Research & Data

The Sealed-Data AI ROI for Clinical Research Coordinators

Jul 20, 2026

Every figure on this page about clinical research coordinators traces back to a sealed snapshot of public data; none of it is modeled guesswork. The result is an AI-automation ROI you can hand to a skeptical CFO and have them check the math themselves.

Headline: a clinical research coordinator carries about 167 AI-addressable hours a year. At a loaded rate of $108.44/hour that is $18,109 of gross value; after a stated $12,000/year tooling budget, the Year-1 net is $6,109 per full-time employee.

Those numbers are a planning estimate built from defaults, not a quote. The three inputs — task hours, wage, and AI-addressable share — come from sealed public datasets; the three assumptions — a 2,080-hour work year, a 1.3× labor-loading multiplier, and the tooling budget — are stated in the open and adjustable in the calculator at the foot of this page. Change them and every figure recomputes.

Most of the upside here traces to one task — Code, evaluate, or interpret collected study data. The Economic Index puts its AI-addressable share at 50.6%, so it contributes 31 hours and $3,383 on its own; the table further down shows where the rest comes from.

Who this is for

R&D directors, lab and quality managers, and engineering operations leaders at laboratories, contract research organizations, device and electronics makers, and precision manufacturers — and anyone building the case for an AI assistant aimed at clinical research coordinators. If you need a number you can defend in a budget meeting, with a citation behind every cell, this is built for you.

How much of a clinical research coordinator's work is AI-addressable?

The occupation-wide signal first: 6.1% of clinical research coordinator task interactions in real Claude.ai usage already fall into an automation-or-augmentation pattern, per the Anthropic Economic Index. That is adoption observed in the wild, not a theoretical automatable share.

At the task level the picture is sharper. O*NET lists 33 distinct work tasks for this role. Of those, 5 have their own task-specific usage measurement in the Anthropic Economic Index; the remainder fall back to the occupation-level exposure above, and every row in the table below is labelled with which source it used (aei_task for a task's own data, aei_occ for the occupation fallback). We never silently mix the two.

For scale: BLS counts 100,870 people employed under "Natural Sciences Managers" — the broader BLS category this role is reported within — at a mean wage of $173,500 a year. That wage is the spine of the dollar figures here, and it is the aggregate category's mean, not a clinical research coordinator-specific figure. See the caveat below before treating it as your own cost.

Task by task: where the hours sit

Each row is one ONET task. Importance and Relevance are sealed ONET ratings; modeled hours allocates a 2,080-hour year across tasks in proportion to Importance×Relevance; AI-addressable share is the Anthropic Economic Index usage figure; hours saved and gross value follow from them. The table shows the 14 highest-value addressable tasks.

O*NET task	Importance (1–5)	Relevance	Modeled hrs/yr	AI-addressable share	Source	Hrs saved/yr	Gross value/yr
Code, evaluate, or interpret collected study data.	4.07	72.2%	62	50.6%	`aei_task`	31	$3,383
Collaborate with investigators to prepare presentations or reports of clinical…	4.11	76.3%	66	40%	`aei_task`	26	$2,863
Oversee subject enrollment to ensure that informed consent is properly obtained…	4.32	95.6%	87	6.1%	`aei_occ`	5	$575
Monitor study activities to ensure compliance with protocols and with all…	4.34	93.2%	85	6.1%	`aei_occ`	5	$564
Identify protocol problems, inform investigators of problems, or assist in…	4.25	95%	85	6.1%	`aei_occ`	5	$564
Perform specific protocol procedures such as interviewing subjects, taking vital…	4.42	89.7%	83	6.1%	`aei_occ`	5	$553
Assess eligibility of potential subjects through methods such as screening…	4.41	90%	83	6.1%	`aei_occ`	5	$553
Record adverse event and side effect data and confer with investigators…	4.35	89.6%	82	6.1%	`aei_occ`	5	$542
Schedule subjects for appointments, procedures, or inpatient stays as required…	4.49	84.7%	80	6.1%	`aei_occ`	5	$531
Review proposed study protocols to evaluate factors such as sample collection…	4.22	89.2%	79	6.1%	`aei_occ`	5	$531
Maintain required records of study activity including case report forms, drug…	4.27	86.6%	78	6.1%	`aei_occ`	5	$521
Prepare study-related documentation, such as protocol worksheets, procedural…	4.41	82.6%	77	6.1%	`aei_occ`	5	$510
Track enrollment status of subjects and document dropout information such as…	4.08	83.8%	72	6.1%	`aei_occ`	4	$477
Instruct research staff in scientific and procedural aspects of studies…	4.01	83.9%	71	6.1%	`aei_occ`	4	$466

