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Non-retail sales supervisors

Official title First-Line Supervisors of Non-Retail Sales Workers (SOC 41-1012)

Also called Sales manager, Sales supervisor, B2B sales manager, Inside sales manager, Inside sales supervisor

In brief

In May 2025, BLS counted 214,390 US non-retail sales supervisors, with median pay of $87,520 a year. Of the BLS counts since 2012, the highest was 252,670, in May 2016. The earliest figure here is an estimate of 85,000 from 1920, not measured the same way as today's count. The last full technology era for non-retail sales supervisors: Salesforce CRM + web-based pipeline management, 1999 to 2020.

What the sources say

Official outlook

BLS projects employment to change +0.5% from 2025 to 2035.

BLS Employment Projections 2025-35

AI exposure studies

Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. BLS publishes these counts as yearly survey snapshots and cautions against reading them as a precise trend. In May 2021 BLS changed how it estimates these counts (a model-based method using three years of survey data) and moved fully to the 2018 occupation codes, so part of a change across 2021 can come from the method rather than the work.

Last updated .

Cite this page
APA

Future History. (2026, October 11). Non-retail sales supervisors: jobs and pay since 1920 [Data set; data as of BLS OEWS May 2025; BLS Employment Projections 2025-35]. https://futurehistory.earth/timeline/41-1012

Chicago

Future History. "Non-retail sales supervisors: jobs and pay since 1920." Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35. Last modified October 11, 2026. https://futurehistory.earth/timeline/41-1012.

BibTeX

@misc{futurehistory_41_1012, author = {{Future History}}, title = {Non-retail sales supervisors: jobs and pay since 1920}, year = {2026}, month = oct, howpublished = {\url{https://futurehistory.earth/timeline/41-1012}}, note = {Last updated 2026-10-11. Data as of BLS OEWS May 2025; BLS Employment Projections 2025-35}, }

The access date is today's date on your device.

drag to travel through time
19001925195019752000now
2026
Known then as District Agent / General Agent / Sales Superintendent (early industrial sales hierarchy)
Known then as Sales Manager / District Sales Manager (corporate sales-force era)
Known then as Sales Supervisor / Branch Sales Manager / Field Sales Manager (diversified sector era)
Known then as First-Line Supervisors of Non-Retail Sales Workers (BLS SOC 41-1012)
Known today as First-Line Supervisors of Non-Retail Sales Workers (BLS SOC 41-1012)
Projected to still be called First-Line Supervisors of Non-Retail Sales Workers (BLS SOC 41-1012)
Data from the May 2025 survey
US Employment
214K
Est.

Stated in source: BLS OEWS

Official statistic
Report an error
Median Annual Wage
$87,520
Est.

≈ $85,276 in 2024 prices (derived by us, CPI-adjusted)

Official statistic
Report an error
Beat · 1884

John Henry Patterson buys the patents for James Ritty's cash register and renames the company National Cash Register. Facing theft by his own agents, Patterson invents the first systematic sales management hierarchy: numbered territories, written scripts, monthly quotas, daily written reports, and a district manager supervising each cluster of agents. This is the founding moment of the non-retail sales supervisor as a recognized occupational role in American commerce.

Beat · 1904

Businessman P. W. Searles summarizes the change that NCR's model had spread across American industry in just twenty years: where a salesman had once traveled as his own boss with no oversight, now routes were planned, customers evaluated before departure, and sales managers assigned territories and issued monthly and weekly quotas. The non-retail sales supervisor had become a standard organizational role in large American manufacturers and distributors by 1904.

Beat · 1916

Walter Dill Scott opens the Bureau of Salesmanship Research at Carnegie Institute of Technology, the first academic institution to study the selection, training, and supervision of sales workers systematically. Scott develops psychological tests to identify high-aptitude salespeople and frameworks for supervisors to evaluate and develop their reps. The bureau formalizes what NCR had pioneered empirically: sales supervision is a teachable discipline with an identifiable best practice.

Beat · 1920

By 1920, approximately one-quarter of the chief executives at the top 200 US industrial firms had backgrounds in sales, signaling the degree to which the sales management hierarchy had become the primary executive pipeline in American corporate life. The non-retail sales supervisor role was by this point a well-established stepping stone to senior management across insurance, pharmaceutical, industrial equipment, and consumer goods distribution.

