Introduction
The explosion of artificial intelligence[1]’s (AI) popularity has transformed various sectors, with recruitment being no exception. In fact, Simpler Media Group’s State of the Digital Workplace found that 9 out of 10 organizations are using generative AI—for everything from content creation to customer service.1 At the same time, KPMG reports that 51% of organizations are exploring the use of AI agents, and 37% are piloting them.2
As organizations strive to enhance efficiency and improve candidate experiences, the emergence of agentic AI presents a paradigm shift in recruitment processes. This article explores the concept of agentic AI, its implications for the recruitment industry, and the broader societal impacts of this technology.
This article will also define agentic AI, explore Open AI’s roadmap to artificial general intelligence as it relates to agentic AI, and discuss the implications of agentic AI in recruiting and beyond.
Understanding Agentic AI
Agentic AI refers to autonomous, goal-directed agents capable of performing non-deterministic tasks without human intervention.
“It refers to AI systems and models that can act autonomously to achieve goals without constant human guidance,” said Enver Cetin in the Harvard Business Review.
Unlike traditional AI systems that rely on rule-based algorithms, Agentic AI can learn from experiences, adapt to new situations, and make decisions based on contextual data[16]. This capability is rooted in advanced machine learning[5] techniques, including reinforcement learning, which allows these agents to optimize their performance over time.
Agentic AI is poised to change all work sectors significantly in the coming years—it was even named a Top 10 Emerging Technology by Forrester.3 Legacy technology brands like Microsoft and Salesforce, recruitment tech brands like Daxtra, and companies across other verticals (like Aflac, Legendary Entertainment, and NASA4) also invest in agentic AI or AI agents.
Agentic AI isn’t a new concept—it’s been talked about in computer science and AI circles for years—but like generative AI[7] in the early 2020s, it is poised to be the next big thing in tech.
The Roadmap to Artificial General Intelligence (AGI)
Sam Altman, CEO of OpenAI, outlined a roadmap to achieve Artificial General Intelligence (AGI).5 This roadmap consists of five levels:
- Chatbots: Basic conversational agents that respond based on pre-defined script, regurgitating outputs based on the inputs and prompts received.
- Reasoning Models: More advanced AI models capable of logical reasoning and problem-solving.
- Agentic AI: Autonomous AI agents that can operate independently and make decisions.
- Innovation Models: Systems that can contribute novel ideas and solutions.
- Organizational Improvements: AI that enhances overall organizational efficiency and effectiveness.
At step 3, agentic AI represents a significant leap from traditional AI systems. Rather than responding to prompts, agentic AI can act autonomously and purposefully. This response capability makes agentic AI particularly relevant for industries that require complex decision-making processes, such as customer service or recruitment.
The Role of Agentic AI in Recruitment
Like generative AI before it, agentic AI has enormous potential to impact the world of recruitment. According to KPMG’s AI Quarterly Pulse Survey, more than half of organizations are exploring AI agents – and as many as 60 percent of leaders expect to utilize the capability for administrative duties.6
When it comes to recruitment, there are similar applications for agentic AI, including:
- Enhancing Candidate Experience
One of the most pressing challenges in recruitment is the candidate experience. Traditional recruitment processes often involve lengthy application[8] procedures, delayed feedback, and a lack of personalized communication. Agentic AI can address these issues by automating candidate engagement and providing real-time[12] feedback. For instance, AI-driven chatbots can interact with candidates, answer their queries, and guide them through the application process[17], reducing frustration and enhancing satisfaction.
Evidence suggests that this automation doesn’t only benefit recruiters. Piotr Horodyski found that candidates perceive AI tech positively in hiring processes, mainly due to the reduced response time from employers and recruiters.7
- Streamlining Sourcing and Screening
Sourcing and screening candidates are time-consuming tasks that could benefit significantly from agentic AI. By leveraging advanced algorithms and learning, agentic AI can analyze vast amounts of data to identify candidates who match specific job requirements. This capability could accelerate the sourcing process and improve the quality of candidate shortlists by finding candidates and working with automation to streamline the shortlisting process. Ultimately, this would mean recruiters have more time to leverage their expertise in connecting with candidates, providing them a valuable tool for addressing the surge in AI-powered candidate applications.8 Furthermore, AI can continuously learn from past hiring decisions, refining its algorithms to enhance future candidate matching.
- Onboarding[13] and Continuous Engagement
Once a candidate is hired, onboarding is crucial for their long-term success within the organization. Agentic AI could help facilitate onboarding by providing personalized training programs, answering questions, and guiding new hires through company policies and procedures—without needing a human to check in and prompt each step of the process. Additionally, agentic AI could monitor employee engagement[3] and performance, offering insights that help organizations identify areas for improvement and support.
Broader Implications of Agentic AI
It’s no secret that agentic AI and the tech landscape are evolving rapidly—and with that rapid change come broad impacts across not only recruitment but also the future of work in virtually every other field and vertical. As Grant Goss writes for CIO:
“With ServiceNow, Salesforce, and other vendors embracing agentic AI, enterprise workflows will be a sweet spot for the technology, experts say, enabling businesses to streamline processes by automating routine tasks,” he says. “For instance, an AI agent could turn meeting notes into project[18] tickets without human input or trigger[19] a supplier order in response to a demand-supply prediction,” he says (also citing Sheldon Monteiro, executive vice president and chief product officer at Publicis Sapient).9
Of course, there are ethical dilemmas, like bias or environmental impact.10 But with that risk[20] comes opportunity, and as agentic AI becomes more popular, employers will need to find a way to balance the two.
