Our view on the future of AI in hiring

I spend a lot of my time talking to TA leaders about AI.

Over the past year, I've noticed three groups emerging:

1. The first isn't using AI at all, although often not by choice. Legal, IT or procurement has said no, or not yet, and nobody can quite work out how to change their mind. Meanwhile, those people are reading the same headlines as everyone else and starting to wonder if they're falling behind.
2. The second is using AI, but carefully. Very carefully. They've switched on a feature or two, but they worry about bias, about getting it wrong in front of candidates, and about what happens if a hiring decision gets challenged. So AI works around the edges, often in disparate ways, and is never trusted with anything that’s going to really impact their hiring process.
3. And finally, are the third group, who have raced ahead. These are the early adopters, the superusers building clever workflows and processes on their own. They’re Impressive to watch, but hard to emulate across entire teams.

Despite taking very different approaches, these three groups have one thing in common: very few are getting what they hoped for from AI.

I don't think that's a technology problem. I think it's a foundation problem. So I want to set out, plainly, what we believe at Pinpoint about the future of AI in hiring, and what we're doing to help shape it.

Built for how you hire today, ready for what's next

No matter which of the above groups you’re in, I don’t think any are ‘doing AI wrong’.

The team that's been told no may have real compliance and security rules to abide by. The cautious team is right to care about risk. And the superuser is proving what's possible.

But the problem is, where do they go from here? How do they evolve as AI  in recruitment technology continues to pick up pace?

That brings me to our first belief at Pinpoint: you shouldn't have to choose between software that works for you today and software that's ready for tomorrow. Your core hiring technology needs to do both.

AI should be something you adopt at your own pace. Switch it on where it helps, leave it off where it doesn't, and know your hiring still runs either way. You shouldn't have to overhaul your processes overnight just because a vendor has decided it's time.

In practice, we see that happening in three steps: bringing your hiring together, making small changes to your processes that deliver an outsized impact, and then scaling what works.

1. Bring hiring together

Most recruitment teams are working across more systems than they'd like. Their ATS, sourcing tools, interview notetakers, scheduling software, background checks, and the HRIS. Each stores part of the overall picture, making it harder than it should be to get a complete view of what's happening across the hiring process.

The result is a fragmented picture of hiring, which is problem for AI, because it needs context to be useful. When information is scattered across different systems and processes, AI can only work with the pieces it has access to. The rest has to be filled in manually, or gets missed altogether.

I've written before that your AI is only as good as your data, and about the end of the twelve-tool hiring stack. The continued pace of change in AI, and the growing number of point solutions promising to solve individual recruitment problems, only make this more urgent.

Context is more than data

There are really two kinds of context that matter.

The first is your hiring record: jobs, candidates, applications, interview feedback, all connected, so the AI understands how one relates to another.

The second is your organization's knowledge. Your hiring policies, your EVP, your tone of voice, what good looks like for a store manager versus a software engineer, and the different processes you follow for different types of roles.

For most teams, a lot of that knowledge lives in recruiters' heads, in documents, or in processes that have evolved over time but were never properly captured. Which is exactly why it walks out the door when people leave, and why AI can't reliably use it.

When AI has access to both your hiring data and your organization's knowledge, it becomes far more useful. It can understand your candidates, your processes and the way your organization hires, rather than treating every task as if it's starting from scratch.

That's why we've deliberately built (and continue to build) Pinpoint as an all-in-one recruitment platform. Not because "all-in-one" sounds good on a slide, but because context is what makes AI useful.

Stop the sprawl

This is the argument TA leaders need to make inside their own business.

When a company rolls out a new HRIS or ERP, someone usually asks: why not just use the recruitment module? Of course, it looks simpler on paper. But hiring is rarely that straightforward.

Different parts of the same business often need to hire in very different ways. High-volume recruitment isn't the same as hiring a senior executive, a confidential search needs a different process from an internal mobility programme, and what works for one team, location or type of role won't necessarily work for another.

If your recruitment technology can't accommodate those differences, people find ways around it. They’ll start to manage certain roles outside the system, create manual processes or rely on spreadsheets to handle the exceptions. Before long, the platform no longer reflects how the business hires.

The same thing happens with a traditional ATS. Even if it can handle different types of hiring, a traditional ATS is unlikely to offer everything the team needs. So sourcing happens in one tool, scheduling in another, interview notes in a third and candidate relationship management somewhere else.

Every gap gets filled with another point solution. Another contract, another login, another integration someone has to babysit. And another place where valuable hiring context gets separated from the rest.

A genuinely all-in-one ATS needs to solve both problems. It has to be flexible enough to support the different ways your business hires, and broad enough to bring the whole recruitment process together, from planning and sourcing through to the offer, before handing over to the HRIS through one clean connection.

And you don't need to switch AI on from the beginning if you don’t want to. Every hire you run in the system builds the context that makes AI easier, and safer, to adopt later.

