Raise your hand if you’re a little afraid of ChatGPT.
Yeah, same.
AI is everywhere. In the last few years, it’s transformed the way we work, live, and communicate. And using AI in recruitment is a way to make your process more efficient, while also giving you back more time to connect with candidates.
You can’t afford to not keep up — so we’re here to help.
Quickfire stats: Why AI is a game-changer for you
You know that AI can help you recruit better. But just to convince you more, let’s rattle off some numbers:
All the cool kids are doing it: 42% of UK tech firms are using AI to screen and recruit candidates. And up to 90% of large private-sector employers have incorporated AI into at least some of their hiring process.
At least 70% of recruiting teams believe AI is actually making their hiring decisions better, and companies report up to 71% lower cost-per-hire and an average of 4.5 hours saved per week.
It goes way beyond asking ChatGPT to write you some emails. It automates repetitive workflows and pulls conclusions from large sets of data, so you can focus on what actually matters: engaging candidates, planning for the future, and making your processes more human-focused.
To back us up, we asked our Talent Associate, Lauren Brown, what she thought about using AI during the recruitment process. “AI is a really good tool to use,” she said, “but it’s super important to still be human-centred.”
In this blog, we’ll outline five key AI use cases — from screening to diversity — to help you automate while still staying focused on a great candidate experience.
1. How to automate candidate screening and shortlisting with AI
Every recruiter knows that manually reviewing hundreds, if not thousands, of CVs and applications for a role gets really old, really fast. AI-driven systems can scan resumes, evaluate candidates against role requirements, and even rank or score applications for recruiter review.
Many modern Applicant Tracking Systems (ATS) often have AI tools built in that use machine learning to identify keywords, qualifications, and experience that match your job criteria.
Tips for using AI in screening:
- Leverage AI in your ATS: Many recruiting platforms, including Workday and iCIMS, offer AI-driven features. These systems automatically rank applicants by fit, using algorithms trained on gigabytes of recruiting data.
- Try generative AI for summaries: With careful prompts, tools like ChatGPT can summarise a candidate’s CV or LinkedIn profile with key strengths and potential concerns.
- Keep it human: AI isn’t coming for your job — you’re still the boss. AI can surface qualified candidates fast, but a human should always validate that those picks truly match the role and company culture, and make sure no superstar candidates are unjustly filtered out.
- Get proactive: Don’t wait for the CVs to come to you. If you’re looking for a hard-to-fill role, AI tools can take basic requirements and search thousands of online profiles to pinpoint a short list of qualified candidates.
2. AI-powered writing: Job descriptions and candidate outreach
Crafting job descriptions and personalised outreach is another area where AI shines.
Recruiters can use tools like ChatGPT and other writing assistants like Grammarly or even Google Gemini to draft job adverts, email, and other recruitment assets.
In fact, writing job adverts was the number one AI use case identified by HR professionals in a REC survey.
The advantage? Consistency and speed: once you have clear guidelines, AI can ensure all your postings hit the right keywords, stick to a consistent tone, and generate variations for different platforms.
Tips for content creation:
- Keep a consistent brand voice: AI writing can occasionally…lack personality. Always review to make sure it matches your organisation’s tone of voice.
- Get specific: An AI draft will be generic, but your company isn’t. Provide details to highlight what’s unique about the role. For example: “Write a job description for a [specific job title] position at [company], a [company size] [industry] in [location]. Emphasise [standout feature one] and [standout feature two].”
- Check for bias or errors: AI is great at writing a first draft, but it might inadvertently introduce bias, incorrect facts, or make assumptions. Manual reviews can spot any terms that could discourage certain groups from applying (more on this in a sec).
3. Improving diversity, equity and inclusion with AI
A lot’s been said about how AI isn’t great for improving DEI efforts at companies. And it’s true — AI is only as good as the data it’s trained on, and data can be just as biased as humans.
To use AI for enhancing DEI, recruiters should focus on solutions that promote equity and broaden talent pools, while exercising oversight to prevent discrimination. Here’s how it can help:
Writing unbiased job adverts
An analysis of UK job ads found that 60% had significant male-focused wording, which has been shown to discourage female candidates.
Tools like Textio flag these issues, spot gender-coded words and even predict the likely diversity of the candidate pool that a job description will attract.
Other platforms like Applied, a UK-based recruitment platform designed for “blind hiring”, helps employers write job specs that focus on skills and uses AI to make sure language is inclusive.
But does it work? Companies that use the tools have seen results. Accenture UK reported increasing female applicants from 34% to 50% for certain roles after systematically de-biasing their job descriptions.
Getting smarter about sourcing for diversity
Traditional sourcing depends on your network, where you can look, and typical career paths — which means you might have qualified candidates falling through the cracks.
AI-driven sourcing platforms can cast a wider net by scanning millions of profiles. This skills-based approach means if a candidate has the capabilities needed, the AI can find them. They’ll also highlight "adjacent skills”: people who, with a little training, could fit the role, which can add diversity to your pipeline.
All this to say, AI isn’t inherently unbiased — the opposite. It learns from historical data that can reflect bias. One study from Cambridge University in 2020 found that using AI “to reduce bias is counter-productive”, and in 2018 Amazon scrapped their AI-powered recruitment tool because it was discriminating against female applicants.
Tips for enhancing DEI with AI:
- Use AI as an assistant, not a gatekeeper: Always review AI decisions, especially rejections. Ensure there’s a process to override or correct the AI if needed to give candidates a second look.
