Which AI software fits a staffing agency? A practical guide to the volume economics: which AI layers deliver a return at volume, and which are just overhead.
This guide goes deeper than the staffing section in our broader guide per agency type. That one has the overview. This one has the build-out, layer by layer.
Staffing isn’t a small corner of the market. It is the market. In the second quarter of 2025, the flexible-work segment made up roughly 37.9% of all workers in the Netherlands, and temporary and seconded workers together accounted for around 3.5% of all employed people (CBS labour market dashboard). Industry body ABU, with more than 500 members, represents around 65% of the market (ABU market figures). It’s large, it’s competitive, and in 2025 it ran on a declining hours economy. That last point suddenly makes the cost side a lot more sensitive.
Here’s the point. A staffing agency doesn’t earn tens of thousands of euros in fees per placement the way an executive search firm does. It earns a few euros of margin per hour worked, times a great many hours. The profit sits in volume and in speed. A vacancy that comes in on Monday and is still open on Wednesday is a vacancy heading to the competitor down the road. Speed-to-fill isn’t one of the KPIs. It is the KPI.
And that changes everything about how you look at AI. For an agency type that earns on depth, a tool may well spend five extra minutes on a richer candidate profile. For staffing the math runs exactly the other way. Every minute of admin a recruiter spends per candidate, you multiply by hundreds of candidates a week. Three extra minutes per intake write-up feels like nothing. Across 200 conversations that’s ten hours a week. Per recruiter.
So the question for a staffing agency isn’t “which AI is the smartest”. The question is: which AI removes the most repeated minutes, across the largest numbers, without you paying for depth you don’t use?
I split the AI stack for staffing into four layers that deliver a return, and one layer to be careful with. Per layer: what it does, and why it works at volume.
The biggest hidden time sink in staffing isn’t the conversation itself. It’s what comes after. A recruiter runs a twenty-minute intake, hangs up, and then spends another fifteen minutes writing notes into the system. Those fifteen minutes are pure overhead. The candidate has already been spoken to, no new information is added, you’re just retyping what you already know.
A notetaker or conversation AI records the call (online via Meet or Teams, in person via a mobile app, by phone via VOIP) and delivers a structured summary straight away. No more write-up. After the call the recruiter reads through a finished summary instead of typing from scratch.
For staffing this is the first layer you switch on, because it’s the purest form of repeated time saving. Every conversation, every day, the same minutes saved. Watch one thing though: pick a tool that produces a fitting summary per conversation type. A candidate intake calls for different fields than an evaluation interview. Generic meeting notes (“here are the action items”) help a recruiter less than a summary built for a staffing intake.
This is the layer people underestimate. A staffing agency forwards CVs to the end client, often in the agency’s house style, sometimes in the client’s. That reformatting is done by hand right now. Logo on it, tidy up the layout, fix the typos, sections in the right order. Five to ten minutes per CV if you do it properly.
Five minutes per CV sounds harmless. But a staffing agency processing hundreds of CVs a week burns dozens of hours a week on formatting work that earns no extra euro. The end client doesn’t pay for pretty margins in a Word document. Auto-formatting that turns a raw CV into the fixed house style, including language correction, is exactly the kind of work AI does cheaply and consistently, and people do expensively and with variation.
Do the math for yourself. It’s almost always the layer with the fastest, hardest payback in staffing.
Recruiters hate data entry. Rightly so. It’s retyping information that already exists somewhere into fields in a system. Desired salary, current salary, availability, location, driving licence, available hours. All mentioned in the conversation, all entered by hand.
Smart data-entry automation recognises those data points in the conversation or the CV and puts them in the right fields of your system. The difference with simply “pasting text” sits in the formatting. A good system understands a field is a dropdown, or an enum, and picks the right value instead of dumping free text. And it presents the result as something to approve rather than something already written: a confidence score per value tells the recruiter which two or three are worth a second look, and the rest go through on a glance. You are trading typing for reading, which is the cheaper action by a wide margin.
For staffing this counts double, because your CRM or ATS is the engine under the whole operation. The faster a candidate is complete and correct in the system, the faster they’re matchable for the next vacancy that comes in. It is also the layer where the question of where the AI sits starts to matter, which is the section after next.
Staffing runs largely over the phone. The first screening is often a five-minute call: are you available, what are you looking for, is this profile still right? Those phone calls normally fall outside every notetaker, because most tools only record video meetings.