Beat · 1987

ACT! ships as the first commercial contact-management software, replacing the paper Rolodex and call-report form that had defined the non-retail sales supervisor's administrative toolkit since the 1920s. For the first time, a supervisor's entire record of rep customer interactions, follow-up tasks, and deal histories existed in a searchable digital format. The transition from paper to digital was not immediate: most sales organizations still ran paper call reports through the early 1990s, but the direction was set.

Beat · 1993

Tom Siebel, departing Oracle, founds Siebel Systems with the explicit goal of building enterprise-grade Sales Force Automation (SFA) software. By 2002 Siebel holds 45% of the SFA and contact-management software market. For the non-retail sales supervisor, Siebel represents the first shared-pipeline view across an entire rep team: deal stages, close probabilities, and activity histories visible in one screen rather than assembled from paper forms. The forecast roll-up, historically an afternoon of phone calls and manual addition, becomes a database report.

Beat · 2000

The 2000 Standard Occupational Classification revises the BLS occupation codes, splitting the former "Supervisors, Sales Workers" category into retail (41-1011) and non-retail (41-1012) for the first time. This is the first year that the non-retail sales supervisor has a dedicated BLS occupational code. Simultaneously, Salesforce.com launches as a SaaS CRM, moving the supervisor's pipeline view from installed desktop software to a web browser and beginning the decade that makes CRM ubiquitous in non-retail sales organizations.

Beat · 2025

Salesforce announces general availability of Agentforce 360 in October 2025, a year after Agentforce itself became generally available, and reports 12,000 customers. The platform introduces autonomous AI agents that handle pipeline forecast roll-up, deal summaries, territory analytics, and next-best-action recommendations within Salesforce Sales Cloud without requiring the supervisor to run reports manually. Salesforce's State of Sales 2026 identifies three new job titles emerging inside enterprise sales organizations for the first time: Agent Supervisor, AI Ops Manager, and Agent QA Lead, all variants of the non-retail sales supervisor role adapted to managing AI-agent behavior rather than solely managing human rep behavior.

How to read this number

OEWS is a point-in-time survey snapshot, not a continuous time series; BLS advises against using it for year-over-year trend comparison.

Each dot is a cited figure over time; the dotted line only links them (values between aren't measured). Hollow dots are estimates.

Official statistic: Copied by a script from a statistics agency table or a published study's table, not retyped by hand. BLS figures and the forecast and exposure tables are also re-checked against that table on every data run. Researched context: Researched from the cited source rather than copied from it by a script, so it is not machine-checked yet. Spot a mistake? Each figure has a “Report an error” link.

Cited forecasts · present → 2035

Will AI replace non-retail sales supervisors? What the research says

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, we show the disagreement rather than an average.

Each figure is stated by the named source; we publish no forecasts of our own. Where sources disagree, the spread is the honest signal.

What published forecasts say
Forecast change in the number of people doing this work, as published by each named source. Bars share one scale on every role, centred on zero and reaching the end at ±30%. Grey marks a small change, 3% or less either way.
BLS Employment Projections 2025-35
→ 2035
+0.5%
BLS National Employment Matrix, 2025-35 cycle, line for SOC 41-1012 (First-line supervisors of non-retail sales workers): 311.9 thousand jobs in 2025, projected 313.6 thousand in 2035, a change of +1.7 thousand (+0.5%). Matrix employment counts all jobs in the occupation, including self-employed workers (30.4% of this occupation in 2025). The average for all occupations in this cycle is +3.5%.
Official statisticReport an error
How exposed is this work to AI (research measures)
Scores as published by each study. Each row says what it measures. These measure what AI could technically do, not forecasts of jobs lost.
Eloundou et al. (2023), "GPTs are GPTs"
2023 study · Share of tasks exposed to LLMs (β, human-rated)
44.4%
of tasks
What this measures

Published occupation-level exposure from the paper's occ_level.csv for O*NET-SOC 41-1012.00 First-Line Supervisors of Non-Retail Sales Workers. Shown: human-rated β = 0.444, the share of the occupation's tasks an LLM could do in at least half the time at equal quality, counting tasks that need LLM-powered software at half weight (β = E1 + 0.5 × E2). Other published measures for this occupation: human-rated α (LLM alone) 0.185 and γ (all LLM-assisted) 0.704; GPT-4-rated β 0.537. This measures how much of the work an LLM could touch, not how many jobs disappear.

Official statisticReport an error
Today, in this role

What's changing day to day for non-retail sales supervisors

Three parts of your work where AI is already doing real lifting, and what stays yours.

