How to Use AI for Recruiting (When You're Not an Expert)

You don’t need to be technical to use AI

AI in recruitment sounds complicated. It conjures images of data scientists, machine learning pipelines, and months-long implementation projects. But the reality in 2026 is very different.

Most AI in recruitment requires zero technical knowledge from the person using it. If you can pick up a phone and work an ATS, you can work with AI. It’s not about the technology. It’s about knowing where to use it and where not to.

What has changed is the shape of the decision. A couple of years ago the question was which AI tool to bolt onto the system you already ran, and the whole conversation was about integration depth. That question has aged badly. The admin that actually costs you time happens inside the system of record: the notes, the fields, the CV, the shortlist. An AI that lives outside it spends most of its effort getting back in, and you pay for that plumbing forever. So the useful question in 2026 is simpler and bigger: does the intelligence sit in the system where the work already happens, or next to it?

This article is your practical guide either way. No hype, no jargon. Just concrete steps you can take tomorrow. From first exploration to a fully working team.

Step 1: Understand what AI can and can’t do

Before you start, you need to know what to expect. Because the disappointment many recruiters feel doesn’t come from AI being bad. It comes from wrong expectations.

AI is good at:

• Recording, transcribing, and summarizing conversations. Faster and more detailed than you could do yourself. And consistent, even after your twelfth conversation of the day.
• Reading a conversation or a document and turning it into structured values on a record. Not loose text: the right dropdown, the right date field, the right number.
• Reading CVs, structuring them, and converting them to another format. Including grammar correction and house style application.
• Scoring a candidate against a vacancy, and saying which requirements are met and which are not.
• Recognizing patterns across large amounts of data. Trends you’d miss yourself, simply because you can’t analyze a thousand conversations at once.

AI is not good at:

• Assessing chemistry between a candidate and a team. Feeling a click is human work.
• Evaluating culture fit based on a conversation. AI doesn’t read body language or subtle social signals.
• Deciding whether someone is the right candidate. That remains your expertise and responsibility.
• Picking up nuances beyond the spoken word. The doubt in someone’s eyes, the hesitation before an answer.

That boundary matters. AI isn’t a threat to recruiters. It’s an assistant that takes over the boring work so you can focus on the work that truly matters.

Step 2: Start with your biggest pain point

The mistake many teams make: they try everything at once. Recording, data entry, CV parsing, matching, reporting. That’s overwhelming and leads to half-hearted adoption. Three weeks later, nobody’s using anything.

Start with the one thing that costs you the most time. For most recruiters, that’s one of these two:

Option A: Conversation processing

If you conduct multiple conversations daily and spend 15-20 minutes on a summary after each one, start here. AI summaries give you a complete summary within minutes of the call. Adapted to the conversation type. An intake produces a candidate profile, a client meeting produces a vacancy briefing.

Results are immediately noticeable. You save time from day one. There’s no learning curve. You conduct your conversation as you always do, and the summary appears. You don’t even have to think about it.

Option B: Getting the data onto the record

If your biggest frustration is filling fields after conversations, start there. Automatic data extraction pulls the relevant data points out of the conversation and offers them for the fields they belong in. Salary expectation in the salary field. Availability date in the date field. Not as loose text, but in the right format.

It sounds simple. But why AI only works when it fills data automatically is a story of its own. Many tools give you a summary and leave it at that. That only solves half the problem. You still have a summary you need to read and retype into fields.

Whichever of the two you start with, notice that both end in the same place: a record. That is the reason the two used to be sold as separate products with a connector between them, and the reason they no longer need to be.

Step 3: Choose the right tool

There are dozens of AI products aimed at recruitment. The old advice was to check the integration list first. That advice belonged to a market where the AI and the system of record were always two different companies. Now that they are often one, these five things separate the options that will still be working in a year from the ones that won’t:

1. The data model: can it hold your desk?

Every recruitment business has objects the generic template never had. A margin per placement. A certificate with an expiry date. A client contact who is also a candidate. If the system can’t hold those as real fields, they end up in a notes field nobody can filter, and no amount of AI fixes that. Ask whether you can add an object, a field and a relation yourself, and whether doing so takes a configuration change or a release. In Simply the data model is metadata rather than code, so a new field exists everywhere the object is used the moment you create it.

2. Matching you can check

Any product can put a number next to a candidate. The question is whether it can show you the arithmetic. Under the EU AI Act, recruitment AI that ranks or supports decisions sits in high-risk territory, and “the model said so” is not an answer you can give a candidate or a regulator. Match Intelligence publishes the sum: the weight of the requirements a candidate satisfies divided by the weight of everything the vacancy asked for, with missing must-haves named separately rather than averaged away. Anything you can’t recalculate by hand, treat as a black box.

3. Recording via every channel

You don’t only call via Teams or Meet. You also call from your mobile in the car, and you meet people in rooms. If the product only supports video conferences, you’re missing half your conversations. Possibly the most important half. Omnichannel recording isn’t a luxury, it’s a requirement — and ask specifically whether a bot has to dial into the meeting as a guest, because that is a conversation with your candidate about a robot rather than about the job.

4. Permissions, transparency and security

You’re processing sensitive candidate data. Everything someone shares in a job interview, from salary expectations to personal circumstances. Ask who can see what, and whether that is decided per request or frozen into a saved report. Ask whether the AI answers respect the same rules as the screens. Ask whether every AI sentence is traceable back to the moment in the conversation it came from — transparency is not optional, because what you can’t verify you can’t send to a client. And ask the security questions plainly: ISO 27001? GDPR? Where is the platform hosted, and separately, where does model inference run? Enterprise security is non-negotiable.

