EXPOSED
The analytical half of this job — pipeline reports, quota tracking, territory splits, call-quality scoring, commission reconciliation, forecast decks — is exactly what AI dashboards and LLM summarizers already produce, and CRM platforms are shipping it as a default feature. What survives is people accountability: hiring and firing reps, coaching a struggling closer through a bad quarter, walking into a key account when a deal is dying, and owning the number to the VP. Expect the same supervisor to run a bigger team with less admin staff, which shrinks headcount without eliminating the role.
Most of this decline happened after 2021 — it is not the pandemic dip.
Median pay $74,760 → $87,520 -6.3% in real terms
This line is counted by the Bureau of Labor Statistics — the one figure on this page that isn't a judgement of ours. Headcount moves on demand, offshoring, demographics and the business cycle, and automation is one term among several, often not the loudest.
So a falling line is not evidence that AI did it, and a rising one is not evidence that it won't. Both happen in this register: some occupations resist automation and shrink anyway, others are highly automatable and keep growing. The marked year is 2020.
BLS projection, 2024–2034
0%
Percentage only. The projection counts a different population from the 214,390 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, and shrinking
Both signals point the same way: the tasks are largely automatable and the BLS projects 0% by 2034. This is the case where the score and the forecast agree, and it is the one worth taking seriously.
Different clocks. The score is what current AI could do to this work today. The projection is how many of these jobs will exist in 2034. Everything between the two — how fast employers actually adopt, whether demand grows in the meantime — is why they can point opposite ways without either being wrong.
~24,800 openings a year on average, including replacing people who leave.
Sales LeaderSales CounselorSales SupervisorShift SupervisorSales Team LeaderDry Cleaning ManagerTelesales SupervisorTerritory SupervisorBulk Plant SupervisorDesk Clerks SupervisorSales Floor SupervisorCirculation Crew LeaderDriver Sales SupervisorInside Sales SupervisorReservations SupervisorStock Broker SupervisorTelemarketer SupervisorSubscription Crew LeaderTelemarketing SupervisorClient Service SupervisorInsurance Sales SupervisorCustomer Service SupervisorInsurance Agents SupervisorInsurance Office Supervisor
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier Roughly half your week — pipeline hygiene reviews, quota attainment reports, ride-along call scoring against a rubric, commission dispute math, weekly forecast roll-up — is already generated by Salesforce/Gong-class tooling, but the termination conversation, the PIP that has to hold up in an unemployment hearing, and the escalation call to a $2M account cannot be handed to a model, which is why this sits at 10 and not 5.
Some physical or field component You are on a screen and a phone for most of the day, but the job still puts you in cars and airports for joint sales calls, at trade-show booths, and physically walking a distributor's warehouse or a client's plant floor — enough recurring off-site presence to clear the desk-only band without approaching field-installation work.
No licence, no signature requirement No state licence gates supervising outside sales reps; the only credentials in play are employer-specific product certifications or an insurance/securities licence held by the reps themselves, and when a deal goes bad it is the company, not you personally, that answers for it.
Meaningful discretion You decide who gets the fat territory, who gets cut in a reduction, how far to discount to save a quarter-end deal, and whether to escalate a rep's questionable expense or discount practice — high-consequence calls made on incomplete information with your name on them, though the comp plan and discount-approval matrix put real walls around how far you can go.
The verdict above describes this occupation as a whole. Almost nobody does the typical version of a job — tick what's actually in your week and see how your own mix sits.
Your task mix speaks to task resistance (10/20 here) — how much of the day's work current AI already does. That is the dimension the boxes above are about.
It cannot move the other three. Liability shield (3/20) is whether the law requires a licensed human to sign. Trust premium (12/20) is whether buyers specifically pay for a person. Judgment and accountability (13/20) is whether the role exists to own consequential calls. Those are facts about the occupation's standing, not about which tasks are in your week — a paralegal who does only trial exhibits still holds no licence. Together they are 28 of this occupation's 44 points (64%).