AI is sitting alongside you hereReview the AI-generated weekly pipeline forecast from Clari or Salesforce Agentforce, auditing the model's deal-by-deal risk scoring against your own knowledge of account relationships and competitive dynamics, overriding the model on deals the AI cannot score correctly (executive-sponsor engagement, procurement timeline shifts, competitive pressure not visible in CRM activity), and preparing a cohesive forecast narrative for the VP or CRO that explains both the number and the key risks.

Review the AI-generated weekly pipeline forecast from Clari or Salesforce Agentforce, auditing the model's deal-by-deal risk scoring against your own knowledge of account relationships and competitive dynamics, overriding the model on deals the AI cannot score correctly (executive-sponsor engagement, procurement timeline shifts, competitive pressure not visible in CRM activity), and preparing a cohesive forecast narrative for the VP or CRO that explains both the number and the key risks.[7],[4],[2]

Where your edge is

AI generates the forecast number; your value is the narrative. Senior leaders want to know not just the number but which three deals are most at risk, what you are doing about each one, and what your confidence interval is. Supervisors who can explain forecast variance with specificity (not just "rep confidence is high") are the ones who build credibility with CFOs and CROs, and who get promoted into VP-of-Sales roles. A great forecast narrative is an auditable track record of your judgment.

AI is sitting alongside you hereMonitor rep deal activity using Gong and Outreach AI-surfaced deal health alerts, reviewing the system's list of stalled or at-risk opportunities flagged by engagement decay signals (no buyer email response in 14 days, no multi-threading to the economic buyer, declining call-to-meeting conversion), then running individual pipeline review conversations with the reps on those deals to diagnose whether the stall is a buyer-side delay or a rep execution gap, and coaching on the specific next action to re-engage.

Monitor rep deal activity using Gong and Outreach AI-surfaced deal health alerts, reviewing the system's list of stalled or at-risk opportunities flagged by engagement decay signals (no buyer email response in 14 days, no multi-threading to the economic buyer, declining call-to-meeting conversion), then running individual pipeline review conversations with the reps on those deals to diagnose whether the stall is a buyer-side delay or a rep execution gap, and coaching on the specific next action to re-engage.[8],[9],[2]

Where your edge is

AI spots the stalled deals so you do not have to review every pipeline row manually. The high-value question is not "why is this deal stalled?" but "is it stalled because of something the rep can fix, or because the buying process has actually died?" Develop a diagnostic framework: if the buyer went dark after a demo, is it a budget freeze, a champion change, or a competitor win? The supervisor who can distinguish these cases accurately is coaching from insight, not from pressure. Reps respond to insight-driven pipeline reviews differently than to quota-pressure ones, and that difference shows up in deal velocity and close rates.

AI is sitting alongside you hereGovern team pricing discipline by reviewing Clari or Salesforce discount-depth flags on deals where rep-offered discounts exceed the team median, approving or denying pricing exception requests with a documented rationale, setting promotional pricing guidance for the quarterly marketing campaign cycle, and conducting a monthly team review of gross margin by rep to identify patterns of margin erosion that indicate either competitive pressure or pricing-governance drift.

Govern team pricing discipline by reviewing Clari or Salesforce discount-depth flags on deals where rep-offered discounts exceed the team median, approving or denying pricing exception requests with a documented rationale, setting promotional pricing guidance for the quarterly marketing campaign cycle, and conducting a monthly team review of gross margin by rep to identify patterns of margin erosion that indicate either competitive pressure or pricing-governance drift.[2],[7]

Where your edge is

Pricing discipline is one of the highest-leverage margin levers a supervisor controls. Most reps over-discount when they are anxious about losing a deal; the pattern is predictable and detectable. Use Clari's anomaly flags as your early warning system, but verify the context before denying a discount request. A competitive deal that genuinely requires a price exception is different from a rep training customers to always ask for a discount.

Where non-retail sales supervisors go next

Natural next steps for someone with your foundation: not exits, evolutions.

What each move takes

A direction you could grow

Sales Managers

The most direct upward path for a non-retail sales supervisor is into a Sales Manager or VP of Sales role overseeing a larger team or full regional book of business. The core task set is identical but the scope expands: more reps, a larger pipeline, executive-level forecast accountability, and cross-functional coordination with marketing, product, and finance. The gap is in the strategic and financial dimensions: building a territory model from scratch, designing a compensation plan, and managing a budget. Sales supervisors who have mastered AI tool governance (Gong scorecard design, Clari forecast auditing, Outreach cadence strategy) are increasingly competitive for VP-level searches because sales technology fluency is now an explicit job requirement in most enterprise sales management postings.

