AI assistance across the platform

How AI chat and AI-assisted workflows support explanation, refinement, and drafting across AssureGrid while preserving user review, approval, and attestation responsibilities.

Overview

AssureGrid provides AI assistance across multiple parts of the platform. In practice, users encounter AI either as chat-style help, context-aware assistance, or embedded suggestion flows inside the module they are already working in. The purpose is to accelerate review work, improve clarity, and help users draft or refine content without disconnecting them from the underlying workflow.

The experience can look slightly different from one module to another, but the operating principle is consistent: AI supports the user, it does not replace judgment. Users may ask the AI to explain content, propose edits, compare versions, or help draft improved text, but they still decide what becomes part of the final audit record.

Core principle

The most important rule is simple and should be kept in mind across all AI-supported experiences in AssureGrid:

AI can assist with suggestions, explanation, or refinement, but the user remains responsible for review, approval, and attestation where applicable.

What AI is used for in AssureGrid

• Generating structured outputs that help users move work forward faster.

• Suggesting edits or refined wording for existing content.

• Explaining rows, sections, or selected content in plain language.

• Helping users compare original content against an improved version.

• Supporting consistency across records, findings, and narrative sections.

What AI is not used for

• An automatic approval system.

• A replacement for attestation.

• A silent save mechanism for sensitive or review-sensitive changes.

• A substitute for user review during formal audit checkpoints.

How AI chat works in AssureGrid

AssureGrid AI is best understood as a conversational assistant embedded inside audit workflows. Where AI chat is available, users can ask questions in natural language, request improved wording, or ask for help interpreting the content currently in view. Rather than moving into a separate tool, the user can stay in context and work against the rows, sections, or records already on the page.

That means AI chat supports both understanding and action. A user may ask, “Explain this control row,” “Refine this issue description,” or “Rewrite this report section to be more concise.” The answer may remain explanatory, or it may introduce a draft suggestion that the user still needs to review and decide whether to use.

What users should expect from AI chat

• A chat response may be explanation-only and may not change saved content at all.

• A suggestion can be strong and polished without automatically becoming the final version.

• The user may still need to apply, accept, save, or attest separately depending on the workflow stage.

• Formal checkpoints, including approval and attestation, remain explicit user actions.

Two common AI usage patterns

Most AI interactions in AssureGrid fall into two patterns. Framing them this way helps users understand when they are simply learning from AI versus when they are being asked to make a content decision.

AI Assistance Modes

Explanation-only assistance

• The user asks the AI to explain what something means.
• Typical examples include explaining a control row, a test step, a report section, or a suggested refinement.
• The AI responds with interpretation or clarification.
• No saved content change is required.

Suggestion or edit assistance

• The user asks the AI to propose a better version of existing content.
• Typical examples include refining selected control inventory cells, rewriting a report paragraph, or improving an issue description.
• The AI proposes a change for the user to review.
• The user decides whether to apply, edit further, reject, or save the result.

Where AI appears

AI appears in multiple modules, but the user control model remains consistent: AI supports understanding and refinement, while the user decides what becomes part of the final audit record.

Module	How AI chat or AI assist helps	What the user still controls
Control Inventory	Explains selected rows, columns, or cells; proposes clearer wording; supports selection-based revision requests and general questions asked in natural language.	Users decide whether to keep the original content, use a suggestion, or revise manually.
Audit Planning	Helps interpret or refine planning content such as RCM rows, test steps, evidence entries, and walkthrough questions.	Planning outputs still require user review before they are treated as final working content.
Data Refinements	Shows original content alongside suggested refined content so users can compare and improve records.	Users choose whether to keep the original, accept the suggestion, or edit the content themselves.
Issue Management	Improves issue descriptions, finding clarity, severity-related wording, and consistency across issue rows.	Users decide whether the suggestion becomes part of the saved issue log.
Audit Report Generation	Rewrites sections, improves clarity, adjusts tone, and helps make report text more concise and polished.	Users review and approve suggested report language before it is saved into the report.

Selection-based AI

In majority of modules in the platform users can provide selection context before asking for assistance. Instead of treating the page as a single undifferentiated document, the user can point the AI toward the exact part of the inventory that needs interpretation or refinement.

• Rows - useful when the user wants an explanation or revision for a specific control entry.

• Columns - useful when the user wants help understanding a field or comparing content across a dimension.

• Cells - useful when the user wants the AI to focus on a precise wording issue or data point.

This makes the response more targeted than modules where AI operates at a whole-row or whole-section level. The user can either ask a general question with no selection or ask for a context-rich revision tied to the current selection scope.

Approval and save behavior

Users should always distinguish between seeing a suggestion, applying a suggestion, and saving the final result. This matters most in review-sensitive modules such as Data Refinements, Issue Management, and Audit Report Generation, where the AI output may look polished enough to feel final even though the user still needs to confirm the next step.

A practical way to think about the workflow is shown below.

flowchart LR
    A[User request<br/>Ask a question or request a revision]
    B[AI response<br/>Explanation or proposed edit]
    C[User review<br/>Assess accuracy, fit, and intended use]
    D[Apply<br/>Use the suggestion]
    E[Reject or edit<br/>Keep original or refine manually]
    F[Save<br/>Commit the final selected result]
    G[Attestation remains a separate user action]

    A --> B --> C
    C --> D --> F
    C --> E
    E --> G

Operational rule A generated answer, suggestion, or polished rewrite should not be treated as approved, saved, or attested unless the interface explicitly shows that those actions were completed.

Review-sensitive checkpoints

• Data Refinements - compare original content and suggested refined content deliberately.

• Issue Management - review whether the suggestion should become part of the issue log.

• Audit Report Generation - confirm that tone, clarity, and final wording are appropriate before saving.

How users should work with AI effectively

Users should identify the content that needs help, ask for the right kind of assistance, and only save or attest once the final result is appropriate.

1. Identify the row, section, or content block that needs help

   In modules such as Control Inventory, a focused selection gives the AI better context and leads to more useful explanations or revisions.

2. Decide whether you need explanation or a proposed edit

   Use explanation mode when you need understanding, and revision mode when you want a new version of the content to review.

3. Review the AI response carefully

   Assess whether the response is accurate, sufficiently specific, and appropriate for the audit context before taking action.

4. Apply only the version you want to keep

   Do not assume the AI suggestion is mandatory. Keep the original, refine manually, or apply the suggested version only when it improves the result.

5. Save or attest only when the final result is appropriate

   Approval-sensitive actions remain user decisions. Saving and attestation should happen only after review is complete.

✓

Best practices

• Use AI to accelerate review, not bypass it.
• Ask clear, specific questions so the AI can respond with useful context.
• Use manual edits when exact wording or control precision matters more than speed.
• Review every AI suggestion before saving it.
• Remember that approval and attestation remain user responsibilities.

Common misunderstandings

“AI generated it, so it must already be final.”
Generated or suggested content often still requires review.

“AI suggestion means saved change.”
In several modules, the user still needs to apply or approve the result.

“AI can replace attestation.”
Attestation is a formal workflow checkpoint and remains a user action.