A tool that captures phone conversations changes that, whether it does it through a VOIP integration or by recording the call from the recruiter’s own machine. Either way the fast phone screening gets pulled into the stack, and there is no separate channel left that stays undocumented. For a staffing agency making dozens of calls a day that’s a layer most tools skip, because international vendors either leave telephony out or price it as an add-on.
Here comes the warning. Right now the market is selling hard on “agentic recruiting”: AI that assesses, ranks and rejects candidates by itself. At volume that sounds tempting. Hundreds of applicants, automatically sifted, done.
Don’t do this without a human in the loop on the rejection. Under GDPR Article 22 every candidate has the right not to be subject to a decision based solely on automated processing that produces legal effects or similarly significant effects. An automated rejection falls squarely under that. At hundreds of rejections a week that’s no longer a theoretical risk. It’s a liability that stacks up with every decision.
And it gets stricter. Recruitment AI is classified as high-risk under the EU AI Act from 2 August 2026, with hard requirements around human oversight, logging and explainability. We wrote two separate pieces about this you should read before you sign: the practical side of GDPR and the AI Act for recruitment tools, and the deeper analysis of agentic recruitment under the EU AI Act.
This doesn’t mean agents are worthless for staffing. An agent that pre-sorts, suggests, or assembles a longlist that a human then reviews is fine. The line is at the rejection. There a human must decide, and you must be able to demonstrate a human decided. At staffing scale that flow has to be watertight, not “we usually take a look”.
Just as important as what you do buy: what you leave on the table.
The biggest pitfall is buying tools designed for deep per-candidate research. Market mapping, extensive candidate dossiers, leadership profiles, sentiment analysis across multiple conversations. Those are brilliant features. For an executive search firm doing one placement a month at a fee of tens of thousands of euros. For a staffing agency placing a candidate on a margin of a few euros an hour, it’s overhead no client pays for.
The same goes for relationship tracking with deep memory over years. That fits secondment, where the relationship with a professional runs for years and the value sits in repetition. In staffing the relationship is shorter and more transactional by definition. Investing in a deep insights layer for a candidate you may not speak to again in three months is capital in the wrong layer.
The rule is simple. In staffing you match the tool to the volume economics, not to what looks most impressive in the demo. Speed and throughput over depth. Every euro of AI budget belongs to the layers that remove repeated minutes across large numbers.
Two answers get sold hard here, and both are sold as obvious. Bolt AI onto the ATS you already run. Or replace the ATS with something that has the AI inside it. The honest version depends on one thing: where your admin minutes physically sit.
Most staffing agencies run on a system of record that has driven the operation for years. Mysolution, Bullhorn, Carerix, OTYS. That system tracks your candidates, placements, contracts and invoicing, and none of that is free to walk away from.
But look at where the minutes from those four layers go. They go into that system. The write-up gets typed into it. The extracted salary expectation has to land in one of its fields, in its format, matching its dropdown options. The candidate only becomes matchable once they are complete in it. Which means a layer sitting on top has to get through the wall on every single field: read the schema, map onto somebody else’s names and types, keep up when a colleague adds a field on a Tuesday afternoon, and pass a certification process to be in the vendor’s official integration list at all. That is an integration tax, and it is charged per field rather than once.
There are two ways to pay less of it, and they are genuinely different bets.
Accept the tax and buy well. Pick a layer whose connector to your specific ATS is actually built rather than a Zapier workaround, and ask which fields it cannot write, not just which systems it supports. This is the right answer when your back office does contracting, collective-agreement calculations and invoicing you are not going to move this year. Ask for a reference from an agency running on the same ATS, at your volume.
Or remove the gap. Run a system where the AI and the record are the same thing, so extraction targets its own fields and there is no mapping layer to maintain because there is nothing on the other side of it. The cost of that route is real and worth stating plainly: replacing a system of record is a project, not a purchase. Reckon on about four weeks, half a day a week from someone who knows why that free-text field was created in 2019, and a week of parallel running. And it is one-way. There is no sync back to the old system afterwards, which is exactly the point and also exactly the risk.
So the buying question isn’t “does this replace my ATS” and it isn’t “does this integrate with my ATS” either. It is: over the next three years, would I rather pay the integration tax on every field, or pay the migration once?
Since I have just laid out that choice, it is only fair to say which side of it we are on. Simply is the second route: an AI-native ATS, so the system of record itself, with recording, extraction and matching sitting in the same database as the candidate. That means we are not a co-pilot you add to Mysolution or Bullhorn — we are in the comparison set with them, and you should read what follows as a vendor making its case rather than as neutral advice.
