In the not-so-distant past, a familiar narrative played out in Silicon Valley—a thriving hub for innovation and technology. Bright, young professionals eagerly entered the job market, anticipating a swift journey into an industry renowned for its efficiency. At the time, I, too, harbored this belief, influenced by the efficiency principles ingrained in me by my computer science professors.

Yet, the reality unfolded quite differently.

The typical sequence of events went as follows: initial interviews moved swiftly, commencing with a phone screening, followed by rounds of friendly conversations within the HR departments of San Jose and Palo Alto startups. Then came the technical skills assessments, sometimes extending into a bewildering cycle of third, fourth, or even fifth evaluations.

Beyond this maze lay Judgment Day—not just one, but multiple Judgment Days: on-site group interviews, meetings with hiring managers and encounters with C-suite executives. In some instances, an anxiety-inducing coffee gathering with the CEO or founder awaited.

Ultimately, every candidate faced the dreaded waiting period. Regardless of whether the interview process culminated in an offer, an agonizing phase of uncertainty prevailed, during which HR and hiring managers seemingly organized their resources.

For businesses, this translated into inefficiency and a time-consuming ordeal.

Clearly, there had to be a superior approach.

A New Approach

Now that I lead an engineering team at a startup focused on staffing tech companies through the power of AI, I know firsthand how and why traditional recruiting processes – even the ones with happy endings – can take far too long. Every essential part of the hiring process, from that initial screening through the offer letter, entails a complex series of interlocking sub-parts, a morass of competing executive schedules and a tangled web of approvals and signoffs.

Recent reports put the length of time-to-hire processes at an all-time high, an average of 44 days in 2023. What are the consequences of this systemic lag? Companies forfeit valuable goodwill with candidates who put faster response times at the top of their recruitment wish lists.

However, there are ways of streamlining and shortening the hiring timetable. And you’ll find these strategies benefit the employer as much as the job seeker.

Tech-Driven Automated Screening

For HR professionals, historically, one of the more onerous parts of the hiring process has been sifting through the avalanche of resumes that come in for competitive openings. Reviewing resumes manually, or even running them through outdated keyword scanners, is both a time-suck and a strain on the eyes.

Instead, HR departments should consider adopting AI tools to automate the screening process and quickly surface the most qualified candidates’ applications. Resumes and other application materials can be run through AI to scan for sets of pre-selected keywords. The model itself can also generate appropriate keywords for an ideal candidate. The more application materials the model scans, the better it becomes at building job-specific keyword vocabularies.

AI can also extract information from resumes, including educational credentials, work experience and skills, and generate organized tables that are easier for recruiters to navigate. Resume data can then be fed back into the AI to score and rank candidates based on how they compare to an ideal profile. In effect, the AI can take a bunch of raw resumes and spit out a refined interview priority list.

GEM Recruiting AI

Optimizing Applicant Tracking

A recruiting process lives and dies by how effectively staff can keep track of candidates. The user-friendliness and efficiency of an applicant tracking system (ATS) can make a big difference. An AI-powered, software-based ATS will streamline various time-consuming hiring processes, like scheduling interviews, managing candidate data and communicating updates and decisions.

For example, an AI-powered ATS can automatically schedule interviews based on the availability of candidates and interviewers, saving HR the headache of balancing dueling calendars. These AI-based ATS models can also prioritize scheduling interviews with candidates who score highest in AI-generated rankings.

One of the main risks of a prolonged interview process is that a desirable candidate feels forgotten and might get snapped up by a competitor who’s quicker on the draw. AI can help keep candidates informed and engaged by automatically sending status updates, reminders and even projected timelines to candidates at regular intervals.

Suppose you’re worried that AI-generated notifications might appear canned or overly general. In that case, these systems can personalize candidate communications based on the information contained in resumes and other application materials and online profiles on platforms like LinkedIn.

These systems can also offer interactive elements that answer candidates’ job-related queries in real-time. AI-powered chatbots can answer various candidate questions and support the hiring process, freeing recruiters and hiring managers to focus on higher-value tasks, like preparing for and conducting more in-depth conversational interviews or developing new recruitment strategies.

Assessing Skills

I undertook countless technical skills assessments in my early tech career. While the sheer number of evaluations for a single job opening sometimes felt like overkill, now, as someone who does a lot of hiring, I recognize the absolute necessity of accurate skills assessment.

Generally, when HR departments administer technical skills assessments, we’ve already significantly winnowed down the candidate pool. And while this may seem like the biggest step towards a final decision, HR professionals often find this stage is where choices get tough.

Enter AI. AI can not only administer skills assessments – multiple-choice tests, technical Q&A interviews, coding challenges, etc. – it can also score them with acuity and attention to detail few humans could match. If there are minuscule distinctions in the performance of two ostensibly similar candidates, AI will find them.

AI can also similarly administer soft skills and personality assessments, generating everything from quickly digestible summaries to thorough, in-depth profile reports. Examples here might include communication skills, for which AI can analyze candidates’ writing samples and live-interview speech patterns for clarity, conciseness and persuasiveness. AI can also be used at this stage to measure problem-solving skills by analyzing answers to questions or problems that are more open-ended and open to interpretation than Q&A or multiple choice skills assessments.

Of course, it’s important to note that AI algorithms, while highly effective candidate selection tools are not infallible. They can be biased, buggy and opaque, often when the data they’re trained on is flawed or outdated. Human oversight is therefore essential to ensure that AI-powered hiring processes are fair, equitable and transparent. While the possibilities for speeding up the hiring process with technology are manifold, it’s essential that hiring retains some aspects of human involvement.

Final Thoughts

We don’t need to hold ourselves prisoner to outdated, sluggish conventions in hiring. Tech-driven recruiting solutions can make the experience smoother, faster and more effective for all involved. Organizations implementing these solutions will undoubtedly enjoy the fruits of a solid-gold reputation among a notoriously implacable bunch: modern job seekers.

Yuzhao Ni

Director of Engineering at Turing