The Skill Gap Analysis helps you decide where your next learning investment should go. It breaks the organization's AI-related skills into five signals, ranks them from lowest to highest, and automatically generates suggested improvement actions for the bottom two. Instead of stopping at "we're short across the board," it helps you choose the one place to start.
- Understand what each of the five signals measures
- Choose your next learning investment from the bottom two signals
- See how widely departments differ and decide where to dig deeper
- Take the suggested improvement actions back to your meetings
Opening the Skill Gap Analysis
Click Skill Gap Analysis (スキルギャップ分析) in the left sidebar. The URL is /en-US/{organization ID}/executive/skills.
You're on the right screen if the introduction at the top says it analyzes five AI-related skill signals across the organization (active rate, AI learning completion rate, path progress, average test score and certification rate) and shows each department's strengths and shortfalls.
What the five signals are
This screen looks at the five signals below. Each one is expressed on a 0–100 scale. On the dashboard, "skill maturity" is the average of three of them — path progress, average test score and certification rate — and the "company-wide AI adoption index" is the average of the active rate and the AI learning completion rate.
Active rate (アクティブ率)
The share of employees who signed in and used Manabu2 in the last 30 days
AI learning completion rate (AI学習完了率)
The share of employees who have completed at least one AI-related course
Path progress (パス進捗率)
Average learning-path progress across all employees (employees with no path assigned count as 0%)
Average test score (テスト平均点)
The average score of test results (employees who haven't taken a test count as 0)
Certification rate (認定取得率)
The share of employees who have earned at least one certificate
What's on the screen
A line chart of lessons completed over the last N months, used as a stand-in measure of learning momentum. If there are no completions, you'll see a message saying no lessons have been completed yet (まだ完了レッスンがありません。).
A bar chart comparing each department's maturity (the average of path progress, average test score and certification rate) side by side. Departments with no members aren't shown. The y-axis is fixed at 0–100.
Five small cards, ordered from the lowest score up. Each card shows the signal name, its score (0–100) and a color (red below 40, yellow otherwise), so strengths and weaknesses stand out.
Recommended actions are generated automatically from the two lowest-scoring signals and shown as a bulleted list. Each item gives the signal, its score out of 100 and a fixed piece of advice for that signal — for average test score, for example, the advice is to review the difficulty and consider providing review material or sending learners back to a foundation course.
Common tasks
- Read the five-signal gap details (5指標のギャップ詳細) from the left (lowest first).
- Look at the two lowest signals and use the suggested improvement actions (改善アクション提案) as a starting point.
- If the active rate is low, for example, consider reminder emails or follow-up through department managers.
- If the certification rate is low, review the conditions for issuing certificates and the tests at the end of courses.
- Look at the skill maturity by department (部門別 スキル成熟度) bar chart.
- Check the gap between the highest and lowest departments (the skill gap).
- If the gap is large (say, 30 points or more), open AI Adoption by Department (部署別 AI活用状況) in the left sidebar and dig into each department's AI adoption index and maturity.
- Check the last six months in the monthly trend chart.
- If it's climbing, your measures are working.
- If it's falling, suspect the active rate first, and confirm it in the gap details on the same page.
- You can copy the bullet points under suggested improvement actions straight into meeting material or an internal email.
- If you need a fuller report, download a PDF, Excel or CSV file from Reports (レポート出力) in the left sidebar.
Where to look when a signal is low
"The score is low" can mean different things: the likely cause and the right response depend on which signal it is.
| Low signal | Likely cause | Next move |
|---|---|---|
| Active rate | People can't find time to learn | Agree on learning time during working hours with department managers |
| AI learning completion rate | The content is too hard, or it isn't sparking interest | Ask the curriculum managers to add an introductory course |
| Path progress | Too many paths assigned, or many people dropping out midway | Ask HR to narrow down assignments and follow up |
| Average test score | Understanding isn't sticking | Strengthen the in-lesson quizzes and introduce a review template |
| Certification rate | Completion requirements are too high, or people drop out at the last step | Redesign the certificate conditions and consider incentives |
Troubleshooting
| Symptom | Likely cause and fix |
|---|---|
| The trend chart doesn't appear | No lessons in your organization's courses have been completed in the last six months. As soon as employees complete lessons, they show up for that month. |
| A message says department data hasn't been aggregated yet (部門データがまだ集計されていません。) | No departments are registered, or the departments have no members. Add them from the department list (部署一覧) in HR. |
| All five signals are 0 | No employees are registered yet, or there's no learning data. Suggested actions are always generated from the bottom two signals, so treat them as a rough guide while data is thin. |
| The suggestions feel off the mark | Suggestions are generated automatically from low signal scores, so they can miss what's really happening on the ground. Compare them with the actual data on the AI Adoption by Department page before deciding. |
| You want the exact definitions of the five signals | See "What the five signals are" above. |