- Continuously audit for bias: Who are your candidates, and where are they coming from? Track demographic metrics at each stage. If you see drops in minority representation after introducing an AI tool, investigate and adjust. Many AI tools provide audit reports — use them.
- Combine tech with training: AI can blag bias, but first you need to be trained in unbiased hiring practices. If your interview panel isn’t trained to carry fairness through later stages, it’s all for nothing.
4. AI for better interview prep and analysis
Whether it’s writing questions or analysing interview results, AI can help you optimise. Here’s how:
Interview prep for recruiters
Tired of writing interview questions? Tools like Claude or ChatGPT can help you write structured interview questions in seconds.
For example: Give [number] behavioural interview questions for a [job title], focusing on [company value] and [company value].
Scheduling interviews
Every recruiter knows the pain of having to coordinate five wildly different schedules with one candidate’s availability.
AI scheduling assistants like Paradox’s Olivia (a conversational AI assistant) can handle the back-and-forth so you don’t have to. If you integrate an AI chatbot with your calendar, it can:
- Offer candidates available time slots
- Schedule or reschedule meetings
- Send confirmations for you
Post-interview analytics
After the interview is over, AI can help you synthesize your feedback. Some companies use AI to look over interview notes and scorecards for common patterns to make sure no detail is overlooked.
Tips for interview stage AI:
- Be transparent with candidates: If you’re using AI to schedule interviews, let candidates know to build trust. Clarify that final decisions are made by humans, and consider drafting a policy around how you use AI as a recruitment team.
- Combine AI scoring with human review: Treat AI interview assessments as a helper, not the final say. This ensures a personable hiring process and catches anything the AI might miss, like context or creativity.
- Use AI note-taking tools to capture the little details: Tools like Granola can help you focus on what the candidate is saying instead of rushing to get down the little details (notice periods, visa requirements, salary, etc).
- Train AI with your top performers: If possible, feed your AI data on what good looks like at your company. Some AIs will let you give it transcripts of high-performing employees’ interviews to help it learn what answers predict success at your organisation.
5. Talent market mapping and analytics with AI
There’s so much more to recruitment than just filling roles. And with AI tools, you can proactively understand the talent landscape, plan for the future, and make data-driven hiring decisions.
Here’s how:
Get talent market insights
AI tools can churn through enormous amounts of market data — job posts, professional profiles, economic trends — to let you know about what skills are in demand and available.
Specialised analytics firms use AI to scrape and interpret job market data, showing you where your target candidates are and how to hire effectively.
Internal workforce analytics and planning
AI helps you partner with managers to make talent decisions. Whether it’s identifying internal skill coverage in relation to industry benchmarks or looking at salary, this can help you identify future needs.
These platforms can also predict talent flight risk, suggest opportunities for internal promotions, and identify gaps before they become critical.
Predictive analytics in recruiting
Oracle’s AI uses predictive modeling to forecast time-to-fill for a given job based on historical data. If you know a role will take a long time to fill, you can set expectations with hiring managers and come up with solutions.
Predictive analytics can also estimate hiring costs, or even the likelihood of a candidate accepting an offer.
Tips for talent analytics:
- Invest in a talent intelligence tool: If you’ve got the budget, platforms dedicated to talent analytics can integrate with your ATS and give you real-time data on your talent pipeline and market trends.
- Collaborate with HR and business units: If you have data that shows a role is hard to fill, talk to hiring managers about adjusting job requirements or offering flexibility on timings.
- Stay up-to-date on labour market trends: Using reports from CIPD or the UK government’s immigration salary list can help you augment AI analysis and give you a full picture. Use these insights to stay agile and target talent before your competitors.
Best practices for implementing AI in your recruitment process
If you’re reading all this and wondering where to start, don’t worry.
Adopting AI in talent acquisition can seem daunting, but the right approach can give you all the benefits and minimal risk.
Here’s how to get started:
- Identify pain points: What part of your process needs improvement — CV screening, scheduling, job ads? Set clear goals around what you want to achieve.
- Start small: Don’t try to do everything all at once. Whether it’s ChatGPT for emails or AI CV screening, start with one project and work out the issues before rolling it out further.
- Keep an eye on outputs: During your first pilot, go over everything with a fine tooth comb. Did the AI screening work? Did the job descriptions attract the right candidate? Continuously monitor and refine, double-checking AI decisions against human judgement.
- Stay compliant: Whether or not you’re using AI for HR compliance, work with your legal team to understand the implications. Especially in highly regulated industries, ensure the tools comply with GDPR and the UK government’s guidance on responsible AI in recruitment.
- Get your team involved: Train your recruitment team on the AI tool effectively and explain that it’s there to help, not replace. Encourage them to be critical about the AI output and find internal champions to help get buy-in.
- Keep the human touch: AI is a tool that gives you more time back to connect with candidates — not as a substitute. Relationship building, judgement calls, and creative problem-solving are all the work of humans.
- Measure, improve, scale: Remember those goals from the first step? Track the impact to decide what to do next. Positive results = scaling to more functions and locations. No improvement? Refine your prompts or what you’re trying to do.
And it doesn’t just stop after your new hire has signed the contract. AI-enabled tools can help with onboarding and pre-employment background checks — like Zinc.
Zinc automates all the checks you need to feel confident in your new hire, and it does it in a fraction of the time compared to traditional background-checking providers. Our digital-first platform takes the stress and manual uploads out of the process, leaving you with more time to focus on candidates.
To learn more about how you can automate background checks with Zinc, speak with our team today.
