AI in Recruiting: A Practical Starting Guide

In the peer group discussions Talivity hosts with talent leaders across industries, AI comes up almost every week. The examples vary, but the pattern is familiar.

One team is trying to get legal comfortable with AI-assisted screening. A healthcare leader described recruiters carrying nearly 100 open requisitions each and looking for ways to operate effectively at that scale. Another organization is dealing with a surge of suspicious AI-generated applications and trying to bring identity verification earlier into the process.

Some teams have already started experimenting and cannot get traction. Others feel pressure from leadership to "do something with AI" but do not know where to begin.

Eventually the conversation lands in the same place: We know we should be doing more with AI, but we cannot figure out how to get momentum.

That question is usually followed by another: Which AI tools should we evaluate?

Our view is that this is the wrong starting point.

None of this tension is surprising. According to SHRM research, 89% of CEOs expect AI to redefine how their organizations create and capture value in the coming years. Yet translating that ambition into real operational impact remains difficult. Boston Consulting Group reported in a 2025 study of more than 1,250 companies that only 5% were achieving AI value at scale, while 60% reported little or no material value despite substantial investment.

HR leaders sit directly inside that gap. AI adoption is expanding rapidly. SHRM also reports that 45% of U.S. workers already use AI in their jobs. But execution remains uneven: 82% of HR professionals say their organization's AI efforts are only moderately successful or not successful.

The pressure to adopt AI is real. Experimentation is happening, but measurable impact remains rare.

So we want to offer a practical guide. A starting framework for HR and talent leaders trying to answer a straightforward question:

How can we leverage AI in hiring better than we are today?

This is a five-step framework: clarify the workflow, identify AI use cases inside it, prioritize those use cases through reward and risk, assess your organization's readiness, and run a focused pilot.

Step 1: Clarify the workflow

The first step is to look at how hiring work actually gets done today, before scheduling vendor demos or seeking out the most compelling sales pitch.

Like most meaningful change, it begins by looking inward; by understanding how the hiring work is actually being done today, and examining the signals that reveal where the process is breaking down.

Start by defining the hiring workflow.

A simplified version may look something like this:

Job approval → Intake and role definition → Go-to-market strategy → Candidate sourcing and attraction → Candidate interest and application → Screening → Interview and evaluation → Offer → Hire → Pre-employment and onboarding

That is an oversimplification for illustrative purposes. The devil is most certainly in the details.

When you map the workflow, capture it from at least three perspectives: the hiring manager, the recruiter, and the candidate. We recommend conducting a workshop or structured interviews with internal team members to capture the hiring manager and recruiter perspectives. For candidates, navigating your own career site and applying to a few jobs can be a surprisingly illuminating experience.

At each stage, document the people involved, the technologies and systems being used, the decision points, and the handoffs. Write down what is really happening, regardless of what the process map says is supposed to happen.

Most organizations are surprised by what they find. Manual steps. Shadow systems. Personal spreadsheets. Email threads that quietly function as operating systems. Workarounds that have been in place so long that people stopped noticing them.

If you are a larger organization, you almost certainly have more than one workflow. High-volume hourly hiring will look different from technical hiring. Technical hiring will look different from early career recruiting. Clinical versus non-clinical. Corporate compared to field operations. You can start with one high-value workflow, but make note of the differences across segments. This will come up later when you start thinking about use cases and readiness.

One way to think about the hiring system is as a series of handoffs between people, decisions, and information. Every stage introduces new inputs, judgments, and delays. When organizations struggle with hiring performance, the root cause is rarely a single broken step. It is usually friction accumulating across many small transitions.

AI has the potential to reduce some of that friction, but only if the underlying workflow is visible and well understood.

Once the workflow is defined, the next step is to look at the data. Most organizations already have enough reporting to start spotting signals if they know what to look for.

Five types of metrics are especially useful:

• Time - How long are candidates spending in each stage? What is the time to offer? Time to fill? Where do things stall?

• Cost - What is the cost per applicant? Cost per hire? What tools are involved? How much recruiter time is being consumed by manual coordination that could be automated or redesigned?

• Volume - How many requisitions is each recruiter carrying? How many applicants are coming in per job? Where are the bottlenecks, surges, and capacity constraints?

• Quality - What percentage of candidates presented are accepted into the next stage by hiring managers? What is the offer acceptance rate? What does early turnover look like in the first 90 days?

