The way candidates discover and evaluate employers is changing. Increasingly, they're turning to AI platforms like ChatGPT and Google AI Overviews to research companies, compare opportunities and decide where to apply. Much of that research happens before a candidate ever visits a career site, engages with a recruiter or enters an applicant tracking system (ATS).
According to Built In's 2026 candidate research, of the 88% of candidates who are using AI to discover or research employers, 69% have discovered companies they hadn't previously considered, and 64% have used AI to decide whether a company is worth applying to.
of candidates are using AI to discover or research employers
of those have discovered companies they hadn't previously considered
of those have used AI to decide whether a company is worth applying to
This creates a growing gap between what influences a candidate's decision and what talent acquisition teams can actually measure.
To understand whether their recruitment strategies are working, talent leaders need to look beyond what happens inside the ATS and develop a more complete picture of the candidate journey.
Your ATS Captures the Application, Not the Influence
Traditionally, candidates might discover an employer through a job board, visit their careers site, read employee reviews and eventually submit an application. While attribution was never perfect, these interactions created opportunities to track where candidates came from and how they engaged.
AI has made that journey less visible.
Today, a candidate can ask an AI platform which companies are hiring for a particular skill, how two employers compare or what it's like to work at a specific organization. The response surfaces information from careers sites, employee reviews, third-party platforms and other sources, giving candidates a synthesized view without requiring them to visit each source individually.
That information can influence whether someone applies, responds to recruiter outreach or considers an employer at all. But traditional recruiting systems may never capture those interactions.
As Tony Cornett, Vice President of Talent Acquisition at Dynatrace, explained during a recent conversation with Built In CEO Maria Christopoulos Katris:
The ATS does see the application. It doesn't see the influence.
Dynatrace has already seen ChatGPT emerge as a source of career-site referral traffic, providing one indication that AI is becoming part of how candidates discover the company. But even when AI influences a candidate's decision, it may not appear in the eventual application source.
A candidate could discover Dynatrace through AI, research the company further and then apply directly or respond to a recruiter's message. The ATS captures the outcome, but not necessarily what led to it.
This doesn't make traditional source-of-hire data irrelevant. It means that source of hire and source of influence are not always the same thing.
To understand what's driving recruiting performance, talent teams need additional measurement capabilities that account for what happens before a candidate enters the funnel.
A More Complete Framework for Measuring Recruiting Success
If candidate decisions are influenced before an application, recruiting measurement needs to extend earlier in the journey. That means connecting traditional funnel metrics with two additional dimensions: employer visibility and reputation.
Together, these three measurement layers provide a more comprehensive view of whether an organization is reaching the right talent, influencing consideration and ultimately attracting qualified candidates.
Visibility: Are the right candidates finding you?
Before a candidate can consider an employer, they need to discover it.
Historically, talent teams could measure awareness through career-site traffic, job impressions, search rankings and engagement across recruitment marketing channels.
While those indicators still matter, they don't capture if an employer is appearing when candidates turn to AI for recommendations.
For example, if a software engineer asks ChatGPT about the best technology companies to work for in Boston, does your organization appear in the response? What about candidates researching employers that offer career growth, remote flexibility or opportunities in a particular industry?
Measuring AI visibility helps talent teams know if they're being surfaced in the conversations most relevant to their hiring priorities.
At Dynatrace, this has become part of how the team evaluates its employer visibility. Using Built In, the company measures how frequently it appears across priority AI prompts aligned with its talent strategy. This provides a baseline for understanding where the organization is visible and where opportunities to improve may exist.
Importantly, the goal isn't to appear in every AI search, it's to appear when the candidates an organization wants to hire are looking for employers.
As Cornett emphasized,
Being in more places isn't the goal.
For talent teams, the starting point should be their hiring priorities: which roles, markets and talent audiences matter most? From there, they can evaluate whether those candidates are likely to encounter their organization during research and discovery.
Get Your Free AI Visibility AuditReputation: What are candidates learning about you?
Being visible is only the first step. What candidates learn about an employer can be just as important as whether they find it.
AI platforms don't simply surface a company's careers site or employer brand messaging. They synthesize information from multiple sources, including employee reviews, company profiles, articles and other third-party content.
That means candidates may encounter information about an organization that they wouldn't have found through traditional research.
For talent acquisition teams, the challenge is understanding how their company is being represented and if that representation aligns with the experience they're trying to communicate.
Are candidates seeing accurate information about career development opportunities? Are outdated perceptions influencing how AI describes the company? Does the company appear credible and compelling compared to competitors?
