Parts Salespersons
The routine layer of this job is compressing.
A substantial share of this occupation's activities overlaps with what AI already does well. Expect the routine layer to compress first, and the human-anchored layer to gain weight and value.
Where this occupation sits among all 785 measured. Bands are quintiles of the real distribution. Applicability measures task overlap with observed AI usage. It is not a probability of job loss.
What this job involves
Sell spare and replacement parts and equipment in repair shop or parts store.
Where AI overlaps this work, activity by activity
The occupation's most important work activities (O*NET weights, normalized to this set), each with its measured AI performance across 200,000 real conversations: how often AI is used for it, how well it completes it, and how much of the activity it covers.
| Work activity | Share of role | AI performance |
|---|---|---|
| Execute financial transactions. | 25.8% | Moderate 0.52 |
| Collect data about consumer needs or opinions. | 11.8% | Not observed 0.00 |
| Explain technical details of products or services. | 10.1% | High overlap 0.81 |
| Advise others on products or services. | 8.6% | High overlap 0.79 |
| Purchase goods or services. | 6.6% | Moderate 0.52 |
| Monitor resources or inventories. | 6.4% | Not observed 0.00 |
| Prepare documentation for contracts, applications, or permits. | 5.9% | High overlap 0.66 |
| Inspect characteristics or conditions of materials or products. | 5.5% | Moderate 0.50 |
| Clean tools, equipment, facilities, or work areas. | 5.0% | Not observed 0.00 |
| Stock supplies or products. | 4.9% | Not observed 0.00 |
| Determine values or prices of goods or services. | 4.7% | Moderate 0.59 |
| Analyze data to improve operations. | 4.7% | Not observed 0.00 |
"Not observed" is a measurement, not missing data: that activity does not meaningfully appear in the usage sample. AI performance = completion × scope × coverage (user-goal view), 0–1.
What drives this score: the biggest AI-overlapped activity in this role is execute financial transactions; the most important activity AI is not observed doing is collect data about consumer needs or opinions. The plan below is built around exactly that split.
Move first: compress the routine before it compresses you.
Next 30 days
- Take the exposed layer yourself. Your most AI-overlapped activity, Execute financial transactions, is exactly what a well-tooled colleague will accelerate. Be that colleague first.
- Time-box the routine. Measure how long your repeatable tasks actually take; that's the budget AI adoption will compete against, and the time you will reinvest.
Next 90 days
- Double down where humans hold. Grow the share of your week spent on work like Collect data about consumer needs or opinions, measured as outside AI's current reach, and take on more of it explicitly.
- Build one AI-assisted workflow end-to-end for your team and own its quality. Being the local standard-setter compounds.
The longer game
- Position at the boundary. The valuable seat in partially-exposed occupations is the translator: someone fluent in both the domain and the tools. Certifications matter less than a demonstrated workflow portfolio.
- Recheck yearly. Elevated-band occupations move: new tools shift specific activities fast. The data vintage is printed at the top of this page.
Where to put your learning time
The activity most exposed to AI on this page is execute financial transactions. The honest hedge is not leaving the occupation, it is owning what the tools still cannot carry: judgment, review, and accountability for the output. If you want structured practice, audit a course free before paying for any certificate.
- Full task, skill and pay profile for this occupation on O*NET OnLine Free
- Browse role-relevant courses on Coursera Partner link
Most Coursera courses can be audited free; payment covers graded work and certificates. The partner link earns this site a commission if you subscribe, at no extra cost to you, and it never changes a score, a ranking or a plan here.
- AI applicability score and per-activity metrics: Tomlinson, Jaffe, Wang, Counts & Suri, Working with AI: Measuring the Applicability of Generative AI to Occupations, Microsoft Research 2025 (arXiv:2507.07935), data CC BY 4.0.
- Occupation description and activity structure: O*NET 30.0 Database, USDOL/ETA, CC BY 4.0.
- Bands, personalization weights and action plans are JobRiskAI editorial layers; method and limitations on the methodology page.