• Sentiment - What are candidates, recruiters, and hiring managers saying about the process? What do surveys, escalations, and anecdotal feedback tell you?

These signals reveal where time is being lost, where money is being wasted, where volume breaks the process, where quality is inconsistent, and where the experience is deteriorating.

That work alone often produces an uncomfortable but useful realization. The highest-leverage move is not always new technology. Sometimes it is simplifying a bloated process, tightening a handoff, cleaning up a broken field structure, or integrating systems that currently force people into manual workarounds.

AI can process enormous amounts of work quickly. That is why workflow definition and design matter so much. If the process is broken, automation will scale the problem rather than solve it.

Once you understand the workflow and the data signals behind it, the next question becomes much clearer: where inside the hiring process could AI actually help?

Step 2: Identify AI use cases inside the workflow

Instead of starting with vendor capabilities, start with a simpler question at each stage of the hiring process: Where is work being done manually today that could be assisted, accelerated, or structured with AI?

The examples below follow the hiring workflow from job definition through onboarding. They are not meant to be an exhaustive list. The goal is to illustrate how AI opportunities tend to appear inside the workflow itself, rather than as isolated tools layered on top of it.

Role definition and job creation

At the front of the process, AI can support the work of defining the job itself. Poorly defined roles create downstream noise throughout the hiring process, which makes this an especially high-value area for AI assistance.

Examples include:

• Normalizing job titles and taxonomy

• Inferring likely skills or competencies for a role

• Rewriting or optimizing job descriptions

• Analyzing labor market supply, competitor analysis, and compensation benchmarks

• Transcribing and summarizing hiring manager intake meetings into structured hiring criteria or scorecards

Because these activities occur upstream in the hiring workflow, improvements here often reduce problems later in advertising, sourcing, screening, and interviewing.

Go-to-market strategy and attraction

Once the role is defined, the next challenge is determining how the job reaches potential candidates. AI can support the strategy and execution of how roles are promoted and discovered across job boards, social channels, and emerging AI-driven search environments.

Examples include:

• Generating promotional content for social media or recruitment marketing campaigns

• Identifying high-performing job boards or advertising channels

• Optimizing job titles and descriptions for search visibility

• Recommending or adjusting programmatic advertising spend

• Answering candidate questions and gathering application information through chatbots

There is also a shift happening on the candidate side. Candidates increasingly use generative AI tools to research employers, compare opportunities, and tailor applications. Organizations that understand how their jobs appear in AI-driven search environments stand to gain an advantage.

Sourcing and outreach

On the sourcing side, AI can help recruiters identify potential candidates and engage them more efficiently. The goal is to convert qualified people into interested applicants without requiring recruiters to manually execute every outreach step.

Examples include:

• Identifying potential candidates based on skills, profiles, or career history

• Drafting outreach messages or recruiter emails

• Sequencing automated follow-up messages

• Personalizing outreach based on candidate data or signals

• Triggering engagement workflows based on candidate responses

Used well, these tools allow recruiters to focus more time on relationship building and evaluation rather than manual prospecting.

Screening, matching, and movement through the funnel

Once a person becomes an applicant, AI can support early-stage screening and candidate movement through the hiring workflow. This stage typically involves gathering information, answering questions, and determining whether a candidate should advance.

Examples include:

• Resume or profile matching against job requirements

• Candidate ranking or shortlisting based on defined criteria

• Automated follow-up questions to gather missing information

• Chat-based candidate Q&A during the application process

• Advancing candidates through early workflow stages based on predefined conditions

This is also where the risk profile begins to change. The closer AI systems get to assembling information about a person and influencing whether that person advances, the more scrutiny they deserve.

AI-assisted interviewing

Structured voice or video agents can run initial screening conversations and capture candidate responses in a consistent format. These tools are designed to standardize early interviews and reduce the manual effort required from recruiters.

Examples include:

• AI-led structured screening interviews

• Automated interview transcription

• Summaries of candidate responses for hiring teams

• Structured documentation for interview scorecards

• Detection of suspicious interview behavior or potential AI cheating

One reason this category is gaining traction is the ability to produce more consistent documentation and structured evaluation. In some cases, it is even replacing the need for phone screens and certain types of assessments.

Candidate trust remains a challenge. Gartner reported that only 26% of job applicants trust AI to evaluate them fairly, even though 52% believe AI already screens their application information. Organizations need to be transparent about how these tools are used to avoid damaging candidate trust or employer brand.

