How we’re embracing AI in our hiring software

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9 mins, 33 secs read time

Our approach and product innovation developments

It seems like everyone, everywhere, is talking about artificial intelligence (AI). When it comes to HR and Talent Acquisition teams, the concept of applying AI to the hiring process has been mixed. A recent report from Harvard Business Review shows that 91% of key HR decision makers “believe optimizing hiring processes with automation and AI is necessary for long-term business success.” While research from Pew on AI sentiment in the US overall is a little less rosy – with many people reporting feeling “wary and uncertain of AI being used in hiring and assessing workers.”

Greenhouse candidate survey data from over 2,700 candidates and over 100 HR leaders supports both sets of research. We’re finding that 62% of HR Leaders believe that AI can help them hire the best candidate – while 31% of candidates are worried that a company using AI might reject their application.

As AI continues to dominate headlines and conferences, discussions sometimes veer to extremes. So, are the robots taking over, or is AI the singular answer to all our hiring challenges? While such questions might be interesting to ponder (and we’re not above pondering), there’s plenty of room in the conversation for exploration, excitement and innovation. Helping recruiters write job posts and candidate emails faster is only the beginning.

Let’s take a look at those areas beyond the expected where we believe AI can really make a positive impact on hiring in the near future, including some aspects of Greenhouse product functionality where we’re already featuring this new technology.

AI Content generation: making hiring faster and more effective

Even with some semblance of AI being built into almost everything, it’s clear there’s so much more it can do for hiring than the methods we’ve seen harnessed in the market today.

One of AI's most widely-known applications is content generation. Large Language Models (LLMs) have proven to be a game-changer for those in and outside of hiring, allowing recruiters to create job postings and prospecting templates with ease. Even now, LLMs enable recruiters to go from zero to one in the structured hiring process, faster. In 2024 we’ll launch the next generation of our hiring software with advanced text generation models built directly into our platform to power faster and more effective hiring.

A good example of this: a recruiter who wants to generate attributes for a specific role will need to generate related attributes and interview questions to test against. Using generative AI (genAI), a recruiter can select the correct prompts to create this instantaneously – while automatically reducing human bias at this stage of the process.

Imagine also the quality of job postings and candidate outreach improving with this technology. The use of AI in content generation for recruiting teams doesn’t only make things faster or increase the volume, but also improves sourcing quality by tailoring outreach to each unique role, using a consistent employer brand voice. Adjust for keeping a candidate warm, including a different tone, at the click of a button. Consider also being able to reach candidates in their native language without the need for expensive translation services. Using AI as their co-pilot, the recruiter is now freed up to focus on more strategic work.

Categorization: reflecting human intention

We’ve mentioned reducing bias, because it’s impossible to fully remove it from the hiring process. But with AI, it’s now possible to reduce bias that much further, creating a more equitable and inclusive experience for candidates.

Picture this: you post a role and hundreds of candidates are applying. Of course, the ability to sift through resumes quickly is essential. But the biggest challenge when it comes to traditional resume parsing, or the categorization of specific content fields, is keeping that process fair. Everyone’s resume is vastly different – from formatting to language choice.

While we’ve long been using an in-house method for resume parsing, applying genAI data analysis now makes it possible to gather the intention behind resume terminology. With higher accuracy in identifying similar roles, AI-powered candidate search tools can now help recruiters identify potential candidates with related skills and even past candidates who reached late stages in similar positions. Now, grouping related categories – something that used to be challenging for a machine to put together without individual instruction – becomes easy.

Think of the ways this unlocks equity and parity among resumes flowing in for a particular role. And how the concept of skills-based hiring is made more accessible as recruiters are now able to source from a broader set of relevant candidate experiences. This is ground-breaking and ensures that keyword fields no longer hold recruiters back from finding the right candidates.

We’ve already added genAI to the parsing process, which allows us to improve the quality of our upcoming Resume Anonymization functionality (where specific resume details are obfuscated to provide a more equal analysis of candidates). In the future, Greenhouse will use AI to create more uniformity in job skills via our in-house similarity models with Structured Candidate Search: the ability to identify common characteristics of a resume, even if they’re phrased differently. For example, the ability to summarize a candidate's years of experience in a certain field. Future benefits also include increasing the accuracy of benchmarks against other companies to help surface and suggest different sourcing strategies – which helps people more intuitively fill out prospecting templates, job posts and more. There’s so much ground we’ve covered already and we’re excited to tackle what’s next in this area.

Summarization: using AI to transform data into insights

A big part of hiring efficiently is when people are able to collate lots of data, quickly. For a busy recruiting team, what better than to be able to ask questions and quickly receive insightful answers from your hiring platform in an intuitive and human-centric way.

