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Designing the Future of HR with AI: Smarter, Faster, Personalized


Human resources (HR) has always been at the forefront of organizational change, from the implementation of performance management systems to the adoption of applicant tracking software. However, as artificial intelligence (AI) becomes integrated into more and more workplace processes, HR must again reinvent itself to take advantage of new technologies that can both streamline operations and enhance the employee experience.


Today we will explore how HR can design its future with AI to become smarter, faster and more personalized through the application of emerging tools like chatbots, predictive analytics and talent matching.


A Brief History of HR Technology Adoption


To understand where HR is going with AI, it is important to recognize how far the function has already come with previous waves of technology. In the late 20th century, early HR systems automated basic recordkeeping tasks like benefits administration and payroll (Lepak & Snell, 1998). These mainframe programs brought efficiency gains but offered little in the way of insights. As the internet emerged in the 1990s, HR portals for self-service allowed employees to access their own data anytime from anywhere. Applicant tracking systems digitized recruiting to manage the flow of candidates (Joo & Park, 2010). Performance management also went electronic with the goal of standardizing annual reviews (Johnson, 2001).


While foundational, these initial HR technologies focused more on transactions than transformations. Data was siloed within departments with limited ability to share insights organization-wide. HR professionals primarily used technology to support administrative duties rather than proactively advise business leaders (Ulrich et al., 2012). Adoption centered on compliance and cost-cutting rather than strategic impact on talent or culture. The next evolution demands harnessing AI to take HR to a higher level of strategic partnership, continuous improvement and work redesign.


Building Smarter HR Operations with AI Chatbots


Going beyond basic needs, AI offers new and novel ways for HR teams to engage employees while freeing up time for higher-level activities. Chatbots powered by natural language processing are demonstrating value across industries by answering common questions from both candidates and staff (Giles, 2018). For example, Anthropic developed Claude, an AI assistant focused specifically on HR tasks like paid time-off requests, updating benefits selections and signing non-disclosure agreements.


By fielding routine inquiries digitally around the clock, chatbots can make HR operations smarter through increased self-service and reduced ticket volumes. Research from PwC (2018) found that well-designed virtual agents can handle 60-80% of employee inquiries independently, slashing the time spent on repetitive inquiries by half. Forward-thinking HR leaders are partnering with IT to embed chatbots within existing HR information systems and intranets. At organizations like Vodafone, chatbots power a “virtual colleague” answering questions about compensation, work policies and career development (Raconteur, 2018).


Beyond efficiency, AI chatbots offer a more positive employee experience. Responses are immediate versus potential wait times for a human representative. Chatbots can also operate outside of regular business hours for round-the-clock support. By personalizing conversations, virtual agents build rapport with employees in a warm and welcoming manner. When properly trained on organizational culture and language patterns, they represent the brand consistently through each interaction. Where challenges arise outside a bot’s scope, the conversation can be seamlessly transferred to a human expert. For ambitious HR departments, chatbots represent a smart starting point for digitizing processes and elevating service delivery.


Faster Decision Making with Predictive Analytics


Powerful algorithms now analyze workforce data to provide proactive insights, speeding up HR assessments and planning. Predictive models utilize past patterns and employee attributes to forecast everything from retention and performance risks to skills gaps and future staffing needs (Aken & Michalisin, 2007). For example, analytics can identify which employees are most likely to leave voluntarily in the next 6-12 months based on factors like compensation, engagement scores, career development opportunities and manager quality.


Armed with these predictions, HR and business leaders can take preventative action such as targeted training, project assignments, pay adjustments or even early promotion. Research finds retention-focused interventions informed by predictive models can reduce unwanted turnover by 20-50% (Fitz-enz, 2010). Other applications include automating candidate screens to spot the applicants most likely to succeed in new roles if hired. Based on attributes from past top performers, models cut through resumes up to 80% faster than human reviewers alone (Harper, 2017).