5. The migration path, in both directions

The question everybody asks late. How does your data get in, how long does it take, and what happens to it if you leave. Ask for the shape of the move rather than a reassurance: what comes out of your current export, what does not, and who reviews the mapping. Simply’s is a four-week migration, and it is one-way — there is no ongoing sync back to your old system, on purpose, because two systems of record writing to each other gives you two systems and no truth.

Considering building something yourself instead? First read why custom AI is almost always a costly mistake. The complexity is structurally underestimated, the costs run into millions, and the timeline is 18 months and up.

Step 4: Implement in phases

Okay, you’ve chosen. Now implementation. Don’t do this as a big bang. That leads to resistance, confusion, and half-hearted adoption. Do it in three phases. If you are moving off another system, those phases sit on top of the migration rather than instead of it — the data move is its own project with its own four weeks.

Phase 1: Pilot with 2-3 recruiters (week 1-2)

Select 2-3 recruiters who are open to change. Preferably people who were already complaining about the admin burden. Let them work in it for two weeks. Just the basics: their records, recording and summarizing. Collect feedback. What works? What doesn’t? Which summary formats fit your workflow? What’s missing?

That feedback is incredibly helpful. It tells you how to shape the fields and the templates before the whole team arrives. And it gives you ambassadors: colleagues who can speak from experience that it works.

Phase 2: Turn on the fields (week 3-4)

Once the basics work, switch on extraction into the fields you actually use. Start with 5-8 fields. Not all 30 at once. The most common: availability, salary expectation, travel willingness, hours per week, experience level. Expand once it runs stably and the team has built confidence.

Read the first few days of proposals carefully. Every AI write arrives as a proposal that names the field, the value and its source, and rejecting one costs a click. That is what builds trust in the thing — not a promise that it is accurate, but seeing it be accurate and knowing you were the one who pressed save.

Phase 3: Roll out to the full team (week 5-8)

Once the pilot group is successful and the fields are filling reliably, roll out to the entire team. Use the pilot users as internal ambassadors. They can convince colleagues better than any presentation or management email.

Now also add CV parsing and CV formatting, turn on matching, and build your first dashboards. Be clear with the team about what that reporting is and is not: it returns counts, rates and trends over your own objects, and it does not let anyone click a chart to get the list of people behind it. It’s a management view of the pipeline, not a window onto individual colleagues.

Step 5: Measure the results

After 4-6 weeks, you can measure. And measuring is important because it justifies the investment and shows where there’s still room for improvement. It also convinces the skeptics on your team.

What you can measure:

• Time saved per conversation. Compare the time recruiters spent on admin before and after. Ask them, or measure it. Teams commonly report 15-25 minutes saved per conversation, but measure your own — a number from someone else’s workflow is not a number from yours.
• Data quality. Are more fields filled? Are they more accurate? Fewer half-empty profiles? Count the filled fields before and after; this is the one that compounds, because every later feature reads those fields.
• Time-to-submit. How quickly do you send a candidate to a client after the first conversation? This is a direct indicator of your competitive position.
• Recruiter satisfaction. Simply ask: do you enjoy your work more now that admin is reduced? Is it helping or nagging?

The first three of those are dashboard questions, and they are exactly the shape reporting handles well: an aggregate over time, broken down by a dimension you care about. The fourth you get by asking people.

Common mistakes in AI adoption

After hundreds of implementations, we know where teams stumble. Avoid these mistakes:

• Trying to do everything at once. Start with one use case, not five. Prove the value, then expand.
• Buying on the length of the integration list. A long connector gallery mostly measures how many partnerships a vendor announced, not how little copying your recruiters will do. Ask instead where the record lives when the work is done.
• Not collecting feedback. Without feedback from the pilot group, you don’t know what to adjust. Ask actively.
• Skipping management buy-in. If management doesn’t support it, adoption is an uphill battle. Get buy-in with concrete numbers.
• Ignoring security. Never choose a product that uses your data for training or isn’t GDPR compliant. One data breach and trust is gone.
• Expecting AI to be flawless. AI makes mistakes, just like people. The difference that matters is whether a shaky answer is written silently or handed to a human first.

How Simply makes this easy

Simply is an AI-native ATS: the records, the recording, the extraction, the matching and the reporting are one system rather than a system plus a layer. That is why the steps above are shorter than they used to be — there is no connector to configure between the AI and the place the work lands.

Concretely:

• Recording works across the channels recruitment actually uses. Online meetings are captured natively through Google and Microsoft with no bot joining as a guest; phone calls run through the desktop app and in-person conversations through the mobile app. All of it lands on the candidate.
• Summary formats are pre-configured for the most common conversation types in recruitment. Intake, client meeting, sales call. You can always customize, per client and per team.
• Extraction writes into the fields you defined, because the data model is yours to shape. No mapping project between somebody else’s field names and your own.
• Every AI write is a proposal with its source attached, so nothing changes on a record without a human pressing save.
• Six connectors — Gmail, Outlook, Google Calendar, Zoom, Slack and HubSpot — and we will name all six rather than showing a logo wall. Simply does not sync with another ATS; coming from one is a one-way migration in four weeks, with a mapping you review.
• ISO 27001 certified, GDPR compliant, with the platform and your candidate data hosted in the Netherlands. The AI processing stays inside the EU as well, or runs on your own key with your own provider and region.
• Core is EUR 79 and Professional EUR 99 per user per month billed yearly. No credits, and no markup on AI usage — you connect your own key and your provider bills you directly. Fourteen days of the full product to try, no card.

Want to know what that means for the ATS you run today? Read how to integrate AI into your existing ATS — including the honest answer about when bolting it on is still the right call.