Dynatrace, for example, uses Built In to measure its AI reputation alongside visibility. The company has an 83% AI Reputation Score, providing an additional perspective on how it's represented across its tracked AI research.
The organization also measures Narrative Control, which evaluates how much of the information AI cites comes from trusted, accurate sources like, its careers site and Built In content.
These measurements help illustrate an important distinction: employers can't control every source informing AI-generated answers, but they can better understand what's shaping their reputation and identify opportunities to improve the accuracy, relevance and credibility of available information.
For talent leaders, reputation measurement goes beyond branding by providing insight into why candidates may discover an employer but choose not to take the next step.
Recruiting Outcomes: Are You Attracting the Right Talent?
Visibility and reputation provide insight into what happens before a candidate applies. Traditional recruiting metrics help evaluate what happens afterward. But even within the ATS, talent leaders may need to rethink which metrics deserve the most attention.
Application volume, for example, is becoming a less reliable indicator of recruiting success on its own.
AI has made it easier for candidates to find opportunities, tailor resumes and submit applications. As a result, attracting more applicants doesn't translate to attracting qualified talent.
At Dynatrace, applications increased 60% year over year, while the number of candidates progressing to second-round interviews increased 130% over the same period.
That distinction is meaningful. While application growth reflects increased volume, progression into later interview stages provides an additional signal of candidate quality and alignment with hiring needs.
As Cornett put it,
Volume alone can be noise.
Rather than focusing exclusively on application counts or time to fill, talent leaders should evaluate indicators such as:
- Application-to-interview conversion rates
- Candidate progression through interview stages
- Interview-to-offer conversion rates
- Offer acceptance rates
- Time spent at each stage of the hiring process
- Quality of hire and longer-term employee outcomes
These metrics help determine if the recruiting process is identifying and converting the talent the business actually needs.
The important next step is examining these outcomes alongside earlier visibility and reputation data.
More Data Is Only Valuable If It Helps You Know Where to Act
Expanding recruitment measurement isn't about creating another dashboard or adding metrics for the sake of reporting. The real opportunity is using a more complete view of the candidate journey to understand where performance may be breaking down.
Consider a company struggling to attract qualified applicants for engineering roles in a new market. Traditionally, the team might respond by increasing job advertising spend or expanding sourcing efforts.
But what if the underlying issue is visibility? If the organization rarely appears when candidates research relevant employers through AI, additional job postings alone may not solve the problem.
Alternatively, an employer might have strong visibility and plenty of inbound interest but struggle to convert candidates into applicants or accepted offers. In that case, understanding how candidates perceive the organization could reveal potential reputation challenges worth investigating.
- Signal
- Struggling to attract qualified applicants for engineering roles in a new market
- Question
- Does the organization appear when candidates research relevant employers through AI?
- Signal
- Strong visibility and plenty of inbound interest, but struggling to convert candidates into applicants or accepted offers
- Question
- How do candidates perceive the organization?
These signals don't establish cause and effect on their own. They help teams ask better questions, identify potential gaps and make more informed decisions about where to invest their resources.
The key is to start with specific hiring priorities rather than attempting to optimize every channel, market and audience simultaneously.
For Cornett, that means understanding who Dynatrace needs to hire, where those candidates are and what they're learning about the organization before an open requisition becomes an immediate hiring need.
It's a more proactive approach that brings employer visibility, reputation and recruiting performance into the same conversation.
From Recruiting Metrics to Talent Intelligence
As the candidate journey evolves, so does the role of talent acquisition.
TA leaders are expected to provide the business with more than application numbers and hiring outcomes. They need to understand the talent market, identify competitive challenges and make strategic recommendations about how to attract the people their organizations need.
For Cornett, this represents a broader evolution in the function.
At Dynatrace, talent intelligence is becoming an integral part of recruiting, supported by AI fluency, a modern technology stack and a more proactive approach to building relationships with talent.
Are the right candidates finding you?
What are candidates learning about you?
Are you attracting the right talent?
Visibility and reputation data can contribute to that shift by giving teams a better understanding of how their organization is positioned before candidates enter the recruiting process.
When talent leaders can measure whether the right candidates are finding their organization, understand what those candidates are learning and evaluate how they progress through the hiring process, they have a complete foundation for making strategic decisions.
The ATS will continue to play an essential role in measuring recruiting performance. But in an AI-first candidate journey, it can no longer be the only source of truth.
The future of recruitment measurement is about understanding not just who applied or who was hired, but what influenced those outcomes along the way.