德勤

Leadership research has paid limited attention to how distinct leadership behaviours operate jointly under conditions of high complexity in high-reliability settings. This study examines how transformational and directive leadership behaviours combine to influence team psychological safety and patient outcomes during complex surgical procedures. Drawing on situational strength theory and integrative leadership perspectives, we argue that the complementarity of people-focused (transformational) and task-focused (directive) leadership becomes especially functional when surgical complexity is high, enhancing team psychological safety and indirectly improving patient outcomes. We test these arguments using multi-source, time-sequenced data from 150 surgeries, including third-party observations of intraoperative leadership behaviours, team reports of psychological safety, objective indicators of surgical errors and blood loss, and patient-reported postoperative complications. Results show that under high surgical complexity, transformational and directive leadership interact to predict higher team psychological safety. In turn, psychological safety is associated with more observed surgical errors per hour but fewer and less severe post-discharge complications. Transformational leadership shows no unconditional main effects; rather, its benefits emerge only when paired with directive leadership under high complexity. These findings highlight the importance of contextual boundary conditions and demonstrate how leadership combinations shape performance processes and outcomes in high-reliability surgical environments.

Nonprofit Community Development Financial Institution loan funds (CDLFs) occupy a distinct position in efforts to address place-based inequality, channeling capital to underserved communities while maintaining financial sustainability. CDLFs were developed based on an integrated hybrid ideal in which social and commercial logics are inseparable in organizational identity and operations. Yet CDLFs face growing pressure from funders, regulators, and third-party rating systems to demonstrate "impact" through output-focused metrics, raising a central question: how do Community Development Loan Funds interpret evaluation and outcome measurement requirements, allocate responsibility for measurement work, and make tradeoffs among financial, operational, and social impact priorities in practice? Drawing on interviews with 39 CDLF leaders, we examine the factors that affect evaluation and outcome measurement practices. We find that CDLFs experience pressure through three mechanisms: (1) funder-driven compliance demands that prioritize outputs, (2) organizational cultures that define "the work" as lending rather than community development, and (3) organizational design choices in hiring, incentives, and technology that center on banking logic. These dynamics shape how place-based development capital is allocated and justified, with implications for accountability to borrowers in underserved neighborhoods. A field-level mechanism we term collective vulnerability helps explain persistence, because rigorous outcome measurement threatens individual organizational legitimacy.

Agentic AI represents a significant shift in how intelligence is applied within organizations, moving beyond AI-assisted tools toward autonomous systems capable of reasoning, decision-making, and coordinated action across workflows. As these systems mature, they have the potential to automate a substantial share of manual organizational processes, fundamentally reshaping how work is designed, executed, and governed. Although many organizations have adopted AI to improve productivity, most implementations remain limited to isolated use cases and human-centered, tool-driven workflows. Despite increasing awareness of agentic AI's strategic importance, engineering teams and organizational leaders often lack clear guidance on how to operationalize it effectively. Key challenges include an overreliance on traditional software engineering practices, limited integration of business-domain knowledge, unclear ownership of AI-driven workflows, and the absence of sustainable human-AI collaboration models. Consequently, organizations struggle to move beyond experimentation, scale agentic systems, and align them with tangible business value. Drawing on practical experience in designing and deploying agentic AI workflows across multiple organizations and business domains, this paper proposes a pragmatic framework for transitioning organizational functions from manual processes to automated agentic AI systems. The framework emphasizes domain-driven use case identification, systematic delegation of tasks to AI agents, AI-assisted construction of agentic workflows, and small, AI-augmented teams working closely with business stakeholders. Central to the approach is a human-in-the-loop operating model in which individuals act as orchestrators of multiple AI agents, enabling scalable automation while maintaining oversight, adaptability, and organizational control.

The accelerating adoption of large language models, retrieval-augmented generation pipelines, and multi-agent AI workflows has created a structural governance crisis. Organizations cannot govern what they cannot see, and existing compliance methodologies built for deterministic web applications provide no mechanism for discovering or continuously validating AI systems that emerge across engineering teams without formal oversight. The result is a widening trust gap between what regulators demand as proof of AI governance maturity and what organizations can demonstrate. This paper proposes AI Trust OS, a governance architecture for continuous, autonomous AI observability and zero-trust compliance. AI Trust OS reconceptualizes compliance as an always-on, telemetry-driven operating layer in which AI systems are discovered through observability signals, control assertions are collected by automated probes, and trust artifacts are synthesized continuously. The framework rests on four principles: proactive discovery, telemetry evidence over manual attestation, continuous posture over point-in-time audit, and architecture-backed proof over policy-document trust. The framework operates through a zero-trust telemetry boundary in which ephemeral read-only probes validate structural metadata without ingressing source code or payload-level PII. An AI Observability Extractor Agent scans LangSmith and Datadog LLM telemetry, automatically registering undocumented AI systems and shifting governance from organizational self-report to empirical machine observation. Evaluated across ISO 42001, the EU AI Act, SOC 2, GDPR, and HIPAA, the paper argues that telemetry-first AI governance represents a categorical architectural shift in how enterprise trust is produced and demonstrated.

This article demonstrates the transformative impact of Generative AI (GenAI) on actuarial science, illustrated by four implemented case studies. It begins with a historical overview of AI, tracing its evolution from early neural networks to modern GenAI technologies. The first case study shows how Large Language Models (LLMs) improve claims cost prediction by deriving significant features from unstructured textual data, significantly reducing prediction errors in the underlying machine learning task. In the second case study, we explore the automation of market comparisons using the GenAI concept of Retrieval-Augmented Generation to identify and process relevant information from documents. A third case study highlights the capabilities of fine-tuned vision-enabled LLMs in classifying car damage types and extracting contextual information. The fourth case study presents a multi-agent system that autonomously analyzes data from a given dataset and generates a corresponding report detailing the key findings. In addition to these case studies, we outline further potential applications of GenAI in the insurance industry, such as the automation of claims processing and fraud detection, and the verification of document compliance with internal or external policies. Finally, we discuss challenges and considerations associated with the use of GenAI, covering regulatory issues, ethical concerns, and technical limitations, among others.