What you'd add
  • · Sales compensation plan design: quota-setting methodology (top-down vs. bottom-up), accelerator and decelerator mechanics, and how comp plan structure drives behavior at different attainment levels
  • · Financial modeling for sales: revenue capacity modeling (headcount x ramp x quota x attainment rate), bookings-to-revenue timing, and cohort analysis of rep performance by hire vintage
  • · Cross-functional coordination: structured communication with marketing (lead-volume SLAs, campaign feedback loops), product (competitive gap identification, roadmap input), and finance (forecast accuracy, budget variance)
  • · Executive presentation: building a 10-minute quarterly business review for the CEO or board that communicates pipeline health, rep performance, and strategic risks with data-backed clarity
  • · Advanced Salesforce: building custom report types, pipeline dashboards, and territory-model views that serve a VP-level perspective rather than a single-team supervisor view
What it takesSome new skills to pick up
Tools of the era

How has the work of non-retail sales supervisors changed since 1884?

The tools that defined the work, one line per era. Select an era to see how it reshaped the work.

  • Territory maps, written sales scripts, and daily written reports (NCR Patterson system)

    John Henry Patterson at NCR introduced the first systematized sales-management toolkit in American commerce: hand-drawn territory maps that divided the country into numbered districts, carefully written scripts that standardized how reps delivered their pitch, and mandatory daily written reports from every rep to their district manager. The district manager's job was to enforce this system, inspect rep performance against quota, deliver on-site demonstrations to struggling reps, and fire those who could not be turned around. This set of paper-based tools was the foundational infrastructure of non-retail sales supervision for the next four decades.

    Effect on the work

    By standardizing and measuring rep performance at scale, the Patterson system allowed a single district manager to supervise many reps systematically, a span of control that would have been impossible with informal oversight alone. The system was so effective that NCR alumni built the same structure at IBM, Burroughs, and Coca-Cola, spreading the model across American industry between 1900 and 1920.

  • Telephone + automobile (territory coverage and real-time supervision)

    The telephone transformed the non-retail sales supervisor's daily rhythm from weekly paper reports to near-daily voice check-ins with reps in the field. Before telephone ubiquity, a district manager's oversight was episodic: a written daily report, a periodic in-person visit. With the telephone, a supervisor could reach every rep the same day a deal stalled, a competitor price cut emerged, or a customer complained. The automobile extended territory coverage: supervisors could physically visit customers with reps, providing live coaching on calls rather than post-mortems on paper. By the 1950s the combination of telephone check-in and automobile ride-along had become the standard rhythm of non-retail sales management.

  • Typewriter + rotary phone + paper call reports (postwar sales management standardization)

    The postwar era standardized the non-retail sales supervisor's administrative tools: the typewriter for weekly territory reports and sales forecasts, the rotary (later touch-tone) telephone for the morning call to each rep, and the printed call-report form that reps filed after each customer visit. Sales organizations from insurance to pharmaceuticals to industrial distribution used essentially identical administrative systems based on the NCR model, adapted to their specific product categories. Forecasting was done by hand: supervisors tallied rep call reports, estimated close probabilities on each open opportunity, and rolled the numbers up to the district manager above them through a paper or typed forecast chain.

  • Contact management software: ACT! (1987), then Siebel SFA (1993)

    ACT!, released in 1987 as the first commercial contact-management software, gave non-retail sales supervisors the first digital tool for tracking every rep's customer interactions, follow-up tasks, and deal status. It replaced the paper call-report and the Rolodex. Siebel Systems, founded by Tom Siebel in 1993, went further: its Sales Force Automation (SFA) platform let supervisors view a shared pipeline across all their reps, track deal stages, and run the first computer-generated forecast roll-ups rather than paper summaries. By 2002 Siebel held 45% of the SFA and contact-management software market. This decade-long transition turned the non-retail sales supervisor from a manager of paper reports into a manager of digital pipeline data, compressing the forecast cycle and making performance variances visible in real time for the first time.

    Effect on the work

    SFA tools increased each supervisor's effective span of control by automating the pipeline data collection that had previously required individual phone calls and paper forms. Analysts at the time estimated that a Siebel-equipped supervisor could manage more reps than one working from paper reports alone, contributing to the modest employment decline from the early-1990s peak.

