At Staffing Advisors, we are process people. We rely on data and research to inform our choices. We build workflows and test and tweak until they are just right. And then we keep adjusting because nothing is ever just right all the time. That’s how we help clients solve their most challenging hiring problems with predictable accuracy and reliable timelines while running with a small team. And now we are using generative AI and process automation to optimize our workflows and reduce the time we spend on repetitive tasks. That means more time to meet our clients’ needs.

We Don’t Use AI for Decision Making

Before we dive into how we use AI and process automation for our internal workflows, we want to be clear about how we use these tools in our executive recruiting process—and how we don’t.

We use tools like LinkedIn Recruiter and hireEZ to supplement our custom sourcing methods (although their AI functionality is limited and tends to yield less than 5% of the total candidates we source) and SourceWhale to automate scheduling our initial outreach to passive candidates. But our use of AI for recruiting stops there.

We don’t use AI for anything related to evaluating candidates. We don’t use AI to review or screen resumes or profiles. We don’t use AI to analyze responses to screening interviews or supplemental information questions. Using AI to sort and rank candidates would undermine our skills-based, inclusive hiring approach. There is too much risk for AI to perpetuate the decades-old hiring biases we are working against.

AI and Automation: Case Studies in Productivity

Here’s the fun part (if you love efficiency like we do). Using process automation and AI, we revamped workflows for two time-consuming client deliverables and changed the way we record and distill key information from calls.

Here’s a quick explanation of what the candidate calibration is. After the kickoff call for a new search, our sourcing team identifies 10-15 sample candidate profiles to gauge whether our vetting criteria is on the right track. To create the client deliverable, another team member would anonymize the profiles, remove irrelevant data, and compile them into a large document. It used to take 3-4 hours to create.

There had to be a better way.

We did some research. We experimented with different tools. And we created a new workflow. Our sourcing team still dedicates the necessary time to identify and analyze the profiles, but we reduced the rest to less than an hour and just a handful of clicks. Here’s how:

  • We use a data extraction tool to export the information from the profiles into a JSON file which we then convert into an Excel spreadsheet.
  • We set up an Excel macro to organize the data.
  • We merge the data from Excel to a preformatted template in Word which we then export as a PDF to share with clients.

No more frustration with formatting. No more copying and pasting. And we can shift our focus to the more complex aspects of what our clients need.

Checking references is a valuable service we offer clients. We conduct a 15-20 minute phone call with every reference for every finalist for every search and produce summaries for the client. Not including the call, this can take anywhere from 1-2 hours. With multiple references for each candidate, that adds up.

Producing summaries of the calls while maintaining the tone of the conversation takes time, but not a lot of judgment—a great use case for automation and AI. After some experimentation with different tools, we developed a new flow that reduced time to about 20 minutes:

  • We record (with permission) reference calls using Teams and export the transcript.
  • We spend a few minutes reviewing the transcript and develop prompts to guide the AI in accurately summarizing key points.
  • To protect the privacy of both the candidate and reference, we use a Word macro to swap out any personal identifiers like names or specific companies with placeholders. As an added layer of security, we also turn off the chat history setting in ChatGPT. This prevents the platform from using our data to train.
  • We upload the formatted and anonymized transcript to ChatGPT, tweak the prompts until the desired output is achieved, review it for accuracy, and paste the summaries into a nicely designed Word template.
  • We swap out the placeholder names for the real ones and off to the client it goes.

Our kickoff conversations with clients and interviews with key stakeholders are packed with highly detailed information—capturing it is essential. It’s a heavy cognitive lift for team members to take precise notes while still engaging in the call.

So we started using to transcribe and record calls (with the permission of all participants). While Fireflies captures the details, our team is free to listen deeply, ask good questions, and take more targeted notes. The platform has AI processing features like creating summaries, lists of action items, a GPT-4 chat interface, etc. The output isn’t perfect, but it often helps to cut the time it takes to review and synthesize takeaways.

Although we consistently use Fireflies for kickoffs, it isn’t the best tool for every use case. Some team members use for other types of calls. Others prefer to record calls in Teams. And some don’t use tech at all. We each have the flexibility to test different platforms and use what works best for our role or the specific task.

( and are both SOC 2 (Type 2) certified and GDPR compliant, ensuring that all interactions through these tools meet stringent security and privacy regulations and protect the confidentiality of our clients.)

Ready-Made Solutions Rarely Fit What We Need

Everything about our recruiting and hiring process is custom. We developed Results-Based Hiring® almost twenty years ago and have been refining and improving it since. And that means many out-of-the box technologies aren’t designed to support what we do. But improving our processes is integral to delivering the best client service possible. So we DIY technology solutions. We aren’t tech experts, but we know what works.

The more time we save on rote tasks, the more we can dedicate to meeting our clients’ needs, developing robust decision-support materials, and responding to an ever-shifting job market. We work smarter so our clients can hire better. AI and process automation are helping us along the way. (Read more about our privacy and technology practices here.)