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.

In this podcast episode, 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. Check out all of the podcasts in the HCI Podcast Network (https://www.podbean.com/podcast-network/HCI) !

In this HCI Webinar, Dr. Jonathan H. Westover talks with Namit Jindal about AI, organizational leadership, and scaling your business. Namit Jindal runs a software company called Aerosend.io. They help B2B companies send cold emails that land in the inbox. They specialize in facilitating highly personalized emails that are relevant to the audience.

Artificial intelligence (AI) is rapidly transforming human resources (HR), promising to revolutionize how organizations hire, develop, and support employees. Despite high expectations, many companies are still in the early stages of AI adoption and struggle to convert ambition into measurable results. AI in HR can be categorized into three types: automation, augmentation, and agentic AI. Automation handles repetitive tasks; augmentation enhances human decision-making; and agentic AI manages multi-step processes independently within set boundaries. However, many AI initiatives fail due to a “tool-first” mindset, lack of clear value focus, and insufficient integration with existing workflows. Successful AI implementation requires a human-centered approach, transparency, and governance to build trust and improve outcomes. The recommended strategy is to start small by deeply understanding critical HR workflows, selecting a targeted use case, building a minimum viable product with cross-functional teams, and instituting governance and feedback mechanisms. This incremental approach helps organizations move from hype to tangible value, making AI an enabler of more human-centered work rather than a replacement for people. Highlights 🤖 AI is transforming HR by automating, augmenting, and independently managing processes. 📉 Most companies are still in early AI adoption stages, with a gap between ambition and results. 🛠️ Common failure stems from buying tools without process mapping or clear value focus. 👥 Human factors like trust, transparency, and employee empowerment are critical for success. 🔍 A workflow-first approach helps identify real friction points before introducing technology. 🚀 Starting small with clear metrics builds momentum and demonstrates AI’s practical value. ⚖️ Governance, fairness, and privacy must be embedded from the outset to ensure ethical AI use. Key Insights 🤖 AI’s Three Pillars in HR: Automation, Augmentation, Agentic AI AI capabilities in HR are not monolithic but fall into distinct categories. Automation replaces repetitive tasks (e.g., screening resumes), freeing human time. Augmentation acts as an intelligent assistant, enhancing managerial decisions with narrative analytics and data insights. Agentic AI, the most advanced, independently executes complex workflows under predefined rules. Recognizing these distinctions helps organizations set realistic expectations and apply AI strategically rather than treating it as a one-size-fits-all solution. ⚠️ The Danger of a Tool-First Mindset Many organizations purchase AI platforms without first understanding their existing HR workflows, leading to underutilized or disruptive tools. This approach wastes resources and breeds frustration. The imperative is to map out workflows meticulously to identify true pain points and decide where AI can add leverage. Without this step, AI becomes a costly experiment rather than a transformative asset. 📊 Value-Driven AI Deployment Requires Clear Metrics and Use Cases AI initiatives often fail when there is no clarity on what decisions the technology will improve or how success will be measured. For example, if managers receive AI-generated risk scores on employee attrition but cannot interpret them or translate insights into action, the tool produces noise rather than value. Defining concrete objectives—such as reducing time-to-hire or improving retention by a specific percentage—is essential to demonstrate ROI and sustain investment. 👥 Human-Centered Design Builds Trust and Adoption Fear of job loss or surveillance can create resistance among employees. Leaders must communicate that AI empowers rather than replaces workers. Systems should be designed to allow human oversight, enabling employees to review, override, and correct AI outputs. This fosters trust and creates learning loops, improving both human and machine intelligence over time. 🚀 Start Small to Build Momentum, Avoid Overreach The temptation to “boil the ocean” by deploying AI at scale too soon leads to failure. Instead, starting with one well-understood challenge and achieving a modest win creates momentum and showcases AI’s potential. ⚖️ Ethical Governance and Data Literacy as Pillars of Sustainable AI Embedding governance—covering fairness, transparency, bias checks, and privacy—is not optional but foundational. AI decisions impact people’s careers and lives, so organizations must invest in clear policies and educate HR teams on data literacy and AI concepts. OUTLINE: 00:00:00 - Bridging AI Ambition and HR Reality 00:00:59 - Three Flavors for HR 00:02:13 - Common Pitfalls and How to Avoid Them 00:03:32 - The Workflow-First Framework 00:04:53 - Your First Steps to Meaningful AI Integration

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.

