Artificial Intelligence
Great AI Feels Like It’s Always Been There
The key to real AI results is embedding, not adding on.
Allison Joyce
VP, Global Head of Talent Acquisition
Workday
Picture a recruiter's desk. One screen for the applicant list. Another tab for scheduling. A third app for candidate messages. A sticky note for the hiring manager who wants updates by Friday. Now multiply that by 50 open jobs.
This is the real starting point for most conversations about AI in hiring. Not should we use it, but we already have six tools and none of them talk to each other.
Here's what I've learned after watching AI actually move the needle on my own team and talking with dozens of talent leaders: AI that gets results isn't the shiny new app bolted on—it lives inside the systems you use every day.
AI that gets results isn't the shiny new app bolted on—it lives inside the systems you use every day.
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The Tab Problem
When a company goes shopping for AI, it's easy to end up with a pile of point solutions. One tool for screening. Another for scheduling. A third for interview notes.
Each one might be smart on its own. But when used simultaneously, they create exactly the mess I described above—a recruiter swiveling between five workflows just to move one candidate forward.
I've sat in rooms with other talent leaders trying to solve this exact problem, each of us holding a different stack of tools, none of which play nicely together.
It’s a detrimental design flaw. An AI tool that doesn't know what your job description actually says, what the role truly needs, or how your pipeline is shaped, is guessing. It's reading a resume in a vacuum. The output might look sharp, but it's disconnected from the reality of your business.
Embedded vs. Bolted-On
Now flip that picture. What happens when AI is already woven into the core system your recruiters, hiring managers, and candidates are already using? It knows the job requisition. It sees the pipeline. It has the context it needs to solve the whole puzzle, not just one piece.
That's the shift I'd point other leaders toward. We shouldn’t be asking: Which AI tool should we buy next? Instead we should ask: Can this capability live inside the system we already trust?
When AI is embedded rather than bolted on, it materially changes the results. We've seen recruiter capacity climb around54%. Recruiters have cut their review time and regained up to 40% of their capacity, allowing them to shift more focus toward candidate sourcing and hiring manager advising.
We shouldn't be asking: 'Which AI tool should we buy next?’ Instead we should ask: 'Can this capability live inside the system we already trust?'
I’m also pleasantly surprised at how AI, built into a system that holds employee data, has gotten much better at spotting people right for internal roles. Employees are more than twice as likely to move into a new internal role once that kind of matching is available. And, typically, a big share of open roles get filled from a company's own talent pool before a single outside application is reviewed.
That's a different kind of win. Not just about hiring faster, but helping employees grow.
The Trust Part Nobody Skips
I'd be lying if I said this is only about speed. Hiring is personal, and it's high stakes for people on both sides of the process. That's exactly why embedding matters for responsible AI, too.
When AI is scattered across various vendors, governing it means different sets of rules, different security reviews, different places for something to go wrong.
When AI is built into a system that legal, security, and compliance teams already know and already monitor, it’s easier to govern. Teams can pull up the data on a regular cadence, check the outcomes, and course-correct quickly. It’s easier to trust one system than multiple unreliable ones.
And, trust is what allows you to move fast in the first place.
The companies I talk to aren't stuck because they lack ambition, but because every new AI pilot means a new vendor review, a new security questionnaire, a new integration project. Embedded AI skips that queue because the foundation of trust is already built.
It's easier to trust one system, than multiple unreliable ones.
Where I'd Start If I Were You
If you're a talent leader staring down a stack of AI tools right now, here's my honest advice: before you add another tool to the pile, ask a simpler question first: Does this live inside what we already trust or does it ask my team to open one more tab?
AI that actually shows up in the numbers and in people's day-to-day experience is rarely the flashiest new thing on the market. It's the capability quietly built into the ground your team already stands on.
Trust doesn't come from a new logo, but from AI that behaves like it already belongs. That's the version of AI that gets us back to what most of us got into this work to do in the first place: helping people find the right role and helping companies find the right people.
Report
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