This video explores the current state and challenges of implementing artificial intelligence (AI) in human resources (HR), emphasizing the significant gap between the high ambitions HR leaders have for AI and the reality of its practical application. Despite widespread recognition of AI’s potential to transform hiring, talent development, and employee support, most organizations are still in the nascent stages of using AI effectively. A major issue is the implementation gap—many companies purchase AI tools without integrating them properly into workflows, resulting in underutilized, costly technology that fails to deliver measurable benefits. Highlights 🤖 AI ambition in HR is high, but practical implementation lags behind reality. 🔍 AI in HR can be categorized into automation, augmentation, and agentic AI. ⚠️ Most AI failures stem from poor strategy, not faulty technology. 🛠️ Redesigning workflows before implementing AI is critical for success. 🎯 Clear, measurable business outcomes must drive AI projects. 🤝 Human-AI partnerships enhance trust and effectiveness in HR tasks. 📈 A real-world example shows how focused, user-centric AI can deliver significant value. Key Insights 🤖 Implementation Gap as the Core Challenge: Although HR leaders widely acknowledge AI’s potential, more than 80% see AI as essential for the future, yet fewer than 20% feel equipped with the skills or processes to deploy it successfully. This gap highlights the need for organizations to move beyond merely acquiring technology and focus on practical integration into daily workflows. The “shiny new tool” syndrome leads to wasted resources and skepticism about AI’s value. 🧩 Three Flavors of AI in HR Clarify Application Scope: Categorizing AI into automation, augmentation, and agentic AI helps HR leaders understand what AI can realistically do today. Automation handles rule-based repetitive tasks—freeing up time but limited to straightforward processes. Augmentation enhances human decision-making by analyzing data patterns, making recruiters or HR professionals more efficient and accurate. Agentic AI, though more advanced, can autonomously coordinate complex workflows, representing a future direction. 🚫 Technology-First Approach Leads to Failure: The video emphasizes that many AI projects fail because companies rush to buy new technology without analyzing or improving existing workflows. Automating flawed or inefficient processes simply accelerates problems rather than solving them. The failure cycle—technology buy-in without strategic fit, low user trust, absence of measurable value, abandonment—underscores the importance of starting with a process-first mindset. 🎯 Necessity of Specific, Measurable Business Goals: Vague aspirations like “improve candidate experience” or “increase employee engagement” are insufficient to guide AI projects. Instead, setting precise, quantifiable targets—such as “reduce time to hire by 15%” or “decrease first-year attrition by 5%”—creates clarity and accountability. This clarity enables organizations to track progress, prove return on investment, and maintain stakeholder support. 🤝 Human-Centric Design and Change Management Are Vital: AI tools often fail because they are developed in isolation by IT or analytics teams with little input from HR professionals—the actual end users. Involving users throughout the design and testing phases builds trust, ensures the tool addresses real pain points, and fosters adoption. Additionally, designing AI to complement human skills—empathy, judgment, ethical reasoning—instead of replacing humans builds a sustainable human-AI partnership. 🚀 Agile, Product Management Approach Enhances AI Implementation: Treating AI initiatives like products rather than one-off projects promotes iterative development, cross-functional collaboration, and ongoing refinement. Agile methodologies enable teams to test minimum viable products (MVPs), gather user feedback, and scale solutions only after demonstrating clear value. 📊 Case Study Demonstrates Best Practices in Action: The example of a global retailer’s AI hiring tool embodies the principles outlined. By mapping the entire recruitment workflow, the team identified a high-impact bottleneck—manual resume screening—and developed a focused AI augmentation tool. The tool surfaced the top 20% of candidates for human review, reducing shortlisting time by 40%. Crucially, recruiters were involved from the start, fostering trust and high adoption. #AIinHR #PeopleAnalytics #HRTech #HRLeadership OUTLINE: 00:00:00 - Ambition vs Reality in HR 00:01:46 - Three Flavors of AI 00:03:17 - Why So Many AI Projects Fail 00:04:39 - The Practical Playbook 00:07:00 - Making AI Work for People