The narrative that Generation Z workers are inherently flaky, disloyal, or entitled is a misconception rooted in outdated assumptions about work and loyalty. Instead, Gen Z’s frequent job changes reflect a fundamental disconnect between their expectations and the realities of modern workplaces. As the first true digital natives, Gen Z has grown up in an environment of rapid technological change, instant feedback, and personalized experiences. They expect the same clarity, growth opportunities, and modern management styles in their careers. When companies fail to provide transparent career paths, continuous learning, and supportive leadership, young workers move on—not out of rebellion but as a rational response to an environment that no longer offers stability or development. Highlights 🔄 Gen Z’s frequent job changes stem from a mismatch between their expectations and outdated workplace systems, not disloyalty. 💻 As digital natives, Gen Z demands clear feedback, transparent career paths, and modern technology in the workplace. 📉 The decline in traditional entry-level jobs and the rise of AI have reshaped the job market, forcing Gen Z into contract roles and job hopping for advancement. 📚 Continuous learning is essential for Gen Z’s career survival, but many employers fail to deliver meaningful development opportunities. 💰 Economic realities, including student debt and economic downturns, drive Gen Z’s pragmatic job-hopping decisions for better pay and titles. ⚠️ High turnover causes significant organizational challenges, including lost knowledge, team burnout, and decreased productivity. 🚧 Frequent job changes can limit young workers’ deep skill development and long-term career stability, creating drawbacks alongside short-term benefits. Key Insights 🔍 Rethinking Loyalty: Gen Z’s job-hopping is less about a lack of commitment and more about a rational response to workplaces that fail to meet their developmental needs. This challenges traditional views on employee loyalty and calls for a reassessment of how organizations retain talent. Rather than blaming young workers, companies must examine whether their leadership and systems are aligned with modern expectations. 🧑‍💻 Digital Nativity Shapes Expectations: Growing up with instant information, seamless digital interfaces, and constant connectivity, Gen Z expects workplaces to offer similarly responsive environments. This means frequent feedback, intuitive tools, and personalized growth opportunities. Failure to meet these standards results in disengagement and departures, underlining the importance of technological and cultural modernization. 📉 Structural Changes in the Job Market: The disappearance of traditional entry-level roles due to automation and outsourcing has disrupted the typical career launchpad for young workers. This structural shift forces Gen Z into precarious contract roles with limited growth potential, making job-hopping necessary to gain experience and improve their positions. Organizations must rethink talent pipelines to create viable career paths for new entrants. 🤖 The AI Skills Gap: Rapid advancements in AI have rendered many formerly valuable skills obsolete, requiring workers to continuously upskill. Gen Z recognizes this necessity and prioritizes continuous learning. However, a significant gap exists between employers’ promises of professional development and the actual support provided, driving turnover and underscoring the need for companies to invest genuinely in employee growth. 💸 Economic Pressures Influence Career Moves: Beyond personal growth, financial stability is a critical motivator for Gen Z career decisions. Burdened by student debt and economic uncertainty, young workers pragmatically seek roles offering better compensation and advancement. This realism highlights the importance of competitive pay structures and transparent career progression in talent retention strategies. 🔄 Organizational Consequences of High Turnover: Frequent departures cause more than recruitment costs—they erode institutional knowledge, disrupt team dynamics, and reduce overall productivity. The resulting burnout and lowered morale among remaining employees create a vicious cycle that harms both company culture and business outcomes, emphasizing the strategic imperative to address root causes of turnover. ⚖️ Trade-offs of Job-Hopping for Employees: While switching jobs can lead to immediate gains in salary and title, it may hinder long-term career development by limiting opportunities to build deep expertise, complete complex projects, and form strong professional networks. The instability associated with frequent moves can negatively impact employees’ professional growth and sense of belonging, presenting a lose-lose scenario when organizations fail to provide stable growth environments. #genz #Leadership #Retention #TalentDevelopment #AI