Guide

Since we started our journey building Carv we've seen firsthand the challenges you face.

Growing workloads, shrinking teams – the pressure to find top talent is real, and current tech solutions often feel like adding another layer of complexity to an already ineffective way of working.

With all the hype around gen AI, you know artificial intelligence has the potential to change the way you find and hire talent - but where do you even begin?

The sheer number of potential AI recruitment tools and gen AI use cases and the lack of a holistic approach can lead to confusion and inaction.

Many teams already manage complex software ecosystems and struggle to see how AI can fit into their existing processes and workflows.

Should they use AI for sourcing and screening candidates? Or is it better to not delegate these tasks to AI assistants? Should they automate only the process steps that don’t require real-time contact with the candidates, like writing job descriptions and interview follow-ups?

AI recruiting software can do so many things that integrating such tools into existing workflows can feel like a daunting additional layer, requiring significant time and resources for configuration and troubleshooting.

Thus, the complexity of adding new AI-powered tools discourages implementation.

Although there’s not a one-size-fits-all when it comes to implementing gen AI in a staffing or recruitment agency, the first blocker one must overcome for successful implementation is defining a clear future state, and the path to get there from their current reality.

This is why we’ve put together this checklist. This document is meant to guide you through the practical aspects you need to consider when building your roadmap for AI-powered recruitment.

The checklist covers four main domains of implementation:

• Current state assessment
• Organisational readiness
• Implementation planning
• Risk assessment and mitigation

By focusing on these core areas, you can ensure that AI implementation has a real impact on your recruitment process, streamlining workflows and improving efficiency.

Our end goal is to help recruitment teams integrate AI into their hiring process without feeling overwhelmed.

If you’re unsure about a certain step or want to discuss our implementation approach in more detail, feel free to contact our team here.

Let's dive in!

Part 1: Current state assessment

The first step to successful AI implementation is gaining a clear picture of your current recruitment process, including people involved, tools used, and data architecture.

By analyzing your current process, you can identify bottlenecks and inefficiencies that AI can address, as well as benchmark your progress post implementation by tracking improvements inefficiency, cost-effectiveness, and candidate experience.

This section, Current state assessment, will help you evaluate your existing workflows and pinpoint areas where AI can offer the most significant impact.

💡 Note: Mapping out your process - If you’re not sure how to map out your existing recruitment process, check out this article: AI-Driven Recruitment - The Before and After States of the Hiring Process.

1.1 Recruitment process

• We have a documented recruitment process flow that outlines all steps, people involved, tools used, and data architecture.
• We have a standardized process for job posting and advertising positions across multiple channels.
• We have standardized document formats for job descriptions, candidate profiles, and candidate write-ups.
• We have a clear process for candidate screening and selection, including standardized interview questions and evaluation criteria.
• We regularly evaluate the efficiency and effectiveness of our current recruitment methods (e.g., time-to-fill, cost-per-hire).
• We track and measure key recruitment metrics (e.g., number of applicants per position, offer acceptance rate).
• We have identified areas in our recruitment process that could benefit from improvement(e.g., screening effectiveness, candidate experience).
• We have the flexibility to redesign processes around AI capabilities.
• Our recruitment process is clearly communicated to all hiring managers and recruiters.

1.2 Data & technology infrastructure

• We keep track of our recruitment data, it is organised and accessible.
• Our Applicant Tracking System (ATS) is regularly used and integrates well with our other HR systems.
• We have a process for regularly cleaning and updating our recruitment data.
• We have a robust HR technology stack with capabilities for integration with other tools (e.g.APIs).
• Our HR technology stack is secure and can meet data privacy compliance standards.
• We have a clear policy and procedures for data governance, retention, and disposal.

💡 Note: AI solution rollout - At Carv, we believe a staged roll-out where AI gradually takes over tasks is the way togo, as this mitigates the risk for “new tech overload”. We’ve detailed the topic in this article: Implementing AI in Recruitment - A Framework to Get You Started.

Part 2: Organisational readiness

A culture receptive to new technologies is crucial for successful AI integration. Assessing your team's sentiment helps identify potential roadblocks and develop strategies to foster a positive adoption environment.

Part 2, Organisational readiness, delves into your agency's preparedness to embrace AI, and ensures your AI recruitment strategy aligns with your broader talent acquisition plans.

Among others, you’ll assess your team's openness to change, technological expertise, and overall alignment with AI implementation.

💡 Note: Unsure about the ROI of AI implementation? If you need some help building the case for AI implementation, you can use this guide: Building the ROI Case for AI in Recruitment.

2.1 Strategic alignment

• We have clearly defined goals for using AI in recruitment (e.g., improve diversity, increase efficiency, enhance quality of hire).
• We are aware of the shortcomings of regularautomation technology, and of the benefits ofAI technology.
• This is a strategic initiative for our team. AI implementation aligns with our overall talent acquisition strategy.
• Leadership demonstrates strong buy-in and support for adopting AI in recruitment.
• We have a plan to communicate the benefits and limitations of AI to all stakeholders(recruiters, hiring managers, candidates).
• We have a dedicated budget for AI implementation, and have considered ongoing maintenance and upgrade costs.
• We have strategies in place to address potential concerns surrounding privacy bias in AI recruitment tools.

