What Legal Talent Teams Are Already Doing With AI

Law firm talent leaders are asking: what does AI actually change about our work now, not in five years?

The technology matters less than what a team does with it. Over the past few months we released a set of AI capabilities across Flo Applicant Tracking and Flo Performance Management, plus Flo AI reporting on top of both, and it’s been thrilling to Flo to see what firms have done with them: a candidate database that holds up under scrutiny, performance reviews that deliver superior feedback, answers to questions that used to require a spreadsheet and a free afternoon, and a resume parser that actually works.

Here’s what our recent updates look like in action for Flo clients.

A candidate database your recruiting team can trust

Candidates enter a database through direct applications, events, agency submissions, and manual adds. Any of those paths can create a second record for someone already in the system, and fragmented history makes a candidate's status harder to trust when it matters most.

In agency-driven lateral recruiting, that is a financial problem as much as an operational one. Unclear candidate ownership is how fee disputes start.

Flo now scans the candidate list with the most advanced automated matching on the market and flags clusters of records that appear to represent the same person, rather than relying on any single, exact field match. Opening "Manage Duplicates" shows each cluster, the records inside it, and whether agency submissions are involved. Merging produces one authoritative record, and all agency submission history survives the merge, which is the point: firms need to know which agency submitted whom long after two entries were combined. If a cluster turns out to be two different people, dismissing it keeps them separate going forward.

The duplicate problem gets caught as it arises rather than when it’s too late to avoid a fee dispute.

High quality feedback that reflects what the reviewer meant

Performance reviews shape how an attorney is perceived, developed, and advanced. The writing is hard. A comment can land harsher or vaguer than intended, and coded language can slip in unnoticed before a review is published. Reading every comment for tone manually has never been realistic at scale.

Two capabilities in Flo Performance Management now sit with the reviewer, in the moment.

AI Rewrite allows partners and managers to paste messy notes or jot down stream of consciousness thoughts in their review form and turn them into a cohesive, polished review. AI Rewrite provides readily available prompt options like “more concise” and “more direct,” plus allows for custom instructions for the rewrite. Rewrite works only from review content already entered, never adding in examples or details that were not originally written. The substance stays with the human.

AI Tone Assist helps reviewers deliver high quality and appropriate feedback by 1) highlighting phrases that may read as vague or problematic, 2) providing an explanation, and 3) suggesting a correction, like that the reviewer add specific examples or replace a certain word. It flags gendered or coded language applied unevenly across groups, references to protected characteristics, vague feedback with no supporting example, unprofessional language, and overused HR clichés. Flags are informational, and Tone Assist never blocks a submission.

Reviewers get a chance to self-correct before feedback reaches a reviewee or enters a formal record. Professional development and HR teams get a consistent standard applied across reviews without depending on manual oversight alone.

Questions that used to take weeks to answer – that firm leadership needs answers to now

Which recruiting sources produce the associates who go on to perform the highest? How do themes in performance reviews affect our recruiting strategy? Which interviewers consistently predict a top performer? Firm leaders have carried questions like these for years without a practical way to answer them, because the answer lives in two systems that never talked to each other.

Flo AI reporting answers them in plain language, from a firm's own recruiting and performance data, across Flo Pipeline Building, Applicant Tracking, Work Allocation, and Performance Management. A practice group head opening a senior litigation associate search can ask which qualifications predicted strong hires in past litigation searches and which candidates in the current pipeline match. A talent leader can ask which skills and backgrounds now predict the firm's highest performers, and how that has shifted over the past few years, reading interview evaluations and performance reviews together.

That second question is the one that was previously out of reach. Spreadsheets handle structured data, and the most useful signal in talent work sits in the unstructured text of feedback forms, evaluations, and notes. Flo AI reporting reads that text at scale and points to the candidates, reviews, and matters behind every answer, so a leader can check the reasoning rather than take the number on faith.

Decisions about who to hire and how to develop them now rest on a firm's full record rather than whatever could be exported into a spreadsheet before the meeting.

Resume parsing that actually works

Resume parsing is table stakes for an applicant tracking system, yet has a reputation for low quality results. Most teams have used a version of it that dropped a job title into the school field, mangled anything with two columns, and left the candidate correcting the form rather than filling it out. Table stakes only counts if the feature works, and for years this one mostly did not.

AI changed what is possible here. Parsing is now live on the candidate-facing career page, in the agency portal, and on the admin job details page. A candidate or agency representative uploads a resume, and in about 10 seconds the application fills in bio data, education history, and work history, accurately enough to leave alone. The parser does not touch self-identification fields or application-specific questions, which stay deliberate. The uploaded file attaches itself to the resume section, so nobody finishes a form and then goes looking for the document again.

Lateral candidates – and their agency recruiters – are employed, busy, and evaluating an opportunity carefully. Retyping a work history that already sits in a document on their desktop was never a good use of that attention. For agencies submitting on a firm's behalf, the effect compounds, because every manual field was a place for a typo, a delay, or a drop-off. Teams are seeing more complete applications arrive with less back-and-forth to correct them.

The common thread

None of these features make the decision. The parser does not fill in self-identification fields. Duplicate detection surfaces clusters and leaves the merge to an admin. Rewrite will not invent a fact a reviewer did not observe. Tone Assist flags and explains, then gets out of the way. Flo AI reporting shows its work and leaves hiring, promotion, and development calls with the people accountable for them.

That is deliberate. In legal talent work, judgment is the job, and the useful role for AI is removing the friction around it. Each of these came out of clients telling us what they were running into. Tell us what you hit next.

Want to see these in action? Book a demo and we will walk you through them.

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