How Talvio scores AI training opportunity across a workforce.
Talvio turns a roster of job titles and departments into a role-level AI readiness map. The score estimates where AI training is most likely to improve productivity, quality, decision support, documentation, and workflow execution. It is not a displacement score and it is not an evaluation of individual employees.
The short answer
Talvio maps each role in a workforce roster to an O*NET-aligned Talvio Job Profile, then evaluates that profile against custom AI attributes and use case categories such as documentation, research, data analysis, decision support, forecasting, planning, drafting, evaluation, and customer triage. The result is a 0 to 10 training opportunity score plus department, tier, role, and use case views that help leaders decide where AI training should start.
How the analysis works
| Step | What Talvio does | Why it matters |
|---|---|---|
| 1. Read the roster | Talvio accepts an Excel or CSV file with only job titles and departments. Talvio discourages uploading sensitive data such as names, employee IDs, Social Security numbers, PHI, or other individual identifiers. | The analysis can be run from workforce structure without relying on sensitive individual data. |
| 2. Normalize and group titles | Uploaded job titles are cleaned, grouped, and counted so repeated titles can be analyzed consistently. | Leaders get a role map rather than a noisy row-by-row spreadsheet. |
| 3. Match to a Job Profile | Each role is matched to a Talvio Job Profile aligned with an O*NET occupation and assigned a match confidence. | The analysis starts from a standardized occupational baseline, then uses Talvio's enriched profile layer instead of relying only on local title wording. |
| 4. Apply the AI attribute layer | Talvio reads profile data such as tasks, work activities, knowledge areas, work context, technology signals, education, outlook, and custom AI use case attributes. | This is where the analysis moves beyond occupation lookup into a practical view of where AI can help the work. |
| 5. Score and explain training opportunity | The profile is evaluated across 14 AI use case dimensions, then combined into a 0 to 10 score with rationale, confidence, and top training areas. | The output points to training priorities, not generic AI adoption advice. |
| 6. Build planning views | Scores roll up into role, department, training tier, AI use case, match review, and per-role Job Profile views. | Teams can move from assessment to a training roadmap. |
The Talvio Job Profile layer
Talvio does not simply display an O*NET occupation and attach a score. O*NET and BLS data provide the occupational backbone, but Talvio turns that backbone into Job Profiles designed for AI training decisions. Each profile combines standard labor-market and occupation signals with Talvio's custom AI-readiness attributes, use case scores, rationales, and training-area guidance.
This profile layer is what lets Talvio answer a more useful question than "what occupation is this?" The goal is to answer: "what kinds of AI training are likely to help people in this role, and how should leaders prioritize that training across the workforce?"
| Profile component | What it includes | How it supports the score |
|---|---|---|
| Occupational foundation | O*NET-aligned title, SOC code, role description, job zone, education level, occupational tasks, work activities, knowledge areas, and work context. | Gives each local job title a stable reference point so the analysis is not driven by inconsistent internal naming alone. |
| Labor and technology signals | BLS-style outlook information, technology skills, role context, and other occupation-level signals used to describe the work environment. | Adds practical context about how the role is performed and where digital or analytical workflows may already exist. |
| Custom AI attributes | AI readiness score, confidence, rationale, top training areas, primary AI use cases, and 14 use case dimensions including documentation, data analysis, research, forecasting, decision support, planning, translation, code, and customer triage. | Converts a static occupation reference into an AI training profile that can identify which workflows are most teachable and valuable. |
| Local workforce context | Your roster's job title, department, headcount, match confidence, and any user-approved match corrections. | Connects the profile library back to the actual organization so leaders can act by department, role, tier, and cohort. |
What the score means
| Score range | Interpretation | Typical training action |
|---|---|---|
| 8 to 10 | High AI training opportunity. The role has many text, analysis, documentation, planning, or decision-support workflows. | Prioritize for early cohorts, workflow-specific practice, and manager enablement. |
| 5 to 7 | Moderate opportunity. AI can help important parts of the role, but the work may also include hands-on, regulated, or real-time human judgment tasks. | Target specific workflows rather than broad tool training. |
| 0 to 4 | Lower direct training opportunity. The core work may be physical, site-specific, procedural, or less computer-mediated. | Focus on supporting tasks such as documentation, scheduling, lookup, reporting, or supervisory workflows. |
What Talvio is designed to avoid
- Talvio is designed to work without employee names, employee IDs, performance ratings, or sensitive individual records.
- Talvio does not rank individual employees.
- Talvio does not recommend layoffs, compensation changes, promotions, or terminations.
- Talvio is a workforce planning and training prioritization tool, not an automated HR decision system.
Common methodology questions
Why use O*NET?
O*NET provides a standardized occupation structure, which helps Talvio interpret local job titles consistently. A local title such as "Client Success Coordinator" can be mapped to a broader occupational profile before AI attributes and use cases are scored.
Are Talvio Job Profiles just O*NET pages?
No. O*NET is the occupational reference layer. Talvio Job Profiles add custom AI training attributes, use case dimensions, profile rationales, confidence signals, and workforce-specific context so the output can guide training prioritization.
Why not call this AI exposure?
Exposure can sound like job risk. Talvio is focused on training opportunity: where AI skills are likely to help people do the work better, faster, or with less administrative load.
Can users correct matches?
Yes. The Match Review view shows confidence for each job title match and lets users correct matches that need local context.