arXiv subjects

Alejandro R. Jadad

Publications and source records attributed to Alejandro R. Jadad.

4 recordsLinked to original sources

arXiv2026

Leading Across the Spectrum of Human-AI Relationships: A Conceptual Framework for Increasingly Heterogeneous Teams

What shapes a consequential decision when human and artificial intelligence work on it together? The answer is becoming harder to see. A decision may look human-led after AI has set the frame, or appear automated while human judgment still carries decisive force. This paper offers a leadership-facing spectrum to see those relationships within a bounded mandate: Pure Human, Centaur (human-dominant, with AI in the loop), Co-equal, Minotaur (AI-dominant, with humans in the loop), and Pure AI. The spectrum asks where leadership work occurs: who frames the problem, who redirects the work, and who can answer for what follows. The five positions are landmarks that help leaders recognize configurations as they layer, drift, or change in a single decision. The central risk is misrecognition: leaders may keep a human-centered story in place after decision-shaping authority has shifted elsewhere. They may believe oversight remains meaningful when it has become ceremonial, or keep humans in the loop when their involvement could make the decision worse. The framework introduces co-adaptability, the capacity of a configuration to improve as human and non-human participants adjust together, and places it within heterogeneous teaming, where participants may vary by number, substrate, model architecture, capability, speed, memory, and form of participation. The aim is practical: to help strategic leaders and those designing or deploying AI systems recognize the configuration at work, notice when it shifts, and judge whether it fits the decision before them. These configurations will shape how power, responsibility, and trust are distributed in organizational life. Whether the futures they help create remain governable and worth inhabiting will depend on leaders who can see, early enough, where and how consequential decisions are actually being shaped.

cs.AI↗

arXiv2025

Making LLMs Reliable When It Matters Most: A Five-Layer Architecture for High-Stakes Decisions

Current large language models (LLMs) excel in verifiable domains where outputs can be checked before action but prove less reliable for high-stakes strategic decisions with uncertain outcomes. This gap, driven by mutually reinforcing cognitive biases in both humans and artificial intelligence (AI) systems, threatens the defensibility of valuations and sustainability of investments in the sector. This report describes a framework emerging from systematic qualitative assessment across 7 frontier-grade LLMs and 3 market-facing venture vignettes under time pressure. Detailed prompting specifying decision partnership and explicitly instructing avoidance of sycophancy, confabulation, solution drift, and nihilism achieved initial partnership state but failed to maintain it under operational pressure. Sustaining protective partnership state required an emergent 7-stage calibration sequence, built upon a 4-stage initialization process, within a 5-layer protection architecture enabling bias self-monitoring, human-AI adversarial challenge, partnership state verification, performance degradation detection, and stakeholder protection. Three discoveries resulted: partnership state is achievable through ordered calibration but requires emergent maintenance protocols; reliability degrades when architectural drift and context exhaustion align; and dissolution discipline prevents costly pursuit of fundamentally wrong directions. Cross-model validation revealed systematic performance differences across LLM architectures. This approach demonstrates that human-AI teams can achieve cognitive partnership capable of preventing avoidable regret in high-stakes decisions, addressing return-on-investment expectations that depend on AI systems supporting consequential decision-making without introducing preventable cognitive traps when verification arrives too late.

cs.AI↗

arXiv2024

Cracking the code: Lessons from 15 years of digital health IPOs for the era of AI

Introduction: As digital health evolves, identifying factors that drive success is crucial. This study examines how reimbursement billing codes affect the long-term financial performance of digital health companies on U.S. stock markets, addressing the question: What separates the winners from the rest? Methods: We analyzed digital health companies that went public on U.S. stock exchanges between 2010 and 2021, offering products or services aimed at improving personal health or disease management within the U.S. market. A search using Google and existing IPO lists identified eligible companies. They were categorized based on the presence or absence of billing codes at the time of their initial public offering (IPO). Key performance indicators, including Compound Annual Growth Rate (CAGR), relative performance to benchmark indices, and market capitalization change, were compared using Mann-Whitney U and Fisher's Exact tests. Results: Of the 33 companies analyzed, 15 (45.5%) had billing codes at IPO. The median IPO price was $17.00, with no significant difference between groups. Those with billing codes were 25.5 times more likely to achieve a positive CAGR. Their median market capitalization increased 56.3%, compared to a median decline of 80.1% for those without billing codes. All five top performers, in terms of CAGR, had billing codes at IPO, whereas nine of the ten worst performers lacked them. Companies without billing codes were 16 times more likely to experience a drop in market capitalization by the study's end. Conclusion: Founders, investors, developers and analysts may have overestimated consumers' willingness to pay out-of-pocket or underestimated reimbursement complexities. As the sector evolves, especially with AI-driven solutions, stakeholders should prioritize billing codes to ensure sustainable growth, financial stability, and maximized investor returns.

q-fin.PM↗

arXiv2024

The Foundations of Computational Management: A Systematic Approach to Task Automation for the Integration of Artificial Intelligence into Existing Workflows

Driven by the rapid ascent of artificial intelligence (AI), organizations are at the epicenter of a seismic shift, facing a crucial question: How can AI be successfully integrated into existing operations? To help answer it, manage expectations and mitigate frustration, this article introduces Computational Management, a systematic approach to task automation for enhancing the ability of organizations to harness AI's potential within existing workflows. Computational Management acts as a bridge between the strategic insights of management science with the analytical rigor of computational thinking. The article offers three easy step-by-step procedures to begin the process of implementing AI within a workflow. Such procedures focus on task (re)formulation, on the assessment of the automation potential of tasks, on the completion of task specification templates for AI selection and adaptation. Included in the article there are manual and automated methods, with prompt suggestions for publicly available LLMs, to complete these three procedures. The first procedure, task (re)formulation, focuses on breaking down work activities into basic units, so they can be completed by one agent, involve a single well-defined action, and produce a distinct outcome. The second, allows the assessment of the granular task and its suitability for automation, using the Task Automation Index to rank tasks based on whether they have standardized input, well-defined rules, repetitiveness, data dependency, and objective outputs. The third, focuses on a task specification template which details information on 16 critical components of tasks, and can be used as a checklist to select or adapt the most suitable AI solution for integration into existing workflows. Computational Management provides a roadmap and a toolkit for humans and AI to thrive together, while enhancing organizational efficiency and innovation.

cs.SE↗