Embodiment (6/20) is also a property of the work rather than the worker, but we don't tag individual tasks as physical or not, so the picker can't tell you anything about it. That's a limit of this tool, not a claim.
Did we get the list right? Tell us what's missing — the tasks are written from the outside, and you're reading this from the inside.
Gambling Managers EXPOSED
The moves above are yours to make. This is the other half: what would have to change in the world for the occupation itself to score higher. None of it is in any one person's gift, but it is where the floor actually comes from. Scores here are not a one-way ratchet. Only two of the five dimensions — task resistance and embodiment — track what machines can do. The other three track law, what buyers will pay for, and who is answerable, and those move in both directions, often in response to the same pressure AI creates. If every lever below landed, this occupation would score around 58/100, still EXPOSED.
Task-mix shift: once CRM-native AI (Salesforce Einstein/Agentforce, HubSpot Breeze) fully absorbs pipeline hygiene, forecast decks, commission reconciliation and call scoring, the residual day is hiring/firing, performance-management documentation, escalation into dying deals, and cross-team negotiation — work that current models cannot do at usable quality. The role narrows to the judgment tier rather than disappearing.
Employment-law exposure around AI-assisted personnel decisions concentrating the call on the named supervisor: NYC Local Law 144, Illinois HB 3773 (effective 2026) and the EU AI Act's 'high-risk' employment classification all require meaningful human review of automated hiring/termination scoring. If employers respond by making the first-line supervisor the documented human decision-maker of record on every rep termination and PIP, the role formally owns consequential calls under ambiguity.
Sector-specific supervisory licensing already exists in slices of this SOC and could broaden: FINRA Series 24/9-10 requires a registered principal to personally supervise and approve broker communications and correspondence, and state insurance codes require a licensed agency supervisor. If FINRA extends principal review-and-approval duties explicitly to AI-generated client outreach and suitability recommendations (a live topic in FINRA's 2024-25 Reg Notice on generative AI), the named supervisor becomes personally sanctionable and unremovable in those verticals.
Narrow route only: in large B2B and channel/distributor sales, buyers contractually name a human account escalation owner. If enterprise MSAs increasingly specify a named human sales manager as escalation point with a no-AI-only-contact clause, the premium is real — but it attaches to the account relationship, not to supervision as such.
The limit. Embodiment has no route — this is desk, call and travel work. Even with every lever, the headcount story dominates: the levers protect the role's content, not the number of people doing it, since one supervisor with AI admin can carry a much larger span of control. Realistic ceiling in the high 50s, and only for the regulated (FINRA/insurance) and large-account slices; unregulated inside-sales team leads have little to gain.
| New York-Newark-Jersey City, NY-NJ | 18,300 | $116,980 +34% |
| Atlanta-Sandy Springs-Roswell, GA | 6,880 | $90,140 +3% |
| Los Angeles-Long Beach-Anaheim, CA | 6,860 | $80,800 -8% |
| Houston-Pasadena-The Woodlands, TX | 5,920 | $76,360 -13% |
| Dallas-Fort Worth-Arlington, TX | 5,530 | $77,870 -11% |
| Denver-Aurora-Centennial, CO | 5,430 | $115,390 +32% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 5,140 | $101,410 +16% |
| Miami-Fort Lauderdale-West Palm Beach, FL | 4,690 | $82,870 -5% |
| Boulder, CO | 730 | $136,380 +56% |
| Boston-Cambridge-Newton, MA-NH | 4,470 | $122,870 +40% |
| New York-Newark-Jersey City, NY-NJ | 18,300 | $116,980 +34% |
We have no reported case of a named organisation automating this occupation. Not one deployment, not one announcement.
That is worth saying out loud next to a score of 44. The verdict above is about what the work exposes — what current AI could do to these tasks. It is not a claim that anyone has done it. For this occupation those two things have come apart completely: the capability argument is on this page, and the evidence column is empty.
Has AI actually changed your work? One tap, anonymous, and the running tally is public. Nothing else is asked of you.
Rather than check back: get the digest and we'll tell you what changed — or watch a single occupation from its own page.