Abstract: Despite surging interest in artificial intelligence within human resources, most organizations remain in the early stages of their AI journey, with two-thirds having less than one year of implementation experience. This article synthesizes research and practitioner insights from David Green's comprehensive September 2025 HR analytics review to examine why many HR departments struggle to realize value from their AI investments. The analysis explores the implementation gap between AI ambition and business outcomes, revealing that successful organizations prioritize workflow redesign over technology adoption, take a product-centric approach to implementation, and maintain a focus on human oversight. The article provides a structured framework for HR leaders to move beyond pilot implementations to achieve scalable, value-generating AI applications that augment rather than replace human capabilities.

In this HCI Webinar, Dr. Jonathan H. Westover talks with Donny Phillips about HR's expanding role in the protection of employee data. Donny Phillips is a nearly 30-year industry expert who has served in leadership for multiple human resource and payroll service providers, including some of the largest third-party administrators and employment and income verification vendors. During his career, Donny has consulted with and provided services to employers of all sizes from the largest of the Fortune 100 to local small businesses as well as local governments and federal governmental agencies. Donny is passionate about helping clients optimize programs while focusing on doing the right thing for all stakeholders.

Many companies invest heavily in artificial intelligence (AI) with the expectation of transformative business results, yet most struggle to realize significant enterprise-wide value. The paradox lies not in the technology itself but in how AI is integrated into existing workflows. While individuals use AI tools to boost personal productivity, these gains do not scale across teams or departments. A common mistake organizations make is the “layering trap”—simply adding AI to old, inefficient processes without redesigning them, resulting in bottlenecks shifting rather than disappearing. To unlock AI’s true potential, companies must embrace a strategic discipline called work redesign, which involves fundamentally rethinking how work is performed by breaking down jobs into tasks and determining which are best suited for humans or AI. This approach requires cultivating hybrid skills, establishing clear governance, and committing to continuous adaptation as AI technology evolves. Starting small with focused workflows prone to bottlenecks can generate quick wins and serve as a proof of concept for broader organizational transformation. Highlights 🤖 Massive AI investments often fail to deliver enterprise-wide value due to flawed integration strategies. 🧩 The “layering trap”: simply adding AI to existing processes without redesign limits impact. 🔍 Work redesign involves breaking down jobs into tasks to identify what AI and humans should each handle. 🎯 Cultivating hybrid skills is crucial for employees to effectively collaborate with AI tools. 🛠️ Clear governance and decision rights prevent confusion and errors in AI-driven workflows. 🔄 Continuous redesign is necessary to keep pace with rapidly evolving AI capabilities. 🚀 Starting small with targeted workflows can create quick wins and build momentum for AI adoption. Key Insights 🤔 The AI paradox stems from misunderstanding value creation, not technology limitations: Despite the sophistication of AI tools, the disappointing returns on investment highlight a strategic gap. Companies often focus on the technology itself rather than on transforming how work is designed and executed. This insight underscores that AI’s potential is unlocked only when integrated thoughtfully into redesigned workflows. 🏗️ Layering AI onto legacy processes is analogous to putting a jet engine on a horse-drawn cart: This vivid analogy illustrates how powerful AI can be stifled by outdated structures. Automating one step in a long, manual sequence does not remove systemic inefficiencies; bottlenecks simply move downstream. This highlights the necessity of reimagining entire processes rather than applying piecemeal fixes. 🔄 Work redesign is a holistic rethink of processes, roles, and management, not just technology deployment: The transformative power of AI emerges when organizations rethink the division of labor between humans and machines. This requires breaking jobs into tasks to determine which should be automated and which require uniquely human skills such as empathy, creativity, and complex judgment. 🎓 Hybrid skills development is essential for maximizing human-AI collaboration: As AI takes over routine tasks, employees’ roles shift toward oversight, exception handling, and decision-making informed by AI outputs. Developing these hybrid skills demands targeted, on-the-job training that integrates AI literacy with domain expertise, emphasizing continuous learning in the flow of work. 🛡️ Clear governance and decision rights are critical to managing AI-driven workflows: Ambiguity in who owns decisions or manages AI tools can lead to errors and inefficiencies. Establishing explicit roles, responsibilities, and accountability frameworks ensures smooth collaboration between humans and AI and safeguards against operational risks. 🚀 Starting AI integration with focused, high-impact workflows enables quick wins and organizational learning: Rather than launching massive AI programs, companies should begin with a single painful or bottlenecked process, empowering a small team to redesign it with AI in mind. This approach generates tangible results quickly, builds confidence, and creates a replicable model for scaling AI adoption. 🔮 Continuous adaptation is necessary due to AI’s rapidly evolving nature: AI is not static; capabilities will advance beyond current imagination. Organizations must embed agility into their approach, embracing ongoing work redesign and iterative improvement to maintain competitive advantage and avoid obsolescence. This mindset shift is as important as any technical upgrade. #AIROI #WorkRedesign #HumanAICollaboration #OrganizationalChange #AITransformation OUTLINE: 00:00:00 - Spending More, Gaining Less 00:01:14 - Why Adding AI to Old Work Fails 00:02:24 - An Introduction to Work Redesign 00:03:38 - The Five Pillars of AI-Powered Work Redesign 00:04:49 - Making Redesign a Reality