Giving our users the ability to have a natural language conversation within our system means saving hours and hours of a recruiters’ time. Imagine typing in a sentence to find the best technical candidate who's also gotten the most strong yes scorecards from the team. Or asking for a summary of reports so you can more easily track your hiring efficiency over the last three months. Using AI, our customers can now take a fresh look at all aspects of the structured hiring process in a fraction of the time.

Another exciting area in automation with AI is summarizing interview transcripts. Think about the potential for recruiters being more present in their interviews, knowing AI will be there to help them with note-taking and synthesizing conversations with candidates. Having an objective view of what someone said in an interview will also help reduce bias. This benefit will be felt by candidates too, with more focused recruiters able to put less time into transcribing answers and more energy into getting to know each person on a human level.

By aggregating and summarizing a diverse set of data, AI provides a deeper layer of sentiment analysis, ensuring a more fair evaluation for candidates. For example, this technology can catch when someone is making presumptions in an interview – interrogating how subjective vs objective a statement is. AI can also test whether statements are grounded in reality or interpretive and help detect gendered or combative language in candidate feedback. A good way to think about this is, anything that’s “fuzzy” about a conversation with a candidate, AI can help make more clear.

Automation: streamlining complex tasks

If you’ve ever tried to schedule a meeting with more than one other person, you’ve felt the pain that many recruiters feel when setting up times to meet with candidates and hiring managers.

It’s the classic tetris problem – understanding what calendar blocks are and which can be booked over is always a time-consuming negotiation. Imagine if the first conversations around panel scheduling were done by the prompting engine in a natural language conversation. “Find my team a time to have a panel discussion about a candidate that suits everyone’s time zone, prioritizes free time over busy and doesn’t book over lunch.” This type of system would break down barriers and interpret calendars more efficiently – again helping busy recruiters get back to other more strategic tasks.

But scheduling is just one piece of how AI and automation could work together to create pathways for efficiency. Consider how an automated applicant flow could be used at the top of your hiring funnel. A system that would turn off the inflow of applications – and more crucially, when to turn it on when you’re not getting enough applicants, or when to start buying job ads programmatically – relies on rules that allow you to decide if you’re getting too many applicants for a role.

In the future, we want to use AI to help customers tackle the challenge of knowing when to activate or deactivate job postings, automating application flow management and even forecast hiring needs, leading to broadened candidate pools. With this new technology, companies will be able to better allocate hiring resources, saving money and time.

Hiring decision-making with AI: our perspective

AI is here now. And there’s no denying it’s powerful. In hiring, we know that every candidate is unique. And understanding the differences between each candidate efficiently has historically been the goal for nearly everyone in the hiring space. In the past six months, we’ve seen both newcomers to the hiring software market as well as legacy establishments running at this problem and offering instant AI solutions.

While many people are excited for the possibilities, we recognize there’s still some trepidation from businesses about its effectiveness – and from candidates that AI might not benefit them. The challenge with AI is that when you allow the machine to write the rules based on how things are today, you also inherit all the bias that currently exists.

The difference for us is that right now, we have no evidence to believe that AI is capable of making end-to-end hiring decisions without human intervention. There’s just no good business or moral reason to hand the wheel to AI when we are aware of its existing flaws and risks. That’s why we’re intentionally investing in research that drives ethical and sustainable hiring, where AI can assist, but not replace, hiring decisions made by human beings. We see AI as a co-pilot, not an auto-pilot. That concept alone is exciting – the idea that with AI, recruiters and hiring managers will have access to instant, personalized automation to help them through their busy day.

No doubt, the advancements in AI technology are transforming hiring at every level. It’s important for any company in the hiring space using AI as a way to make things better, faster, fairer and more efficient, do so in ways that benefit companies and candidates alike. At Greenhouse, we’re ready for what’s next. We’re committed to helping our customers utilize advances in AI to get better at hiring, while staying ethically responsible and sustainable.

Our approach to structured hiring empowers companies to facilitate stronger recruiter and hiring manager alignment, improve candidate experience and ultimately make better hires. Learn more at

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Henry Tsai

Henry Tsai

is Chief Product Officer at Greenhouse Software, focusing on enabling and empowering the product and design teams to create solutions for Greenhouse’s people-first customers. Henry acts as a product advocate, investing in both the success of the products as well as elevating and solidifying relationships with both Greenhouse customers and partners.

Prior to joining Greenhouse, Henry worked in leadership positions at SAP, building AI/ML solutions for Intelligent Spend Management. Henry received his B.A. in economics from the University of California, Irvine and holds a patent for configurable entity matching systems.

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