From AI Experimentation to Operational Maturity: How SASR Scaled Talent Acquisition

For most talent acquisition teams, the distance between building a successful AI pilot and a process that scales is where momentum dies. But SASR Workforce Solutions closed that gap, and the way they did it offers one of the cleaner blueprints available for organizations moving past surface-level adoption.

On a recent episode of Talent Experience Live, Bradley Cooper, AVP of Technology, Jason Effinger, Senior Recruitment Marketing Manager, and Brendan Easlick, Senior Marketing Coordinator, walked through four years of applied AI evolution, from basic scheduling automation to a live voice screening agent handling candidate interactions around the clock.

Watch the full episode here, or read on for the highlights!

What Did SASR's Hiring Operation Look Like Before AI?

SASR is a workforce solutions company built entirely around remote engagement. Candidates never visit a corporate office. Recruiters work out of a single centralized location. The workforce moves between clients like Dollar General, Walmart, and Advance Auto Parts depending on the season, and because the entire candidate population operates on mobile devices as their primary point of access, every hiring touchpoint has to work seamlessly on a phone.

SASR recruits continuously across multiple client sites, with demand that spikes seasonally and shifts depending on where client work is concentrated at any given time. Before partnering with Phenom, candidate management was fragmented enough that growth had become a structural challenge. As SASR expanded into new business lines, the team needed a more structured way to attract and move candidates through the process. Existing workflows had become disconnected enough that scaling efficiently was increasingly difficult, and the sheer volume of applicants moving through at peak season made those gaps impossible to ignore.

Phenom could handle what SASR needed from day one, without requiring them to wait for a later implementation phase before seeing value. By 2023, the full Applied AI platform was live, and the work of layering automation on top of a stable foundation had begun.

How Did Four Years of AI Adoption Unfold?

SASR's progression followed a crawl-walk-run arc, with each automation layer creating capacity for the next.

Scheduling automation came first, removing the recruiter from calendar coordination entirely. Candidates progressed through interview scheduling with no intervention from the recruiting or marketing team required. The immediate effect was hours recovered weekly, and more importantly, it created space for the team to experiment further.

What Cooper described next is perhaps the most practically useful framing in the entire conversation.

"Each time you add something into the process, that bottleneck moves," he said.

Every solution revealed a new problem. SASR fixed scheduling, but then interview volume caused delays. Once the voice agent fixed that, the handoff to operations slowed things down. The process showed that AI maturity isn’t seamless – it solves known problems while uncovering new ones.

Why Was the Phenom Voice Screening Agent So Effective?

January through April is SASR's peak hiring season, and the recruiting team has become leaner than in prior years. Previously, the answer was to bring in seasonal contract recruiters to absorb the volume spike. But predicting how many were needed, for how long, and where made planning difficult year over year.

The Phenom voice screening agent solved that problem directly. It scales up when volume spikes and back down without the overhead of managing temporary headcount.

"The voice agent actually does two things because it frees their time up to have those more meaningful conversations, while the handoff between recruiting and operations becomes smoother,” Cooper said.

The onboarding context that used to get communicated after the hire was now being established during the screening conversation itself, making the transition shorter and more human-led rather than administrative.

The mobile-first reality of the company’s workforce made the adoption curve shorter than expected. Candidates didn't need to download a new application or navigate unfamiliar technology. They clicked a button, took the call, or scheduled a callback at a time that suited them. After-hours and weekend engagement picked up significantly, opening a segment of candidates that standard business hours had previously made difficult to reach.

What Did the Numbers Look Like After AI Adoption?

The clearest signal of SASR's progress is what measurably improved after Phenom's AI and automation became embedded into day-to-day recruiting workflows.

For a travel-based workforce where a no-show on day one means a client site goes unstaffed and a lean remote team has to scramble to backfill, first-day show-up rates aren't a vanity metric. They're a direct measure of whether the hiring process is setting candidates up to follow through. Before automation was in place, misaligned expectations and manual coordination gaps were quietly eroding that number. After, the difference was measurable.

In one month, 95% of placed workers arrived on day one.
Across 2025, the first-day show-up rate held steady at 92–93%.
Earlier qualification and expectation-setting prevented roughly 800 mismatched interviews, saving more than 250 hours of recruiter time.
After-hours and weekend coverage from the Voice Screening Agent significantly expanded candidate engagement hours.
Onboarding conversations improved as recruiters moved out of back-to-back interview blocks.

For SASR, the gains extended beyond efficiency. Recruiters had more time for onboarding conversations, and candidates moved through screening on their own schedule.

How Can You Build Internal Confidence with AI Without Creating Fear?

SASR expanded its AI adoption gradually, which helped organizational buy-in. The company's approach centered on visible, contained wins before any broader expansion. Scheduling automation was familiar and low-stakes enough to build confidence, and that confidence carried forward into the Voice Screening Agent rollout. Recruiters who had already seen automation work in a limited context were then more open to the next layer than they would have been if both had arrived simultaneously.

Also, throughout the rollout, Cooper maintained an important boundary. "AI should never say, 'Hey, we want to hire you,'" he said. "We're a company. We want people to hire people. But we can get all the admin work out of the way."

That distinction kept recruiter buy-in intact because the technology was consistently positioned as support, not substitution, and the line between what AI handles and what humans own was never blurred.

Where is AI Maturity Headed for SASR?

Cooper was candid about something many organizations avoid discussing: AI maturity rarely develops evenly, and pretending otherwise creates unrealistic internal expectations.

Within SASR's recruiting workflows and the Phenom ecosystem, adoption has advanced significantly through sustained, layered investment. Across the broader organization, however, maturity levels vary by workflow ownership, priorities, and the systems invested in. That unevenness isn't a failure; it's an accurate reflection of how maturity builds through focused application rather than broad rollout.

What's also interesting is the direction that the spread is now moving. The framework and operational confidence built through the Phenom partnership have sparked conversations about other technology systems within the organization. The recruiting function has effectively become the internal proof of concept, pulling other departments into the discussion because of results that other leaders can see and want to replicate.

The next formal priority is Phenom Onboarding, which has already begun. On the candidate-facing side, Easlick is continuing to invest in the career site experience through Phenom Design Studio, staying in early access for new Content Management System (CMS) capabilities as they become available.

The broader principle Cooper offered to close the conversation is the one worth carrying into any AI adoption conversation: Look at your processes, find one friction point worth solving, and move from there rather than trying to change everything at once.

Four years in, SASR has moved from crawling to running, one bottleneck at a time. The question for most talent acquisition teams isn't whether to start. It's which step they're on and what staying there is costing them

83% of organizations are still in the early stages of AI and automation maturity. Find out where yours stands. Access the State of AI & Automation: 2026 Benchmark Report

Devi B

Devi is a content marketing writer passionate about crafting content that informs and engages. Outside of work, you'll find her watching films or listening to NFAK.