Abstract: Despite widespread adoption of artificial intelligence tools at the individual level, organizational returns remain disappointing. Recent industry research indicates that only a small fraction of companies achieve significant value from AI investments, with satisfaction rates similarly low. This gap between individual experimentation and enterprise-scale value realization stems not from technological limitations but from a fundamental mismatch: organizations layer AI onto legacy processes rather than redesigning work systems to exploit AI's capabilities. This article synthesizes evidence from management consulting, organizational design, and human-computer interaction research to demonstrate that sustainable AI value requires systematic work redesign. Organizations must analyze and reconstruct roles, cultivate hybrid digital-domain expertise, and realign skill requirements to match augmented workflows. Without intentional redesign of work architectures, AI initiatives remain trapped in pilot purgatory, generating demonstrations rather than transformative business outcomes. Evidence-based interventions spanning process deconstruction, capability development, governance structures, and change management offer pathways from tactical adoption to strategic value creation.

In this HCI Webinar, Dr. Jonathan H. Westover talks with Jess Ringgenberg about career stagnation and identity shifts. Jess Ringgenberg, PCC, is a researcher, former Fortune 500 biotech leader, and mother of two with deep expertise at the intersection of caregiver strategy, leadership, and advocacy. She is the Founder & CEO of ELIXR, a workforce advisory firm on a mission to make caregivers in the workplace impossible to ignore—turning a hidden risk into a driver of retention, performance, and growth. Jess also spearheaded The Science of Modern Motherhood™, a national study redefining how organizations support parents and mothers across the career continuum. A recognized voice in the care economy, she has spoken at government summits, national conferences, and partnered with organizations including Microsoft, Southwest Airlines, and Hilti.