Concretely for a staffing agency:
Where it does not fit, stated here rather than in month two: multiposting to job boards and vacancy distribution are coming, but they are not in the product today, so if vacancy distribution is the centre of your process you need a second system alongside it today. A branded careers site is not something Simply delivers. And if your back office runs contracting and invoicing you genuinely cannot move this year, a well-chosen layer on top of what you already have is the more honest answer. More on the economics per desk is on the page for staffing and secondment firms, and the comparison pages set us against the systems you are probably already running.
Not with a tool list. With your own numbers.
Take a week and count three things. How many minutes does a recruiter spend on average on intake write-up after a conversation? How many CVs go to end clients per week, and how many minutes does the reformatting take? How much time disappears into manual data entry into the CRM? Those three numbers, multiplied by your number of recruiters, are your business case. Not an assumption, measured.
Only then the tool. Build a shortlist that explicitly serves staffing, and put both routes on it: the layers that connect to the ATS you run, and the systems that would replace it. Comparing them on one sheet is the only way the integration tax shows up as a number instead of as a feeling. Request the compliance documentation before the demo, not after. And don’t sign an annual contract without a pilot. Three to six weeks on one team, with the measured numbers from week 1 as a baseline. Not “does it feel good”, but “does it save the ten hours a week per recruiter we calculated up front”.
In staffing, with that thin margin and those large numbers, the right AI pays for itself in weeks. The wrong AI is overhead with a nice dashboard. The difference isn’t in the cleverness of the tool. It’s in whether it fits how you earn your money.
About the author
Remo Vloet is a co-founder of Simply, the AI Operating System for recruitment agencies: inbox, meetings, sourcing, CV parsing, search and matching, documents and automation in one system. With a background in building complex software, he contributes to the technical vision behind Simply.
LinkedInArticles by Remo VloetStart with conversation AI for intakes and CV formatting into house style. Those are the two layers with the purest, most repeated time saving at volume. The write-up after a conversation and the reformatting of CVs are pure overhead that earn no extra euro, and you multiply them across hundreds of candidates a week. Only once those two layers are proven do you expand into data-entry automation and VOIP screening. Don't start with a platform promise; start with one measured problem.
That depends on where your admin minutes sit. They almost always sit inside the system of record itself: the write-up is typed into it, the extracted salary lands in one of its fields, the candidate becomes matchable in it. A layer on top therefore has to map its output onto somebody else's field names and types, and keep up every time you add a field. That is an integration tax charged per field, not once. The alternative is a system where extraction targets its own fields and there is nothing to map. Both are defensible; they just have different bills. Replacing a system of record is a real project of about four weeks and it is one-way, so ask both kinds of vendor for a reference from an agency your size.
Not without human review. Under GDPR Article 22 every candidate has the right not to be subject to a decision based solely on automated processing that produces legal effects. An automated rejection falls squarely under that. At hundreds of rejections a week that's a real, stacking risk. From 2 August 2026 recruitment AI is also classified as high-risk under the EU AI Act, with hard requirements around human oversight and logging. An agent that pre-sorts or assembles a longlist is fine; the rejection decision must sit with a human, and you must be able to demonstrate that.
Because you're not paid for that depth. Market mapping, leadership profiles and extensive candidate dossiers are valuable for an executive search firm doing one placement a month at a high fee. A staffing agency places on a margin of a few euros an hour and lives on speed and volume. Investing in a deep insights layer for a candidate you may not speak to again in three months is capital in the wrong layer. The rule: match the tool to your volume economics, not to what looks most impressive in the demo.
More important than for most other agency types. Staffing runs largely on fast phone screening, and most notetakers only record video meetings. Those calls then stay undocumented, outside your stack. Native VOIP support that records and summarises outbound and inbound calls (including Dutch 06 mobile numbers) pulls that channel into the stack. For an agency making dozens of calls a day that's a layer most international tools skip or offer only through expensive add-ons.
Measure before and after, in real minutes. Three measurement points work best: time per intake write-up (between the end of a conversation and complete processing in the CRM), minutes per CV reformatting into the end client's house style, and time per candidate on manual data entry. Multiply by your number of recruiters and you have your business case as a baseline. Then run a pilot of three to six weeks on one team and compare against that baseline. Avoid "gut feel" measurements. Many time-saving claims don't survive a real before-and-after measurement, and at the thin staffing margin you want to be sure.
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