.g., data owner responds to change requests within 3 business days; steward runs validation weekly for high-risk fields.

Practical checklist you can print and follow

1. Create FieldRegister workbook and Lists sheet within 48 hours of starting CRM integration.
2. Populate at least System, Table/Object, FieldName, DataType, DataOwner for all report fields before going live.
3. Mark AIAllowed and PII fields; set Masked where necessary.
4. Implement Data Validation dropdowns and a ValidationStatus formula.
5. Automate LastRefreshed using Power Query loads or Power Automate update flows.
6. Set up ChangeLog using Power Automate or VBA and store workbook on SharePoint/OneDrive.
7. Document sanitisation process for prompts to Gemini and log all inputs/outputs.
8. Run a privacy review and sign-off for AI use-cases before production rollout.

Illustrative example — how a UK SME used this register

We worked with a London-based B2B reseller preparing to enable AI-driven sales notes using a CRM add-on powered by Gemini. They had 120 custom fields across Contacts and Opportunities and relied on spreadsheets to track field meanings. Problems were common: sales notes contained free-text PII (emails, contract terms) and AI prompts occasionally summarised confidential clauses.

Using the register model above, they:

• Tagged 18 fields as PII and set AIAllowed=No for all of them.
• Marked 12 fields as Masked to allow model-driven summarisation without leaking raw values.
• Implemented a simple Power Automate flow that built AI prompts only from AIAllowed=Yes fields and appended prompt & response to a secure log for audit.
• Added checks so any time a new custom field is created in Salesforce, an automation task opened a ticket for the Data Owner to update the register before that field was used in AI workflows.

Outcome: the team kept their AI features but eliminated accidental PII exposure and reduced manual redaction time by making governance explicit.

Advanced strategies and future-proofing (2026+)

As vendors ship more native AI in CRMs, governance must evolve:

• Adopt data contracts between source systems and reports — the register can be the canonical contract. Treat the register as a source of truth for CI/CD tests.
• Use automated tests: create Power Query / Excel tests that run on pull requests to detect schema drift or unexpected NULLs.
• Maintain model lineage and model card metadata. Record which model version (e.g., Gemini vX) was used in each AI run.
• Consider storing embeddings and prompts in a governed vector store with access controls, rather than ad-hoc spreadsheets.
• Perform periodic red-team testing of prompt sanitation and data labelling — a common 2025–26 best practice.

Common pitfalls and how to avoid them

• Storing the register as a local file: always use a collaborative platform (OneDrive/SharePoint) to capture version history and enable flows.
• Allowing free text in AIAllowed: restrict to explicit dropdown choices and require owner sign-off for changes.
• Relying solely on user training: embed checks and automation so compliance is enforced by design.
• Not logging prompts/outputs: if you can’t reproduce or explain an AI decision, it becomes impossible to defend in audits.

Next steps — how to operationalise this in a week

1. Day 1: Create the FieldRegister workbook and Lists sheet. Populate the top 20 business-critical fields.
2. Day 2: Add Data Validation dropdowns, DataOwner and Data Steward contacts. Put the workbook on SharePoint.
3. Day 3: Implement LastRefreshed mechanism (Power Query or Power Automate) and a LoadHistory sheet.
4. Day 4: Create ChangeLog via Power Automate or add the VBA macro. Test with a few edits.
5. Day 5: Define AIAllowed rules for the critical set and integrate prompt sanitiser in the middleware that calls Gemini.
6. Day 6–7: Run a smoke test with stakeholders, sign off, and schedule weekly validation checks.

Final recommendations

Spreadsheet governance is no longer a 'nice-to-have' — it's a fundamental control when your CRM and AI services collaborate. Use the FieldRegister approach to make decisions explicit, automate checks where possible, and treat AI interactions as first-class auditables. The combination of clear metadata, refresh provenance, validation rules, and AI flags transforms spreadsheets from brittle sources of truth into governed assets.

Call to action

Ready to get started? Download our free Excel FieldRegister template and step-by-step checklist at excels.uk, or book a short consultation to review your CRM + AI governance in 30 minutes. Protect your data, speed up onboarding and keep Gemini-powered features compliant — without slowing your team down.

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