AI Readiness Executive Report
A board-ready preview of the report Attest generates after an organization completes the AI Readiness Assessment. Sample data below shows how readiness can be analyzed by department, manager hierarchy, adoption behavior, risk, tool demand, training need, and governance execution path.
Northstar Financial Group
Enterprise AI governance baseline, workforce readiness, and recommended execution plan.
Ready, with targeted governance gaps
AI adoption is broad and employee demand is strong. Risk awareness and policy clarity lag behind usage, especially in Finance, Human Resources, Legal, and customer-facing teams.
Score composition
AI is already operational, but governance is uneven.
81% of respondents use AI at least weekly, but only 48% are confident about which information should not be entered into public AI tools. The immediate opportunity is not tool suppression; it is approved-tool rollout, role-specific guidance, and measurable attestation.
8,900 weekly hours of potential productivity lift.
Respondents identified summarization, drafting, meeting notes, data analysis, and coding support as the largest repeatable opportunities. Engineering and Operations have the strongest near-term adoption capacity.
Sensitive data handling is the readiness constraint.
Finance, Legal, Human Resources, and Customer Support show elevated concern around confidential data, candidate records, customer information, and AI-generated output review.
Your Governance Program Is Ready
The assessment shows what is happening: readiness, department scores, risk areas, governance gaps, and training gaps. The paid governance platform turns these findings into policy, guidance, training, attestation, and evidence.
Governance documents to generate
AI Acceptable Use Policy, Data Classification Policy, and AI Data Handling Guidance.
Department guidance to tailor
Finance, Human Resources, Engineering, Legal, and Marketing addendums based on assessment signals.
Campaigns to launch next
AI Awareness, Prompt Safety, Responsible AI Usage, and manager-led enablement.
Department readiness
Department scoring combines adoption, opportunity, risk awareness, policy clarity, training demand, and demand for approved AI tooling. Reports use aggregate results and do not expose individual answers.
Ready for approved developer AI with source-code controls.
Engineering AI addendum and secure coding guidance.
Strong productivity opportunity; needs brand and review standards.
AI content policy and disclosure checklist.
Needs manager-led enablement and workflow-specific training.
Operations pilot and process automation training.
AI use is constrained by financial data handling uncertainty.
Finance AI guidance and spreadsheet validation rules.
Requires strict contract, privilege, and review expectations.
Legal AI addendum and review workflow.
Requires recruiting and employee-data protections before expansion.
HR AI addendum and PII training.
Useful for response support once escalation rules are clear.
Customer data handling and AI response review training.
Manager hierarchy insights
Manager-level views help executives target enablement without turning the assessment into employee surveillance. Scores roll up through the management hierarchy and can be filtered by minimum response thresholds.
Tool demand
| Tool | Demand | Interpretation |
|---|---|---|
| Microsoft Copilot | 64% | Broad office productivity demand. |
| ChatGPT Enterprise | 48% | General research, drafting, and analysis demand. |
| GitHub Copilot | 37% | Developer workflow acceleration. |
| Claude | 29% | Document analysis and policy drafting interest. |
| Google Gemini | 18% | Lower demand in this Microsoft-heavy environment. |
Training demand
| Training need | Signal | Priority |
|---|---|---|
| Data handling guidance | 61% | Critical |
| Approved AI tools | 57% | Critical |
| Role-specific examples | 46% | High |
| Prompt writing basics | 42% | Medium |
| Management guidance | 33% | Medium |
Anonymized employee feedback
Free reports summarize employee feedback without exposing individual response datasets. These comments are representative examples of the kind of anonymized insight executives can review.
I spend hours writing status reports.
I am unsure what information may be entered into AI tools.
We need approved AI solutions.
Managers need practical examples for our department.
Top feedback themes
- Demand for AI productivity tools: Employees want approved tools for writing, summarization, research, and analysis.
- Uncertainty regarding acceptable use: Teams need clear guidance on what can and cannot be entered into AI systems.
- Need for AI training: Data handling, prompt safety, and role-specific examples are the strongest training requests.
- Concerns regarding sensitive data: Finance, Legal, HR, and Customer Support need department-specific rules.
Governance recommendations
Use assessment results, requested tools, sensitive-data concerns, and department gaps to draft the core policy.
Generate Engineering, Finance, HR, Legal, and Customer Support addendums with practical examples and review expectations.
Prioritize data handling, approved tools, role-specific examples, and manager guidance.
Run attestation campaigns after policies and training are approved, then retain immutable evidence.
Benchmark snapshot
Recommended 90-day governance program
Approve tool strategy and generate the AI Acceptable Use Policy draft.
Review department addendums for Engineering, Finance, HR, Legal, and Customer Support.
Launch AI awareness training focused on data handling, approved tools, and human review.
Run knowledge checks and manager briefings for lower-readiness departments.
Launch policy attestation campaigns tied to final policy versions.
Export evidence package and executive progress report for risk, audit, and compliance stakeholders.
Turn AI discovery into a governance program.
Start with the readiness assessment, then generate the policy, training, attestation, and evidence path your organization needs.