Reading one row: the top task above is modeled at 62 hours/year; the Economic Index puts its AI-addressable share at 50.6%, so 31 hours are addressable, worth $3,383 at the loaded rate. Nothing is rounded up: hours saved is hours × share, full stop.

Every step of the dollar math

No black box. Here is every step:

Loaded hourly cost = (mean annual wage $173,500 ÷ 2,080 hours) × 1.3 loading = $108.44/hour. The 1.3× covers benefits, payroll tax, and overhead on top of base pay.
Addressable hours saved = the sum of (task hours × AI-addressable share) across the role's addressable tasks = 167 hours/year.
Gross annual value = 167 hours × $108.44 = $18,109/year.
Net Year-1 ROI = $18,109 gross − $12,000 stated tooling budget = $6,109 per FTE.

The break-even point is worth stating plainly: this role's AI-addressable work is worth $18,109 a year at the loaded rate, so any tooling spend below $18,109 per FTE is net-positive on hours alone — before any quality, speed, or capacity upside.

Where workflow automation fits for clinical research coordinators

The clearest automation opportunity sits in the paperwork and data layer around a study, not in subject-facing judgment. A workflow can handle intake of source documents, drafting of study-related paperwork such as protocol worksheets, reconciliation of case report forms against source records, routing of adverse-event data to the investigator, and keeping enrollment and compliance trackers current as a study proceeds. US Tech Automations builds agentic workflows for exactly this orchestration layer: connected intake, drafting, reconciliation, routing, exception queues, and system-of-record updates, with explicit approval points built in rather than assumed.

Human approval has to remain mandatory wherever the work touches informed consent, subject eligibility, or an adverse-event judgment — those calls carry accountability to the subject and to the investigator that a drafting assistant cannot hold. Before automating, a site should measure how long intake, drafting, and routing actually take today so there is a real baseline to compare against. When the automation is not confident — a mismatched field, a missing source document — it should stop and route the item to a queue the coordinator or investigator reviews rather than guess. The automation does not take authority over consent, eligibility screening, protocol interpretation, or any clinical judgment about a subject; those decisions stay with the coordinator and the investigator.

The honest limitations

The single most important caveat: the Anthropic Economic Index measures observed Claude.ai usage patterns, not a theoretical "this much of the job can be automated." A high share means practitioners are already routing that task to AI; a low share can mean the task is hard to automate or simply that few people have tried. Treat these as a grounded default, then replace them with your own automatable share in the calculator — that is exactly what it is for.

The hour-allocation heuristic. O*NET does not publish hours per task, so we allocate the work year in proportion to each task's Importance×Relevance. It is a transparent, defensible split, not a stopwatch study; if you know your team spends disproportionate time on one task, the calculator lets you see the table and reason about it.

Why Importance×Relevance? O*NET rates each task on how important it is to the role and how relevant it is to a typical worker (the share who actually perform it). Multiplying the two ranks tasks by real time-pull — a high-importance task nearly everyone does outranks a niche one — which is precisely the weighting you want when dividing a fixed work-year. It is the most defensible allocation available short of a per-employer time study, and any row you disagree with is editable in the calculator below.

The wage is a national mean. BLS OEWS reports a $173,500 mean across all employers nationally (median $161,180). Your local, loaded cost may differ; set your own wage to localize the dollars.