2.2 Internal capabilities

• Our team possesses a basic understanding of AI technology and we have in-house capabilities for a successful implementation project.
• We have identified key team members who will lead the AI implementation and ongoing management.
• We will consider partnerships with AI vendors or consultants to provide additional expertise and support.
• We have considered the potential impact of AI adoption on existing recruitment roles.
• We are aware that additional training on effectively using AI for recruitment tasks might be needed for recruiters and hiring managers.

💡 Resources - Future of AI in Recruitment

‍If you want to get familiar with the main concepts related to AI recruitment, take a look at these resources:

• Autonomous Recruitment: What Is It and How Do We Get There?
• AI-Enabled Recruiter vs. AI Recruiter: Key Differences
• How AI is Reshaping Recruitment and Why the Time to Act is Now

Part 3: Implementation planning

A well-defined plan ensures a smooth transition, helps mitigate potential risks associated with AI implementation, and maximizes the benefits of AI.

Part 3, Implementation planning, focuses on developing a clear roadmap for AI integration. This involves selecting the right AI solution, assembling a capable team, and outlining a detailed project plan.

💡 How can you use AI in recruitment? If you’re not sure how to use AI in your recruitment workflows, take a look at this article: 6 Top Use Cases of Gen AI in Recruitment.

3.1 Project plan

• We have dedicated resources (both human and financial) allocated for the implementation, including a project sponsor and project leader.
• There is a cross-functional team (HR, IT, legal)in place to oversee the AI implementation process.
• We have a tentative project timeline for AI implementation, with milestones and deliverables clearly defined.
• Our AI implementation plan includes pilot testing phases to evaluate effectiveness before full-scale deployment.
• We have a set of KPIs to measure the success of the AI tool implementation.
• We have a feedback loop for collecting feedback from recruiters and candidates.
• We have defined a post-implementation support structure, including maintenance and updates.

3.2 Vendor selection

• We have identified the specific AI functionalities we will be using initially (e.g.,candidate screening, interview scheduling).
• We have researched and evaluated different AI recruitment solutions based on our specific needs and goals.
• We have developed selection criteria (functionality, scalability, integrations) to shortlist potential AI vendors.
• We have considered the vendor's track record, customer support, and data security practices.
• We have a clear understanding of the vendor's pricing model and licensing terms.
• We have negotiated a clear contract with the chosen vendor, including terms of service, SLAs, and pricing.

Part 4: Risk assessment & mitigation

Mitigating bias and ensuring responsible AI practices, as well as adhering to privacy regulations and compliance are paramount for an ethical and legal recruitment process.

Part 4, Risk assessment and mitigation, focuses on identifying and managing potential risks associated with AI implementation in recruitment.

4.1 Risk detection and prevention

• We have a risk management plan in place.
• We have identified potential sources of bias in our recruitment data and AI algorithms.
• We have developed strategies to mitigate bias (e.g., using diverse training data, implementing human oversight).
• We have a process for monitoring and addressing bias issues within our AI recruitment practices.

4.2 Compliance and regulations

• We have researched relevant data privacy regulations concerning AI usage in recruitment (e.g., GDPR).
• We have developed policies and procedures to ensure compliance with data protection laws.
• We have established a clear data governance framework for AI-driven recruitment practices.
• We have a clear process for obtaining informed consent from candidates regarding data collection in recruitment.
• We will stay updated on evolving data privacy regulations and adapt our practices accordingly.
• We will conduct training for HR professionals on data privacy compliance in AI recruitment.

Conclusion

By reviewing the areas explored in this checklist, you've taken a crucial step towards building a recruitment engine powered by AI.

Remember, the goal isn't achieving a perfect score across every section. Instead, focus on demonstrating a strong foundation in these key areas:‍

• ‍Current state assessment: A clear understanding of your current recruitment process helps you pinpoint areas where AI can have the most significant impact.‍
• Organizational readiness: Team buy-in, strong leadership support, and a commitment to ethical AI practices pave the way for a smooth integration.
• ‍Implementation planning: A well-defined plan with vendor selection, project management, and ongoing support ensures a successful transition from legacy processes to AI-powered recruitment.
• Risk assessment & mitigation: Proactive risk management safeguards your recruitment process, builds trust with candidates, and demonstrates your commitment to responsible AI use.

The beauty of AI in recruitment is its flexibility.

Unlike traditional software implementations that can be disruptive and require major overhauls, AI in recruitment offers a more agile approach.

You don't need to rip and replace your entire system to see results. Instead, you can leverage AI's flexibility to target specific pain points, such as reducing the time your team spends on admin tasks, or increasing the effectiveness of your sourcing efforts.

For example, if your goal is to achieve zero-admin recruitment, you can start by delegating tasks related to interviews and intake calls to AI.

Start small, achieve immediate wins, and iterate from there. This helps with internal adoption, and ensures your team is fully comfortable collaborating with AI workmates before a more extensive roll-out.

If you’re curious to learn more about the use cases or the implementation of AI in recruitment and how Carv can help, feel free to get in touch.

Carv’s AI for Recruiters is designed to seamlessly integrate with your existing tools and workflows, enhancing your recruitment process without disruption, and taking you one step closer to AI-led recruitment.