2. Improve your process, starting small

The team that's been told no and the team using AI cautiously have something in common. They're being asked to do more with less, and need ways to make a real difference without taking unnecessary risks.

So start with the boring stuff. Flexible workflows and good old rules-based automation still do a huge amount of heavy lifting, without needing AI at all. Interview scheduling, reminders, candidate updates. Get those right and you've already saved your team hours a week.

Then bring AI in where it adds most value, without giving anyone sleepless nights. Prepping an interview brief. Summarizing feedback. Comparing who you hired against what you said you wanted, and spotting where the brief was off. Practical wins, with your team still in charge of every decision.

We see every version of this across our customers. Some run hiring for five brands on one platform, fully automated, with barely any AI at all. Others use AI for sourcing and interview briefs, freeing up time for the decisions that matter. Both are getting value, just in different ways.

3. Scale what works

The real opportunity is to take what your superusers are doing with AI and make it how the whole team works. When someone finds a great way to get something done, save it, share it, and give everyone the same quality of output without needing to write the perfect prompt.

Then let it run. A lot of modern recruiting is about nudging things along: the offer that's been waiting for approval since Thursday, the two interviews with no feedback. That work shouldn't depend on someone remembering to check. AI can keep things moving in the background, following up and flagging what needs attention, so your team can spend more time building relationships with candidates and helping hiring managers make better decisions.

In an earlier piece, I called this the shift from buttons you press to work that happens. It's now how AI works in Pinpoint.

Your process, your rules

The more AI does on its own, the more important it becomes to know exactly what it can and can't do. Who decides where AI gets used? What information can it access? How do you know what it's done? And how do you make sure people stay in control?

These are the questions TA leaders need to be able to answer, particularly when legal, IT and security teams are involved.

Here's how we approach it at Pinpoint. AI is off by default, and admins decide what gets switched on. It works inside each user's existing permissions, so it can never see or do more than the person using it. Every action gets logged against the person who started it. It works from your explicit instructions, rather than learning preferences from past hiring decisions. And your data is never used to train outside AI models.

Most importantly, people still make the hiring decisions. AI surfaces, drafts and chases. Your team decides. We've built these principles into how we develop and manage AI at Pinpoint, an approach backed by our ISO/IEC 42001 certification. I'd encourage any TA leader considering AI to ask their vendors how they manage risk, protect candidate data and ensure people remain in control of hiring decisions.

What comes next

Our mission at Pinpoint hasn't changed. We're building a true all-in-one recruitment platform, and we're getting closer to that goal faster than ever before.

This year, we've launched a range of AI tools, from our AI Notetaker and Candidate Companion to AI-powered candidate filters, checklists and match scoring. And this week, we're taking things further with the launch of Pin, our AI recruitment agent that works across your entire hiring process, alongside new Sourcing Agents that find relevant candidates beyond your applicant pool and Scoring Agents that evaluate applications against your criteria. Together, they're helping teams spend less time on repetitive work and more time with candidates and hiring managers.

Beyond that, I'd rather be honest than make promises. Where this is heading is a platform that holds your whole hiring process, knows what needs doing before you ask, and does the routine work in the background, inside the permissions and records you already trust.

What won’t change is the foundation. The agents will get smarter. The models will get better. But hiring decisions will still need clear accountability, with people in control. That's the job of the ATS, and it's a bigger job than it's ever been.

The opportunity for TA leaders is to build on what already works, bring AI in where it adds value, and scale it as they're ready. That's the future we're building towards at Pinpoint.

Want to see what this looks like in practice? Join us live on Wednesday 18 November at 4pm GMT / 11am ET / 8am PT for Built for how you hire today, ready for what's next: AI in Pinpoint.

Quick answers

What is Pinpoint's view on AI in hiring? Pinpoint believes AI in hiring works best on top of an all-in-one recruitment platform that holds the full hiring context. Teams should adopt AI at their own pace, with humans making every hiring decision.

Is AI included in every Pinpoint package? Yes. From November 2026, every Pinpoint package includes a much wider set of AI tools as standard, for new and existing customers, at no extra cost.

How should TA teams start with AI? Start small. Bring hiring into one platform, use rules-based automation for the repetitive work, then add AI for low-risk tasks like interview briefs and feedback summaries. Scale what works once the team trusts it.

Why does an all-in-one recruitment platform matter for AI? AI is only as useful as the context it can see. When your hiring record and your organization's knowledge live in one place, AI can do more, more safely, than when they're spread across separate tools.

What is Pin? Pin is Pinpoint's AI assistant, previously called Hiring Copilot. Teams can save and share how they use it, and set it to handle recurring tasks in the background.

Does Pinpoint's AI make hiring decisions? No. Pin works inside each user's permissions, follows company-wide instructions and logs every action. People make the hiring decisions.