Read this before you trust the dollar figures: the wage is an aggregate, and it is probably too high. BLS OEWS publishes wages at the 6-digit SOC level, and Clinical Research Coordinators (O*NET 11-9121.01) has no wage line of its own. The $173,500 above is the mean for 11-9121 "Natural Sciences Managers", the broader category BLS reports this role inside. That category spans senior and adjacent occupations, so its mean typically runs above what a clinical research coordinator is actually paid. Because every dollar figure on this page is the loaded hourly rate times hours saved, an inflated wage inflates the savings by the same proportion. Replace it with your own fully-loaded rate in the calculator before quoting these numbers to anyone. The hour estimates are unaffected; only the dollars move.

The tooling budget is an assumption, not a quote. The stated annual tooling figure in the model is a placeholder for licensing, integration, change management, and ongoing human review — not a US Tech Automations price quote. A buyer should replace it with real vendor numbers before treating the net figure as final.

What this is. A sourced, reproducible first estimate to start a buying conversation — not a guarantee of savings. The value of the method is that every input is sealed and checkable, so a skeptic can audit it rather than argue with a vendor's slide.

Where these numbers come from

O*NET 30_3 — task statements and Importance/Relevance ratings. This page includes information from O*NET 30.3 Database by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). Used under the CC BY 4.0 license. License: CC BY 4.0. Sealed snapshot 251d3df7766aa152, evidence 9e12c3890449ec21. Occupation page: O*NET OnLine: Clinical Research Coordinators.
BLS OEWS May 2024 — occupational mean wage and employment. Source: U.S. Bureau of Labor Statistics, Occupational Employment and Wage Statistics (OEWS), May 2024. License: Public Domain (17 U.S.C. §105 — U.S. Government work). Sealed snapshot d032d178d7a95cdc, evidence 1237fd6700a000e9.
Anthropic Economic Index — observed AI task/occupation exposure (Claude.ai usage). Source: Anthropic Economic Index (https://huggingface.co/datasets/Anthropic/EconomicIndex), released under CC-BY. Reflects observed Claude.ai usage patterns, not a measure of theoretical automatability. Pinned to commit db51ecb12920, sealed snapshot c6870bb780772e4f, evidence 66b4254a97b1e852.

Every numeral on this page is reproducible from those three sealed snapshots by re-running our open model — there is no hand-entered or estimated figure in the tables or the math.

FAQ

Is "6.1% AI exposure" the share of the job AI will replace?
No. It is the share of measured Claude.ai task interactions for this occupation that showed an automation or augmentation pattern — an observed-usage signal, not a replacement forecast.

Where does the $173,500 wage come from?
BLS Occupational Employment and Wage Statistics, May 2024, used verbatim from the sealed snapshot — but it is the mean for the aggregate category 11-9121 "Natural Sciences Managers", not for clinical research coordinators specifically. BLS does not publish a separate wage for O*NET 11-9121.01. That aggregate typically pays more than this role does, so treat the dollar figures as an upper bound and substitute your own loaded rate.

How do you get 167 hours saved?
For each addressable task we multiply its modeled annual hours by its AI-addressable share, then sum. Modeled hours allocate a 2,080-hour year by each task's O*NET Importance×Relevance.

Can I change the assumptions?
Yes — the calculator below this article lets you set the wage, the work-year hours, the labor-loading multiplier, the tooling budget, and each task's automatable share. The net ROI updates live.

Why these three data sources?
O*NET gives the tasks, BLS gives the labor cost, and the Anthropic Economic Index grounds "how much is AI-addressable" in real usage rather than a guess. Each is public and pinned to a sealed snapshot.

What this looks like in production

The math above is the business case; the next step is a workflow you can watch run. Pick one document-heavy piece of clinical research coordinator work, establish a measured baseline, and identify the system of record it needs to update. US Tech Automations can then map the intake, drafting, reconciliation, routing, and exception-handling steps against the addressable hours above, so recovered time converts into capacity you keep instead of headcount you chase.

See how AI agents handle clinical research coordinators → — or bring this page's numbers to a scoping call and we will pressure-test them against your actual task mix.

More automation-ROI breakdowns

Same sealed O*NET + BLS + Anthropic Economic Index method, other roles:

Make the model yours

The interactive calculator below loads this role's sealed task table. Adjust the wage, hours, loading, tooling budget, or any task's automatable share, and watch the net Year-1 ROI move. The defaults are the sourced figures above; the controls are yours.

Loading the interactive ROI calculator…