Sample Report

AI Compliance Report

Acme Corporation — 200 employees, Tech sector

41/100

Developing

11 points below the compliance floor (52). See the full pillar breakdown, risk register, and 90-day roadmap below.

Interactive sample report — scroll to explore all sections

TwinLadder

AI Competence Assessment Report

Report date

7 April 2026

Acme Corporation

Sector: Tech

Employees: 200

Location: US

Report type: Sample dashboard company

Executive Summary

41 /100

Developing

Acme Corporation scores 41 out of 100 on the TwinLadder AI Competence Standard, placing the organisation in the “Fast Car, No Brakes” archetype — strong tool adoption running ahead of the governance and evidence structures needed to use AI safely. The current gap to the Article 4 compliance floor stands at 11 points.

This is not unusual for a technology company at this stage. The risk is not the AI itself — it’s the growing distance between what teams do with AI daily and what the organisation can document, govern, and defend to a regulator.

The action plan below is designed to close that gap. If all planned actions land, Acme Corporation projects a +70-point lift, taking the score to 100 and above the compliance floor — with the governance infrastructure to sustain it.

Signature Competence Radar

Organisation Archetype

Fast Car, No Brakes

Your organisation has adopted AI tools ahead of the governance structures needed to use them safely. Awareness and tool adoption are comparatively strong, but evidence capture and governance lag behind — creating a widening gap between what your teams do with AI and what you can prove about how they do it.

The red dashed line marks the Article 4 compliance floor at 52. Your radar shape reveals the asymmetry: capability is running ahead of control. The priority is not to slow down adoption — it’s to build the evidence and governance infrastructure that lets you accelerate safely.

Department Competence Heatmap

This heatmap is derived from the same sample-company data shown in the dashboard. It reveals where competence, ownership, and risk are uneven across departments.

Department	Deploy.	Policy	Training	Tools	Evid.	Gov.	Avg
HR 18 lead · 23% training complete	54	40	31	46	14	13	32	33
Exposure: HireVue for screening is Annex III high-risk. Largest literacy and governance exposure across the org.
Legal 15 lead · 53% training complete	62	40	39	46	14	13	32	35
Exposure: High-risk contract review and due diligence AI (Harvey). Evidence capture and validation controls still weak.
Operations 35 lead · 30% training complete	49	41	31	54	15	22	39	36
Exposure: UiPath RPA and ChatGPT for process automation. Tool adoption strong but governance ownership underbuilt.
Engineering 45 lead · 47% training complete	57	41	39	52	15	22	39	38
Exposure: Code generation tools in daily use. IP exposure and code-leak risk need clearer policy controls.
Marketing 22 lead · 30% training complete	52	44	31	54	18	25	42	38
Exposure: Generative content adoption (Jasper, ChatGPT) ahead of policy guardrails and formal review routines.
Customer Success 29 lead · 30% training complete	52	44	31	52	18	25	42	38
Exposure: Salesforce Einstein for customer scoring and churn prediction. Automated decisions need transparency.
Finance 16 lead · 22% training complete	54	46	31	52	20	27	44	39
Exposure: Tableau AI for forecasting and reporting. GDPR-relevant financial data processing needs classification.
Product 20 lead · 34% training complete	54	46	39	50	20	27	44	40
Exposure: AI-assisted research and testing used widely. Data privacy for user research needs guardrails.

People & Workflow Literacy

This section makes the gap concrete at the workflow level, not just in the aggregate score.

HIGH RISK Workflows still under-supported

HR hiring uses AI-supported people workflows, but training coverage is still missing and governance clarity is weak.

Legal review is relatively advanced, yet evidence capture still lags behind tool usage and policy expectations.

Marketing content has adopted tools quickly, but policy and disclosure guardrails are behind the pace of use.

Literacy Gap Summary

200

Total employees

68

Using AI tools daily

11

Have completed structured training

84%

Untrained AI users

Connected Action Plan

Each action below ties directly to the current score gap, a named owner, a timeline, and an expected score lift.

Action	Pillar	Responsible	Timeline	Expected lift
Centralise Evidence Repository Create a single repository for training records, policy sign-off, vendor reviews, and assessment outputs.	evidence	Sarah Kim, General Counsel	Week 1-2 By 2026-04-05	+7 pts
Approve AI Usage Policy v1 Finalize baseline AI usage rules, prohibited use cases, and human review requirements for all departments.	policy	Sarah Kim, General Counsel	Week 3-4 By 2026-04-12	+6 pts
Launch Article 4 leadership training Enroll department leads into the baseline AI literacy and oversight training track.	training	Diana Torres, VP of HR	Week 5-6 By 2026-04-18	+8 pts
Complete AI tool inventory review Validate all AI-enabled tools currently in use and classify them by owner, purpose, and risk level.	tools	Marcus Rivera, VP of Engineering	Week 7-8 By 2026-03-28	+5 pts
Establish AI governance cadence Create monthly review meetings with Legal, HR, and Operations for AI incidents, approvals, and remediation.	governance	James Chen, CEO	Week 9-10 By 2026-04-22	+6 pts
Upload board AI strategy presentation Board presentation delivered 2026-02-28 needs to be digitised and uploaded as evidence of board-level AI accountability.	evidence	Sarah Kim, General Counsel	Week 11-12 By 2026-04-10	+4 pts
Complete FRIA for HireVue recruitment tool Fundamental Rights Impact Assessment required under Article 27 for high-risk AI in employment decisions.	governance	James Chen, CEO	Week 13-14 By 2026-04-15	+6 pts
Schedule skills gap assessment per department Formal role-based gap analysis required for Article 4 compliance. Currently only done informally.	training	Diana Torres, VP of HR	Week 15-16 By 2026-04-25	+4 pts
Distribute AI literacy survey results Share Q1 2026 AI literacy survey findings with department leads and create improvement action plans.	awareness	Sarah Kim, General Counsel	Week 17-18 By 2026-03-20	+4 pts
Review and update GDPR data processing register for AI tools Data processing register (AI tools section) exists in OneTrust but needs export and formal review. Currently past review date.	policy	Sarah Kim, General Counsel	Week 19-20 By 2026-04-08	+4 pts
Document escalation paths for high-risk workflows Map and formalise escalation paths for all AI-assisted workflows classified as high-risk under the EU AI Act.	authority	James Chen, CEO	Week 21-22 By 2026-04-20	+6 pts
Assign accountability owners to all AI-assisted decisions Designate named individuals responsible for oversight and final sign-off on each AI-assisted decision process.	authority	James Chen, CEO	Week 23-24 By 2026-04-25	+6 pts
Conduct quarterly delegation scope review Establish a recurring quarterly review to assess whether AI authority delegation boundaries remain appropriate and compliant.	authority	James Chen, CEO	Week 25-26 By 2026-05-01	+4 pts

Projected score after plan completion

41 current + 70 planned = 100

Closes the floor gap and moves above 52

Roadmap & Recommendations

Week 1-3

Evidence pillar reaches Developing

Evidence and documentation score crosses 26.

Week 4-6

Compliance floor reached

Overall compliance score reaches 52.

Week 7-9

Leadership training completed

All department leads complete baseline Article 4 training.

TwinLadder

AI Competence Standard v1.1 · Public sample report aligned with dashboard sample-company data.

twinladder.ai

Acme Corporation41/100

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