Artificial Intelligence (AI) has evolved from a mere technological tool to the central operator driving critical business functions across industries. This transformation presents a fundamental challenge for organizations because traditional management structures, designed for human workers, are ill-suited to govern the dynamic, high-volume decision-making performed by AI systems. The conventional hierarchical organizational chart, based on clear chains of command and human accountability, breaks down when applied to AI, leading to confusion over responsibility, inefficiencies, and risks in sectors such as banking, healthcare, and logistics. Highlights 🤖 AI has transitioned from a tool to a core operator managing critical business functions. 🏢 Traditional hierarchical organizational structures fail to accommodate AI’s decision-making scale and speed. 🔄 Successful AI integration requires new platform-based structures with cross-functional hybrid teams owning entire processes. ⚖️ Dynamic decision rights clearly define AI autonomy boundaries, balancing efficiency and human oversight. 📊 Data stewardship by business owners ensures high data quality, ethical use, and trustworthy AI outputs. 👥 AI augments human work by automating repetitive tasks, allowing humans to focus on creativity and judgment. 🚀 Incremental pilots and leadership enabling decentralized decisions facilitate smooth AI adoption and organizational transformation. Key Insights 🤖 AI as an Algorithmic Worker Requires Rethinking Management: Traditional management hierarchies—designed for human supervision—cannot cope with AI’s capacity for thousands of independent decisions per minute. This necessitates a fundamental redesign of organizational structures to accommodate AI as a new type of “worker” that operates autonomously yet requires integrated oversight. The failure to do so leads to accountability gaps and operational inefficiencies. 🏦 Accountability Fragmentation Undermines Trust and Efficiency: The transcript highlights real-world examples in banking and healthcare where responsibility for AI errors is diffused across IT, data science, and business departments. This fragmentation erodes customer trust and increases operational risk. Organizations must establish clear ownership of AI-driven decisions through integrated teams to restore accountability and improve outcomes. 🛠️ Platform Model Balances Centralized Expertise with Decentralized Innovation: Centralizing AI infrastructure management as a utility-like platform ensures robustness, security, and compliance. Meanwhile, decentralized hybrid teams—each responsible for an entire business process—drive agile innovation and continuous AI improvement. This hybrid model breaks down silos, accelerates problem-solving, and aligns AI deployment tightly with business goals. 👩‍💻 New Roles Like AI Trainers and Auditors Are Crucial for Ethical AI: AI trainers, often subject matter experts rather than data scientists, play a vital role in teaching AI systems and refining their performance. AI auditors independently evaluate for bias, fairness, and compliance, safeguarding against discrimination and legal risk. These roles institutionalize ethical AI governance and ensure responsible AI evolution. ⚙️ Dynamic Decision Rights Enable Safe AI Autonomy: By defining explicit rules about when AI can act independently and when human review is mandatory, organizations strike a balance between efficiency and risk management. For example, low-risk loan approvals can be fully automated, while complex cases are escalated. This approach creates a predictable partnership between AI and humans, facilitating trust and operational clarity. 📈 Data Stewardship as a Business Discipline is Essential: Unlike traditional IT ownership, appointing business data stewards for critical domains ensures data integrity, accuracy, and ethical use. This stewardship prevents the “garbage in, garbage out” problem that can degrade AI performance and compromise fairness. Good data governance is the foundation of trustworthy AI and sustainable AI-driven decision-making. 🌱 Incremental Change and Leadership Evolution Are Keys to Success: Transforming organizations to work effectively with AI does not require overnight overhaul. Starting with mapping AI’s influence and piloting cross-functional teams in contained settings allows learning with limited risk. Leaders must shift from command-and-control to roles as enablers and facilitators, championing transparency, trust, and continuous learning. This cultural transformation is as important as structural redesign for AI success. #AIFirst #OrgDesign #AIGovernance #HybridTeams #DecisionRights OUTLINE: 00:00:00 - Why AI Breaks Traditional Structures 00:01:17 - Where Old Models Fail 00:02:55 - Platform Teams and Hybrid Roles 00:04:29 - Governance, Data, and Trust 00:06:08 - A Leader's Guide to Redesign

Abstract: Organizations deploying artificial intelligence at scale face a fundamental structural challenge: traditional hierarchies built for human decision-making prove inadequate when algorithms assume core operational roles. This article examines how AI-first operations—where AI systems execute primary workflows rather than merely supporting human tasks—necessitate new organizational forms that blend human oversight with algorithmic autonomy. Drawing on research across technology, financial services, healthcare, and logistics sectors, we identify how leading organizations are reconfiguring decision rights, accountability frameworks, and team structures to accommodate hybrid human-AI operations. The analysis reveals that successful AI-first organizations adopt platform-based structures with distributed authority, create new coordination roles bridging technical and operational domains, and establish governance mechanisms that maintain strategic human control while enabling algorithmic execution. These structural innovations carry significant implications for organizational performance, workforce adaptation, and operational resilience in an increasingly automated economy.