EXPOSED
The bulk of this job — interview synthesis, process mapping, benchmarking, cost models in Excel, and the 60-slide findings deck — is exactly the text-and-spreadsheet work generative AI now produces at draft quality in minutes. What survives is the part that requires being in the room: extracting what executives won't say in a survey, brokering agreement between departments that don't want to change, and owning a recommendation when it costs someone their headcount. There is no license, no signature requirement, and no personal liability, so the only real moats are client trust and accountability for the call.
Headcount grew steadily across the period.
Median pay $85,260 → $101,860 -4.4% 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
+8.8%
Percentage only. The projection counts a different population from the 898,280 above — it includes self-employed workers, which for this occupation is most of them, so the two headcounts are not comparable.
Exposed, but growing
AI can already do a lot of these tasks, and the BLS still expects +8.8% more of these jobs by 2034. Demand for the output is growing faster than the work is being automated away — the mechanism BLS gives for software developers, and the combination people most often misread as an error.
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.
~98,100 openings a year on average, including replacing people who leave.
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Engagement ManagerRevenue Operations ManagerSales Operations ManagerSolutions Consultant
Holding it up: judgment & accountability . Weakest point: liability shield .
Mixed — a routine tier and a judgment tier An 8 reflects that the deliverable chain — coding interview notes, building the as-is/to-be process map, running the benchmarking pull, the activity-based cost model, the org chart redesign scenarios — is now draftable by a model faster than you can open the workbook, while the workshop facilitation, the stakeholder interviews where the real constraint is disclosed off-record, and the steering-committee negotiation still require a person present, which is why it sits mid-band rather than at 4.
Fully desk- and screen-based A 4 is the ceiling for a job done from a laptop in a client conference room or on Zoom; the only physical element is walking a plant floor or warehouse during a time-and-motion study or Gemba walk, and even that is observation you could delegate to the client's own supervisors.
No licence, no signature requirement A 2 is right because no state licenses management consulting, CMC certification from IMC USA is voluntary and rarely a bid requirement, and when a restructuring recommendation destroys value the client's own officers and board carry the fiduciary exposure — your engagement letter caps your damages at fees.
Meaningful discretion A 12 fits work where you choose which cost driver to attack, which sites to close, and which function absorbs a 15% headcount reduction, and then defend it under executive cross-examination — but the decision is signed by the CEO or board, not you, and much of the engagement runs on the firm's standard diagnostic methodology rather than open-ended calls.
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 (8/20 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 (2/20) is whether the law requires a licensed human to sign. Trust premium (11/20) is whether buyers specifically pay for a person. Judgment and accountability (12/20) 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 25 of this occupation's 37 points (68%).
Embodiment (4/20) 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.
Chief Executives SAFE
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 51/100, still EXPOSED.
Genuine two-tier occupation: if the synthesis/benchmark/deck tier is automated, what remains is confidential executive elicitation, cross-department negotiation, and change management in the room — work that resists because the input is guarded and political, not because it's cognitively hard. Watch staffing pyramids flatten (analyst-to-partner ratio falling at major firms) as evidence the routine tier has gone.
Task-mix shift plus contract form: if AI absorbs the deck-and-model tier, the remaining engagement is restructuring and headcount decisions where the analyst is named in the board minutes as recommending party. Watch for outcome-based/at-risk fee structures (already used by Bain, McKinsey Implementation, Alvarez & Marsal turnaround mandates) becoming standard rather than exceptional — the firm owning P&L consequence rather than delivering advice.
Narrow, real route in specific sub-segments: expert-witness and litigation-support damages work already requires a testifying human under FRE 702 and Daubert, and federal court rules do not accept AI-generated expert opinion. If courts adopt explicit AI-disclosure/certification rules for expert reports (several federal districts have issued standing orders on AI in filings), damages and valuation consultants gain a signature requirement. Similarly, a PCAOB or SEC rule requiring a named individual to attest to internal-control remediation advice would extend this.
Confidentiality-driven premium: if a client's own AI-use restrictions (bank, defense contractor, or a firm under litigation hold) require that pre-decisional strategy work not touch a vendor model, the human engagement becomes the only permitted form. Watch for procurement clauses prohibiting contractor use of LLMs on privileged material, and for privilege-preservation arguments about AI-processed work product.
The limit. The liability lever only reaches a slice of the 898k — expert witness, valuation, restructuring — not the generalist internal-consulting majority, whose title has no license to attach a signature to. Trust premium is capped by the fact that buyers of management analysis are themselves AI adopters under cost pressure; they are the least likely population to pay for human-made slides. Realistic ceiling around 50, and the honest path there runs through the occupation shrinking to its judgment tier rather than the occupation getting protected.
| New York-Newark-Jersey City, NY-NJ | 67,630 | $124,390 +22% |
| Washington-Arlington-Alexandria, DC-VA-MD-WV | 62,360 | $126,830 +25% |
| Chicago-Naperville-Elgin, IL-IN | 37,510 | $105,090 +3% |
| Los Angeles-Long Beach-Anaheim, CA | 37,060 | $104,930 +3% |
| Atlanta-Sandy Springs-Roswell, GA | 26,870 | $101,220 -1% |
| Boston-Cambridge-Newton, MA-NH | 26,450 | $135,640 +33% |
| Sacramento-Roseville-Folsom, CA | 25,600 | $82,990 -19% |
| San Francisco-Oakland-Fremont, CA | 23,560 | $126,730 +24% |
| San Jose-Sunnyvale-Santa Clara, CA | 9,020 | $136,670 +34% |
| Boston-Cambridge-Newton, MA-NH | 26,450 | $135,640 +33% |
| Barnstable Town, MA | 140 | $132,140 +30% |
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 37. 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.