Should we pick one ATS or run both until the dust settles?

Pick one. Running both feels safe, but it doubles your data problem every single day. Every req, every candidate, every placement logged in the system you plan to kill is more cleanup later. Choose the winner inside the first 30 days and freeze new work in the loser. The pain of deciding early is smaller than the pain of migrating two years of duplicate records.

How clean does our data need to be before we turn on AI tools?

Cleaner than you think. AI does not fix bad data, it speeds up the damage. If half your candidate records have no skills tagged and your placement history lives in three formats, an AI matching tool will give you confident, wrong answers. Get to one system, one set of fields, and deduped records first. Then layer AI on top. The order matters.

Who should own the consolidation, the acquired team or the buyer?

One named person owns it, and they report to the deal sponsor. Not a committee. Not the two ops leaders splitting it. Committees stall and everyone assumes someone else has the migration. Give one person the authority, a 90-day deadline, and a weekly check-in with the PE operating partner. Ownership is the difference between a plan and a wish.

Tech Stack Consolidation After a Staffing Acquisition

Q1 2026 logged 35 staffing M&A deals. The day each one closes, someone inherits two ATS platforms and a pile of fragmented data. Here is the 90-day plan to fix it before it kills your AI roadmap.

The day a staffing deal closes, someone inherits two applicant tracking systems that do not talk to each other. That person usually does not have a plan for it.

StaffingHub counted 35 staffing M&A transactions in Q1 2026, the hottest opening quarter since 2022, with Griffin Financial Group projecting 85 to 100 deals for the full year. Every one of those deals creates the same mess behind the scenes: duplicate platforms, overlapping candidate databases, and placement data living in formats that will never match.

Buyers have caught on. Tech debt is now priced into the deal as a valuation risk. If your portfolio company is carrying three ATS platforms and a data pile nobody can trust, that shows up in the number. And here is the part most operators miss: that same messy data is the reason your AI investment will not pay back. AI runs on clean, consolidated data. You cannot skip this step and expect the smart tools to work.

So let's walk through the 90-day plan. Not a theory. A sequence you can run starting the week the deal closes.

Days 1 to 30: Decide and Freeze

The first month is about decisions, not migration. Most teams get this backwards and start moving records before they have picked a direction.

Pick the surviving ATS. One system wins. The other becomes read-only. Base the choice on three things: which platform holds the cleaner data, which one your highest-billing recruiters already know, and which one has the integrations you actually use. Do not pick based on which firm bought which. Pick based on what runs the business better.

Once you choose, freeze new work in the losing system. Every candidate added, every req opened, every note logged in the platform you plan to kill is cleanup you are creating on purpose. Tell the team the freeze date and hold it.

Then run a data inventory. You need to know what you actually have before you move anything. Count the records in each system. Find the duplicates. Flag the fields that are empty or inconsistent. This is boring work and it is the most important part of the whole 90 days.

Name one owner

Before day 30 ends, name one person who owns the consolidation. Not a committee. One person with a deadline and a direct line to the deal sponsor. When two ops leaders split the job, the migration falls into the gap between them and nothing moves. One owner, one deadline, one weekly check-in.

Days 31 to 60: Migrate and Dedupe

Now you move. With the surviving system picked and the inventory done, migration is a project with a known shape instead of a guess.

Map the fields first. The two systems store the same information in different places. One calls it "skills," the other calls it "specialties." One tracks pay rate as a number, the other as a text field. Build the map before you move a single record, or you will import garbage and spend the next quarter fixing it.

Dedupe as you go. The acquired firm and the buyer almost certainly share candidates. The same nurse, the same warehouse worker, the same engineer sits in both databases with slightly different spellings. Merge them during the migration, not after. A candidate record that exists twice will break every AI matching tool you plug in later, because the tool treats them as two people.

Here is the standard every migrated record should meet:

One record per person, deduped across both systems
Core fields filled: name, contact, skills, pay rate, placement history
Consistent formats for dates, rates, and status codes
A clear source tag so you know which system it came from

You will not get every record perfect. Aim to get the records you actually use clean, and park the dead weight. A candidate nobody has contacted in three years does not need the same care as your active bench.

Days 61 to 90: Verify, Then Build on Top

The last 30 days are about proof and the first layer of value. Do not turn on AI tools in week one of the migration. Turn them on once the data holds up.

Verify the migration with real work. Pull 50 placements from the last quarter and trace them through the new system. Do the numbers match? Does the candidate history line up? Can a recruiter find a req and the people who filled it without calling IT? If the answer is no, you are not done, no matter what the migration report says.

Shut down the old system for good once verification passes. Keeping it alive "just in case" means your team keeps checking two places and your data keeps drifting apart. Export an archive, confirm the backup, and turn it off.

Now, and only now, you build AI on top. With one clean system, you can run real AI matching, automated candidate outreach, and the reporting that proves placements per recruiter are climbing. The tools work because the data underneath them is trustworthy. This is the whole point of the 90 days. Clean data is not the goal. It is the foundation the AI roadmap stands on.

Skip the consolidation and you get the worst version of AI: confident, fast, and wrong. Do the consolidation and the same tools pay back, because they are reading data that reflects reality.

Why PE Operators Keep Getting This Wrong

The plan above is not complicated. It fails for one reason: nobody owns it on day one. The deal team moves to the next transaction. The ops leaders assume integration will "happen." Three months later the portfolio company is running two systems, the data is a mess, and the AI budget is sitting unused because there is nothing clean to point it at.

Fix that by treating tech consolidation as a day-one deliverable with a name attached, the same way you treat finance integration. The deals are coming fast. The firms that build a repeatable 90-day consolidation playbook will absorb acquisitions without choking on tech debt. The ones that wing it will keep pricing that debt into every exit.

This week, do one thing: if you have closed a deal in the last six months, ask who owns the tech consolidation and what the deadline is. If you cannot get a name and a date in one email, you have found your problem. Assign the owner, set the 90-day clock, and start with the inventory.

Tech Stack Consolidation After a Staffing Acquisition: The 90-Day Playbook PE Firms Don't Have