The artificial intelligence tools entering the enterprise aren’t just faster versions of the automation HR teams have grown accustomed to. Unlike the rule-based systems of the past, today’s AI in HR can analyze, learn and make nuanced decisions that were once the exclusive domain of human judgment.
Speaking in a recent HR Executive webinar, Akshara Naik Lopez, senior analyst at Forrester, said the shift from traditional HR systems to AI-enabled platforms represents more than an upgrade—these new tools are sparking a reimagining of how work gets done. She drew a critical distinction: “With automation, we generally mean that we are looking at executing predefined tasks without human input; with artificial intelligence, basically, there’s a simulation of human intelligence to make decisions or learn from data.”
This distinction is critical for HR leaders, according to Naik Lopez. She highlighted that while conventional automation handles routine tasks like sending interview invites, AI can analyze resumes, rank candidates and even predict job fit using historical hiring data—capabilities that give organizations a significant edge in talent management.
The transformation extends beyond individual tools to entire system architectures, and implementing these is no small feat. Naik Lopez advocated for unified platforms over siloed systems, emphasizing that “AI models actually perform better with unified, high-quality data.” She says this technological foundation becomes the backbone for all other AI initiatives.
The ethical challenges of AI in HR cannot be treated as secondary considerations. “AI algorithms actually learn from historical data, and if that data contains existing human biases, the AI will replicate and even amplify those biases,” warned Naik Lopez. This creates potential for “discriminatory hiring practices, unfair performance evaluations and unequal opportunities.”
Akshara Naik Lopez
However, when implemented thoughtfully, AI can serve as a powerful tool for identifying bias. “With proper tuning, predictive AI can detect both subtle and explicit language, as well as behaviors that may stem from unconscious bias,” she explained.
The regulatory environment is evolving rapidly. Naik Lopez pointed to emerging frameworks like the EU AI Act and New York City’s Automated Employment Decision Tools Law, noting that different regions have varying requirements. She said HR leaders must build ethical frameworks proactively rather than reactively to avoid “facing legal action due to discriminatory outcomes, privacy violations or failure to adhere to transparency requirements.”
2. Invest in the right skills
The narrative around AI and jobs requires reframing, according to the analyst. “AI will influence more jobs than it actually replaces, making influence a more important metric than job loss,” said Naik Lopez. This shifts HR’s focus from managing job displacement fears to strategic workforce development.
She explained that “influence” describes how much AI alters the essential tasks of a job and the way that work is carried out. The higher the level of influence, the greater the need to redesign roles and provide training and upskilling so employees can effectively use AI in their daily work.
The solution lies in developing capabilities that complement rather than compete with artificial intelligence functionality. HR leaders must “heavily invest in skills that AI actually cannot replicate,” said Naik Lopez. She identified critical thinking, problem-solving, creativity, innovation, emotional intelligence, empathy, lifelong learning and ethical reasoning as traits worthy of development.
Naik Lopez also called for comprehensive initiatives that provide AI literacy across the entire workforce and offer practical training on specific AI tools relevant to each role, helping employees learn how to use them effectively.
3. Build strategic integration
Successful AI adoption hinges on maintaining a disciplined focus on business value. Naik Lopez stressed that value creation must serve as the guiding principle when introducing any new technology: If an AI initiative can’t clearly demonstrate a return on investment, it ultimately provides no benefit.
This value-first mindset also requires an assessment of potential risks. Naik Lopez suggested categorizing AI use cases by their possible impact on the business, asking questions like: If the AI makes an error, how serious are the consequences? What safeguards are in place?
For example, she pointed to drafting performance reviews as a high-risk application because errors could affect pay decisions, employment status or even trigger regulatory issues. By contrast, using AI to support project focus and task management carries minimal risk, as mistakes have little business impact.
4. Embrace data-driven decision making
AI’s effectiveness depends entirely on the quality and accessibility of the underlying data. Naik Lopez advised that “AI’s effectiveness hinges on data quality—invest in meticulous data collection, ensure data integrity, maintain consistency and implement stringent security protocols.”
She said data challenges in HR are particularly complex because systems often handle “vast amounts of sensitive employee data,” including not only employee information but also personal data of family members through benefits administration and other HR functions.
Naik Lopez advocated for centralized data strategies that enable “real-time dashboards and predictive analytics.” This approach supports advanced automation—like personalized learning plans or proactive retention alerts—while improving security through “fewer integration points and reduced risk of data breaches.”
The ultimate goal is to transform HR from reactive administration to predictive strategic partnership. “Natural language processing and large language models can make sense of that data, providing coherent insights about skills, employee sentiment, employee burnout, engagement and bias,” she explained.
5. Maintain a human-centric approach
All of this advice came back to one gold rule: Tech can’t do it all. As Naik Lopez emphasized, “No amount of innovative technology can fix a broken culture—only an organization can, by staying true to its core values and treating employees with respect to create real value.”
This principle should guide all AI implementation decisions, she said. Rather than seeing AI as a replacement for human work, leaders should reframe the narrative: Use AI to augment human capabilities. Identify tasks AI can handle—such as repetitive or data-heavy work—to free employees for higher-value, complex and creative contributions.
The human element becomes even more critical as AI takes over routine tasks. The analyst noted that HR decisions often require empathy and emotional intelligence, which AI currently lacks. She warned that employees may feel disconnected or undervalued if their primary interactions are with bots or dashboards instead of real people.
The organizations that will thrive will be those that master AI implementation while preserving their core values and commitment to human development. These foundational principles provide a framework for this journey, but Naik Lopez says success ultimately depends on leadership commitment to using AI as a tool for creating “more meaningful, productive and equitable work experiences” rather than simply automating existing processes.
This year at HR Tech: Join Forrester and EPAM for a conversation on how AI can empower L&D professionals to create more human-centric learning experiences. Register now.