Interview coordination and candidate evaluation

After an initial screen, whether conducted by a recruiter or an AI system, candidates typically move into human-led evaluation stages involving hiring managers or team members. This stage focuses on coordinating interviews, capturing feedback, administering assessments, and synthesizing information that supports a hiring decision.

AI can reduce the administrative burden of interview coordination while helping hiring teams capture and interpret evaluation signals more consistently.

Examples include:

• Automated interview scheduling across recruiter, hiring manager, and candidate calendars

• Coordination and management of interview loops or panels

• Transcription and structured documentation of interview conversations

• Administration and monitoring of skills, work-sample, or personality assessments

• Synthesizing interview feedback and assessment results into candidate summaries

Because these tools aggregate candidate information and may influence hiring decisions, governance and oversight become increasingly important.

Offer, pre-employment, and onboarding

Once a hiring decision is made, another operational sequence begins. This stage involves offer creation, approval routing, candidate communication, and pre-employment administrative steps.

AI can help streamline these tasks and reduce manual coordination across HR, recruiting, and payroll systems.

Examples include:

• Drafting or generating offer letters

• Routing offer approvals through stakeholders

• Triggering background checks or identity verification

• Collecting payroll and tax documentation

• Distributing onboarding materials or employee handbooks

• Answering standard new hire questions

These administrative workflows often span multiple systems. Automation and AI can reduce friction and help ensure candidates move smoothly from accepted offer to productive employee.

When organizations map the workflow carefully, they usually discover that AI opportunities appear at nearly every stage of the hiring process. The next challenge is deciding which ones are worth pursuing first.

Step 3: Prioritize using reward and risk

A useful way to think about prioritization is by evaluating each use case across two dimensions: reward and risk.

Reward reflects the measurable impact identified earlier in the workflow analysis. Can the use case meaningfully improve time, cost, volume, quality, or experience within the hiring process?

Risk reflects how directly the use case interacts with candidate information or influences hiring decisions.

Looking at both together helps determine where experimentation makes sense.

Reward is easiest to estimate using the workflow signals identified earlier. Even rough calculations can help determine whether a problem is large enough to matter.

For example:

• Recruiter time: If recruiters spend several hours each week coordinating interviews, multiply that time across the recruiting team and across a year.

• Workflow delays: If a step in the process consistently adds several days to time-to-fill, estimate how many roles are affected annually.

• Interview waste: If poor sourcing or screening quality produces unnecessary interviews, consider the recruiter and hiring manager time tied up in those cycles.

Precision is less important than developing a practical sense of scale. When a workflow problem touches hundreds of requisitions, thousands of applicants, or dozens of recruiters, even modest improvements can produce meaningful operational impact.

Risk varies more widely. It depends on industry, geography, organizational risk tolerance, and how directly the use case influences employment decisions.

A helpful heuristic: the closer the use case gets to personally identifiable information and decisions about whether a candidate advances, the more governance it requires.

For example:

• Workflow support: AI used to refine job descriptions, analyze labor market data, or draft recruiter communications generally presents lower risk because it does not directly influence hiring decisions.

• Candidate interaction: Tools that answer candidate questions, schedule interviews, or automate communication introduce moderate risk because they interact directly with applicants and shape the candidate experience.

• Candidate evaluation: Systems that rank, score, or screen candidates carry higher risk because they may influence who advances in the hiring process.

Higher-risk use cases are not off limits. However, they do require stronger governance, clearer controls, more documentation, and broader internal collaboration.

The regulatory environment is moving in that direction. New York City's AEDT requirements impose bias-audit and notice obligations for certain automated employment decision tools. California regulators have clarified how existing anti-discrimination law applies to automated systems in employment. The EEOC has repeatedly signaled that employers remain accountable for outcomes produced by these tools. In the EU, the AI Act establishes a risk-based framework for higher-risk uses, and litigation is beginning to test these boundaries as well.

The goal is to prioritize thoughtfully, not to avoid AI entirely. Many organizations find that the easiest starting points sit in the upper-left portion of the grid: meaningful operational impact with relatively low decision risk.

The rise of agentic AI in recruiting

One emerging development is worth calling out in the context of prioritization: agentic AI.

Many of the tools discussed earlier help with individual tasks: summarizing resumes, drafting outreach, or answering candidate questions. Agentic systems go a step further. They are designed to coordinate multiple steps within a workflow.