Once balanced and used effectively, agentic AI has broad implications, ranging from disrupting existing jobs to changing the nature of work.
- Disruption of Traditional Roles
The rise of Agentic AI is likely to disrupt at least some traditional roles. As AI systems become more capable of performing tasks that were once the sole responsibility of humans, the skills required for professionals may shift.
Bartech Staffing’s 2025 What Candidates Want Study found that 30% of interviewed candidates believed AI would improve task[21] efficiencies, while 28% believed AI would improve data analytics[14].11
These efficiencies apply to recruiters, too. LinkedIn’s Future of Recruiting Study found that only 32% of talent acquisition professionals weren’t exploring, experimenting, or integrating generative AI into their hiring processes. That study also found that, on average, those who had integrated generative AI saved 20% of their time.12
Leveraging these tools has a clear benefit, but to make the most of them and ensure adaptation in the future, recruiters will likely need to develop skills in AI management, data analysis, and strategic decision-making and focus on higher-value human tasks such as relationship-building, project management[4], and strategic planning.
Implementing agentic AI in recruitment also raises ethical concerns that must be addressed. Issues such as algorithm[15] bias, data privacy[9] concerns, and the potential for job displacement are all critical considerations.
In Computers in Human Behavior Reports, Horodyski notes that “the lack of nuance in human judgment, low accuracy and reliability and immature technology were identified as the biggest drawbacks of AI in recruitment.”13
As a result, organizations must ensure that their use of AI systems is designed to promote fairness and transparency, mitigating the risk of perpetuating existing biases in hiring practices. Additionally, data privacy regulations must be adhered to to effectively safeguard candidate information[10] and maintain trust in the recruitment process.
- The Future of Work
Integrating Agentic AI into recruitment workflows is part of a broader trend toward automation and digital transformation in the workplace. As organizations increasingly rely on AI to enhance productivity and efficiency, the nature of work will likely evolve. While some roles may become obsolete, new opportunities will emerge, requiring an adaptable and skilled workforce leveraging AI technologies. Continuous learning and professional development will be essential for individuals to thrive in this changing landscape.
Conclusion
Agentic AI represents a significant advancement in artificial intelligence, with profound implications for the recruitment industry and the workforce. By enhancing candidate experiences, streamlining processes, and enabling autonomous decision-making, agentic AI has the potential to revolutionize recruitment practices. However, as organizations embrace this technology, they must also navigate the ethical challenges and workforce implications accompanying its implementation[6]. The integration[11] of agentic AI will undoubtedly shape the future of recruitment, and those who adapt to this change will be well-positioned to succeed in the evolving work landscape.
The rise of agentic AI is not merely a technological trend but a transformative force that will redefine how organizations approach recruitment and talent management. As we progress, industry stakeholders must engage in thoughtful discussions about the opportunities and challenges this technology presents, ensuring its implementation benefits organizations and candidates alike.
ENDNOTES
1 The State of the Digital Workplace (Reworked Insights), Simpler Media Group, Inc., https://bit.ly/3Xf9dYC
2 KPMG AI Quarterly Pulse Survey (KPMG), KPMG, January 9, 2025, https://bit.ly/3X9xdww
3 Forrester’s Top 10 Emerging Technologies For 2024: As AI Dominates, Security Becomes Paramount (Forrester), Brian Hopkins, June 25, 2024, https://bit.ly/4i7ZTxQ
4 Agentic AI: 6 promising use cases for business (CIO), https://bit.ly/4bdVlUr
5 Planning for AGI and Beyond (OpenAI), https://bit.ly/3QxdV0e
6 KPMG AI Quarterly Pulse Survey (KPMG), bit.ly/3X9xdww
7 Piotr Horodyski, Applicants’ perception of artificial intelligence in the recruitment process, Computers in Human Behavior Reports, Volume 11, 2023, https://bit.ly/4kcAOnm
8 Is AI sabotaging the ‘September Surge’ in hiring this year? (Fast Company), https://bit.ly/3QvaYx9
9 Agentic AI: 6 promising use cases for business (CIO), https://bit.ly/4bdVlUr
10 The ethical dilemmas of AI (USC Annenberg Relevance Report), https://bit.ly/3ERtrS0
11 What Candidates Want 2025 (Bartech Staffing/Impellam Group), https://bit.ly/3QunyN5
12 The Future of Recruiting 2025: How AI redefines recruiting excellence (LinkedIn), https://bit.ly/4iaYNBC
13 Piotr Horodyski, Applicants’ perception of artificial intelligence in the recruitment process, Computers in Human Behavior Reports, Volume 11, 2023, https://bit.ly/4kcAOnm
The simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. The simulation of human intelligence in machines that enables them to perform tasks such as learning, reasoning, and problem-solving.
The ethical principles and considerations that guide the development, deployment, and use of technology, ensuring responsible and ethical practices in Web3 applications and systems.
Employee engagement, also called worker engagement, is a business management concept. An “engaged employee” is one who is fully involved in, and enthusiastic about their work, and thus will act in a way that furthers their organization’s interests.
The application of knowledge, skills, tools and techniques to project activities to meet project requirements.
An application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available. A branch of artificial intelligence that enables computers to learn and improve from experience without being explicitly programmed.