Faster access to predictive insights transforms HR from reactive to proactive. At SAP, a data science team partners closely with HR to develop customized models answering operational questions specific to their business (HR Executive, 2017). For example, one model identifies which roles suffer the most unplanned absenteeism to proactively address underlying issues. Analytics speeds up strategic workforce planning by outlining future capacity and skill needs years in advance versus vague annual forecasts. The faster decision cycles and continuous improvement enabled by people analytics represent a total Mindset shift for high-performing HR.


Personalized Experiences through Talent Matching


AI-powered matching algorithms analyze profiles of both candidates and open roles to surface the best fits through skills, experience, interests and organizational culture match. By considering far more attributes than hiring managers alone, these systems identify hidden talent that may have otherwise been overlooked (Cappelli, 2012). Google uses refined matching to sift through over 2 million resumes per year and recommends top candidates directly to hiring managers. They estimate AI matching improves hiring quality by 30% and reduces time-to-hire by 25% versus traditional methods (Van den Heuvel, 2017).


Outside of recruitment, companies deploy talent matching platforms to help employees discover new opportunities internally before considering external moves. Software suggests the roles, managers and projects that align closest with each individual's background, strengths and career goals. According to Deloitte's 2018 Global Human Capital Trends survey, 43% of organizations have adopted some form of talent mobility technology, up from just 13% in 2012 (Deloitte, 2018).


Personalized talent matching accelerates skill and leadership development through curated stretches and on-the-job learning. By surfacing great developmental fits proactively, the process feels less rigid and more empowering for all parties involved. AI matching also helps optimize utilization of scarce expertise across divisions. Overall, these systems enrich careers while retaining top performers in a tight labor market (Stainton, 2018). Talent mobility supported by advanced algorithms brings personalization to a new level for forward-thinking HR departments.


Recommendations and Considerations for Implementation


While AI promises transformation, successful adoption requires careful planning, change management and ongoing governance. Several key recommendations emerged based on academic research and organizational examples:


  • Start with the employee experience - Chatbots, analytics and matching should center on solving real pain points and demonstrating fast value for both candidates and current staff. Measure engagement and sentiment to ensure AI enhances rather than replaces human interactions.

  • Take an experimental approach - Pilot high-impact but lower risk use cases to build internal champions, develop expertise and refine processes incrementally before scaling solutions company-wide. Continually test, learn and iterate based on data.

  • Focus on collaborative partnership - HR must work hand-in-hand with business leaders, IT and data science teams to design solutions aligned with strategic priorities and embedded within existing systems seamlessly. Strong cross-functional governance maintains accountability.

  • Address ethics, fairness and bias proactively - Transparency into how algorithms make decisions helps establish appropriate controls and oversight. Continually validate models for fairness towards all demographic groups to avoid unintended discrimination.

  • Support a learning culture - AI evolves rapidly. Empower employees at all levels to experiment with emerging tools through dedicated skilling and resources. Measure skills gaps to reskill displaced roles into higher-value activities.

  • Consider phased investment - Rather than “big bang” overhauls, schedule deployments thoughtfully based on payback periods, departmental readiness and technology adoption curves specific to each organizational context.


The workplace of tomorrow demands an HR function leading change, not reacting to it. With careful planning and stakeholder buy-in, AI offers a promising toolkit for designing the future of HR into its smartest, fastest and most personalized form yet. HR technology maximizes its impact through a commitment to continuous experimentation, collaboration and a laser focus on elevating every employee experience along the talent lifecycle.


Conclusion


As AI reshapes work itself, HR has a mandate to steer that transition proactively on behalf of both organizations and individuals. Emerging chatbots, analytics and talent matching represent the beginning, not the end, of HR's technology revolution. By putting employee needs front and center in all implementation decisions, HR can feel confident ushering in smarter, faster and personalized processes that complement rather than compete with human relationships. While challenges always accompany change, a healthy appetite for experimentation, learning agility and cross-functional alignment will serve HR leadership well in designing tomorrow with AI today. The future remains unwritten, and it awaits those bold and ambitious enough to create new possibilities through advanced technologies applied responsibly and for the benefit of all.


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Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.


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