Capabilities in this area are advancing quickly. AI has moved from operating quietly behind the scenes in software, to tools that support and augment human work, to systems that can increasingly execute multi-step processes with limited supervision.

In recruiting, that could mean a system that interprets a job opening, identifies potential candidates, initiates outreach, coordinates interviews, conducts an initial structured screen, summarizes the interaction, and moves candidates through the workflow.

Seen through the reward-and-risk lens, this is clearly a high-reward, high-risk category. These systems have the potential to change how recruiting work is executed, but they also raise new questions about accountability, transparency, and control.

Research suggests this shift is coming quickly. Boston Consulting Group reported that AI agents accounted for about 17% of total enterprise AI value in 2025, and are expected to reach 29% by 2028.

If agentic systems become more common in hiring, the nature of operating the recruiting function changes. The challenge becomes less about executing individual tasks and more about orchestrating work across humans and intelligent agents, while ensuring that accountability for hiring decisions remains with people.

This is another reason workflow design matters so much. When the underlying process is poorly designed, adding agents does not create intelligence. It produces faster chaos.

This is where operational readiness becomes critical.

Step 4: Assess operational readiness

Once the workflow is defined, the use cases are identified, and risk and reward have been considered, the next step is operational readiness.

There are five conditions we would focus on.

1. Alignment

Have the relevant stakeholders agreed on the goal, the problem to be solved, and the metrics that will define success?

If there is no agreement on what you are trying to improve, AI will not fix that.

2. Process clarity

Is the workflow actually clear and consistently followed?

If the process is fuzzy, inconsistently executed, or built on undocumented workarounds, AI will magnify those inconsistencies.

3. Data foundation

AI relies on context, clean data, and structure. If your internal reporting is not trusted, if fields are used inconsistently, if definitions vary across teams, or if key context is missing, that should give you pause.

Sophisticated skills matching, internal mobility, and workforce planning use cases do not work well if the underlying data is not defined and stored cleanly inside the operating systems.

4. Workflow orchestration

This is different from process clarity. A process can be clearly defined on paper and still be badly orchestrated in practice. Workflow orchestration is about how systems connect and whether the work moves coherently between them.

If the process jumps across multiple disconnected systems with constant manual intervention, integration, automation, or tool consolidation may need to happen before AI adds real value.

This point matters even more as agentic systems become more prevalent.

5. Governance

Governance is the final readiness condition, and it overlaps with the earlier risk discussion.

The rule of thumb is: if AI influences an employment decision, a human needs to remain accountable for the outcome. That is where governance, controls, anti-bias measures, oversight, and documentation come in.

The company is still responsible, whether the technology is built internally or provided by a third party.

Organizations that skip these conditions often find themselves experimenting with AI tools without seeing measurable operational improvement.

Step 5: Start with a focused pilot

Once you have mapped the workflow, identified use cases, evaluated risk and reward, and assessed readiness, then you are finally in a position to run a pilot.

Start small.

Pick one workflow. Maybe one segment of one workflow. Focus on a problem you know is worth solving and where the upside is meaningful. Do not try to boil the ocean.

The pilot is where vendor evaluation becomes useful. At that point, you can look at native capabilities inside your ATS or CRM, integrated partners, narrower point solutions, and broader platforms.

But even here, the buying path matters.

Our bias is to start by looking at what is native or already adjacent to the systems you own. Then look at integrated partners. If a large platform promises full transformation from day one, recognize what usually comes with that: a six-figure or seven-figure investment, long implementation timelines, and a slower path to return.

That is a risky starting point.

A better discipline is to require a proof of concept around a specific problem first. If the provider can solve that problem in a measurable way, you can expand from there.

As your readiness and internal capacity grow, so can the use cases. Hopefully, your current systems will evolve with you. If not, that learning may eventually point toward a larger platform change. But that is a much better decision to make after you have proven what works than before.

Closing thought

If you are struggling to gain traction with AI in hiring, you are not alone.

There is a tremendous amount of noise in the market. Buzzwords everywhere. No shortage of vendors willing to promise the world.

The answers, though, do not come from "out there."

Look inward. Clarify the workflow. Identify the highest-leverage use cases inside it. Evaluate those use cases through the lens of risk and reward. Assess readiness across alignment, process clarity, data foundation, workflow orchestration, and governance. Start with a focused pilot. Learn. Expand from there.

AI becomes useful in hiring the same way any operational improvement does: by improving how the work actually gets done. The most impressive demo rarely produces the most meaningful outcome.