Your next-level AI in recruiting playbook
Key takeaways:
- The "walk" stage transforms AI from a simple productivity tool into a strategic insight generator that detects patterns in recruiting data you'd otherwise miss.
- Moving from generic AI tools to recruiting-specific solutions unlocks context-rich understanding that makes AI 10x more useful.
- Successful team-wide AI adoption needs standardized processes, shared resources like prompt libraries, and leadership buy-in.
If to “crawl” in AI adoption is to automate routine tasks, then to “walk,” says Metaview co-founder Siadhal Magos, is to move beyond simple efficiencies into insight detection. “Step one really is about finding any obvious automations,” he explains. “And then step two is about detection. Now that you have this data from automated processes, can you start to detect things that you otherwise would have missed?”
In the “walk” stage, AI shifts from a time-saving assistant to a force multiplier — generating insights, enhancing collaboration, and improving hiring outcomes. Conversations with Siadhal, Nitin Moorjani (Senior Director of Talent Operations at Automattic), and Miro’s Nick Krekis (AI Program Manager) and Rico Habraken (Talent Operations Manager) paint a clear picture of what it takes to advance from AI experimentation to higher-leverage implementation.
From generic to specialized tools
While general tools like Microsoft Copilot, Zoom AI companion, or Google Gemini can be a good great entry point for AI beginners, they have inherent limitations when it comes to recruiting workflows.
The fundamental difference is that generic AI tools aren't built with recruitment-specific data models or workflow understanding in mind. Siadhal points out that although hiring teams would love for these companies to be thinking about how to enable recruiters, that’s not the reality. “They’re not thinking about that,” he says. “They’re thinking about broad use cases.”
Recruiting-specific AI tools understand the nuances of hiring in ways general tools can't, including:
- Context awareness: Understanding the difference between different types of interviews and taking into account other relevant data sources (scorecards, resumes, job posts, etc)
- Recruitment fluency: Recognizing industry-specific terms and concepts
- Workflow integration: Connecting with your ATS and other recruiting tools
- Specialized data analysis: Processing conversational recruiting data to extract relevant insights
Nitin has seen the difference firsthand: "We use Metaview for our initial intake calls, which is when we're talking to a hiring team about the requirements. We immediately share those with the hiring team, and that sometimes gets shared with a business unit leader for posterity. That's increased collaboration."
Turning data into insights
The cornerstone of the “walk” stage is using AI to detect patterns in your recruiting data that would otherwise go unnoticed.
"If five years ago you had started to record every single interview in your company, you'd have found occasional cases where that's useful, maybe because you knew a particular moment you wanted to replay," Siadhal explains. "But across thousands of hours of conversations, you'd never be able to say, 'bring me something interesting out of it.’”
This is where specialized AI tools really shine by helping you do things like:
- Generate proprietary compensation insights: "Based on all the conversational data you receive, you can get proprietary compensation banding data," Siadhal notes. "Which is something that recruiting or HR teams will spend a lot of money on from third parties, never really leveraging all of the compensation data they're receiving in-house every single day."
- Create better job descriptions: "You can use data from conversations to create higher quality job descriptions because you can actually get a feel for what the hiring manager is really looking for," says Siadhal.
- Identify interviewer training needs: "We can now say 'Hey, hiring manager, you need to be trained on interviewing because what you're asking about doesn't match up to what you told us you're looking for'" Siadhal says.
Beyond these insights, AI is also helping teams fine-tune their candidate communication. Nitin's team uses AI to refine messaging strategies, particularly for outreach and engagement. "If we're about to launch an email campaign, we'll review AI-suggested messaging and adjust it together with hiring teams. AI has enabled additional collaboration and allowed us to do things faster."
Creating systems and standards
Scaling AI across a team requires consistency. The key, Nick explains, is recognizing patterns. “If you keep going back to ChatGPT, asking it for the same thing again and again, that for me is your little moment to go, ‘Okay, we could probably be doing things differently.’”
Rico describes how Miro has approached this: “When you ‘walk,’ you start to scale the power of AI beyond your own personal usage. I look at the broader team that’s doing the same type of work as I am, and I think ‘maybe there’s a prompt library or other resource I can build that people can leverage outside of my own team’.”
Practical examples of standardizing AI processes include:
- Template libraries: Creating standardized prompts for common recruiting tasks
- AI workflows: Mapping out step-by-step processes that incorporate AI
- Feedback mechanisms: Systems to refine and improve AI outputs
- Knowledge sharing: Documenting successful AI applications for team learning
AI as a strategic partner
One of the most powerful transitions in the “walk” stage is moving beyond basic task automation and using AI as a deeper problem-solving tool.
Nick describes his realization after taking a prompt engineering course. He said an “aha moment” happened for him when he began “understanding how to actually start writing prompts at a more advanced level, how to give things roles, how to really start to get the most out of it and basically talk to the AI.”
Rico points out that this shift represents a fundamental advancement: "Taking AI use to the next level means looking at AI as a strategist or thought partner, rather than just your assistant where it's just asking you in a different way to use a Google search."
Team adoption strategies
Moving from individual AI usage to team-wide adoption is a critical aspect of the “walk” stage. Our experts shared several strategies for successfully scaling AI across recruiting teams:
Leadership modeling
AI adoption isn’t just about introducing tools — it’s about making AI part of daily leadership workflows. "We constantly talk about ways in which we use it, and we try to model that behavior so other people can be inspired to follow," Nitin explains. By integrating AI into their own processes and sharing successes, leaders create a culture where AI feels less optional and more essential.
Creating shared resources
Rico emphasizes the power of collective knowledge: “If you look at candidate feedback or outreach messaging across a recruitment team, these are examples of some of the things that you can easily create templates for.” Miro’s prompt library, for example, helps standardize AI usage across teams so consistency and efficiency remain at the forefront of their hiring workflows.
Practical application development
At Miro, AI-powered tools streamline recruiting tasks. Nick built an outreach and feedback generator. “Recruiters can use it as an app,” Rico explains. “You upload the public LinkedIn profile or the PDF, it has a simple prompt, and based on that, creates a two- or three-step sequence that you can send out to the candidate.” These tools cut down repetitive work and improve recruiter efficiency.
Creating a culture of AI adoption
At Automattic, AI adoption is embedded in company culture. "We recently looked at some numbers: 87% of our stakeholder group is using AI. That's a pretty high adoption rate," Nitin says. He attributes this success to leadership buy-in: “Our leaders also use it, and having that culture really helps.”
Start walking today
The difference between staying relevant and falling behind is how quickly you move from experimenting with AI to embedding it strategically in your recruiting process. Here are five practical steps to get started:
- Get specific: Move from generic to recruiting-specific AI solutions.
- Put it on repeat: Identify frequent, tedious tasks that could benefit from AI templates and standardization.
- Share the knowledge: Document effective prompts and AI workflows and scale them across your team.
- Turn conversations into insights: Use AI to analyze conversational data into actionable insights that actually improve your process.
- Model from the top: Make sure leadership is demonstrating their own AI usage and conviction.
And when you’re ready to transition from “crawl” to “walk,” try Metaview for free and discover how specialized AI can transform your hiring processes.