  • Salesforce CRM (SaaS, 1999) + web-based pipeline management

    Salesforce.com, founded in February 1999 and launched publicly in 2000 at a "No Software" rally in San Francisco, moved CRM from installed desktop software to a web browser subscription. For the non-retail sales supervisor, this was the shift that made the digital pipeline standard rather than optional: every rep updated their opportunities in a shared system accessible anywhere, and every supervisor could view the full team pipeline, close probabilities, and activity history without a specialized IT environment. The weekly forecast roll-up, which had required phone calls and manual data aggregation under Siebel, became a report generated in seconds from the CRM database. Salesforce became the dominant CRM platform of the 2000s and 2010s, and the non-retail sales supervisor's daily rhythm became inseparable from it.

  • AI revenue-intelligence platforms: Gong (2016), Clari (2012/2016 pivot), Outreach, Salesforce Agentforce

    Gong, founded in 2015 and reaching meaningful enterprise adoption from 2016 onward, introduced a new category of tool: it recorded and transcribed every sales call, then applied AI to score each call against configurable coaching rubrics. For the non-retail sales supervisor, this was the most significant change to the role since the shift from paper to CRM: the supervisor had previously been able to review one or two calls per rep per week by manual selection; Gong scored every call automatically and surfaced the lowest-scoring ones for coaching attention, giving supervisors full-team coaching visibility at scale for the first time. Salesforce Agentforce (generally available from October 2024) brought autonomous AI agents into the supervisor's daily CRM workflow. By 2026, Salesforce's State of Sales report found 87% of sales organizations using AI for at least one core supervisory workflow.

    Effect on the work

    Gong's 2025 study found that sales teams using its AI platform generated 77% more revenue per rep. Gartner (2025 Magic Quadrant for Revenue Action Orchestration) found that frontline sales managers still spent only 9% of their time coaching despite coaching being the highest-leverage supervisory activity, with administrative tasks absorbing the rest; AI tools freed supervisors from administrative work but the time savings had not yet been fully redirected to coaching at most organizations.

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First-Line Supervisors of Non-Retail Sales Workers employment and pay by year

On record since1884
Latest US employment214,390 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Latest US median pay$87,520 (May 2025)Source: BLS OEWS May 2025Official statisticReport an error
Outlook+0.5% by 2035Source: BLS Employment Projections 2025-35Official statisticReport an error
View all 28 years of cited US data, each linked to its source
YearUS employmentAnnual pay (median unless marked)SourceReport
192085,000n/aEst., Walter A. Friedman, Birth of a Salesman Researched contextReport
1950200,000n/aEst., US Census Bureau Researched contextReport
1956n/a$8,500Est., University of Missouri Libraries Researched contextReport
1990395,000n/aEst., IPUMS USA / BLS Researched contextReport
2000370,000$48,960Est., Bureau of Labor Statistics (employment) Researched contextBLS OEWS 2000 (pay) Official statisticReport
2003327,180$55,690BLS OEWS May 2003 Official statisticReport
2004307,610$59,300BLS OEWS May 2004 Official statisticReport
2005294,010$61,970BLS OEWS May 2005 Official statisticReport
2006285,690$65,510BLS OEWS May 2006 Official statisticReport
2007280,770$67,020BLS OEWS May 2007 Official statisticReport
2008275,390$68,100BLS OEWS May 2008 Official statisticReport
2009261,200$67,470BLS OEWS May 2009 Official statisticReport
2010244,320$68,880BLS OEWS May 2010 Official statisticReport
2011235,630$70,520BLS OEWS May 2011 Official statisticReport
2012243,420$70,060BLS OEWS May 2012 Official statisticReport
2013245,780$70,560BLS OEWS May 2013 Official statisticReport
2014248,770$71,600BLS OEWS May 2014 Official statisticReport
2015247,850$72,300BLS OEWS May 2015 Official statisticReport
2016252,670$73,150BLS OEWS May 2016 Official statisticReport
2017251,830$71,650BLS OEWS May 2017 Official statisticReport
2018247,570$73,390BLS OEWS May 2018 Official statisticReport
2019249,090$74,760BLS OEWS May 2019 Official statisticReport
2020240,290$78,560BLS OEWS May 2020 Official statisticReport
2021243,920$79,680BLS OEWS May 2021 Official statisticReport
2022237,520$82,850BLS OEWS May 2022 Official statisticReport
2023227,150$84,570BLS OEWS May 2023 Official statisticReport
2024219,010$84,130BLS OEWS May 2024 Official statisticReport
2025214,390$87,520BLS OEWS May 2025 Official statisticReport
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