Elli Peterson's TechCrunch

Implementing artificial intelligence in internal audit represents one of the most significant transformations the profession has ever undertaken. Unlike traditional process improvements that modify existing workflows, AI integration fundamentally reimagines how audit work is performed, analyzed, and delivered. The complexity of this transformation demands a systematic approach that addresses technical, organizational, and governance considerations. A comprehensive checklist provides the structured framework necessary to navigate this complexity while ensuring that critical elements receive appropriate attention throughout the implementation journey. The following checklist distills insights from numerous successful and unsuccessful implementations of Generative AI Internal Audit initiatives across diverse industries and organizational contexts. Each item includes not just what to do, but why it matters and what happens when organizations skip or inadequately address these consideratio...

The journey to implementing intelligent automation in large organizations is rarely straightforward. While the promise of autonomous systems handling complex business tasks sounds compelling, the reality involves navigating technical challenges, organizational resistance, and unexpected edge cases that no whitepaper prepares you for. Over the past three years, I've witnessed dozens of enterprise deployments—some that transformed operations and others that became cautionary tales. These experiences reveal patterns that every organization should understand before embarking on their own automation initiatives. The gap between proof-of-concept demonstrations and production-ready systems is where most initiatives stumble. When organizations first explore Enterprise AI Agents , they often underestimate the complexity of integrating autonomous decision-making into established workflows. The difference between a successful deployment and a failed one usually comes down to how well teams pr...

The healthcare industry faces unprecedented challenges balancing rising costs, aging populations, workforce shortages, and increasing patient expectations for personalized care. Traditional approaches to these challenges have reached practical limits, prompting healthcare organizations to explore transformative technologies that can fundamentally reshape care delivery models. Intelligent systems that continuously monitor, analyze, and respond to patient conditions and operational dynamics offer promising solutions to many of these persistent challenges. This examination explores how these technologies are being deployed across healthcare settings and the tangible improvements they are delivering for patients, clinicians, and healthcare organizations. Healthcare providers are discovering that Continuous Ambient Intelligence addresses several critical pain points simultaneously. In hospital settings, these systems monitor patient vital signs, movement patterns, environmental conditions,...

Development teams across industries face mounting pressure to deliver increasingly complex software under tighter deadlines while maintaining code quality and security standards. Traditional development methodologies struggle to keep pace with these escalating demands, creating bottlenecks that slow innovation and strain technical resources. The emergence of AI-driven development approaches offers multiple pathways to address these challenges, transforming how organizations conceptualize, build, and maintain software systems in an era of accelerating technological change. Among the most promising solutions emerging from this transformation, AI Vibe Coding stands out for its ability to address multiple pain points simultaneously through natural language-driven development workflows. Rather than offering a single rigid methodology, this approach encompasses several distinct solution strategies that teams can adopt individually or combine based on their specific challenges. Understanding...

When a Fortune 500 financial institution embarked on its digital transformation journey three years ago, the governance team believed automation would be straightforward. They had budget approval, executive sponsorship, and a proven technology stack. Yet six months into deployment, audit trails were incomplete, compliance officers were frustrated, and the CFO was questioning the entire initiative. This story is not unique. Across industries, organizations are discovering that automating enterprise governance requires more than software licenses and technical expertise. It demands a fundamental rethinking of how controls, risks, and compliance processes interconnect across sprawling corporate ecosystems. The lessons emerging from these real-world implementations reveal patterns that textbooks rarely capture. Enterprise Governance Automation succeeds or fails based on organizational readiness, stakeholder alignment, and the willingness to confront uncomfortable truths about existing pro...

Implementing AI-assisted development workflows represents one of the most significant technical transitions an engineering organization can undertake. Unlike adopting a new framework or migrating to cloud infrastructure—changes that primarily affect technical architecture—this shift fundamentally alters how developers think, communicate, and create. The difference between successful adoption and expensive failure often comes down to methodical preparation and systematic rollout. This comprehensive checklist distills lessons from dozens of implementation projects into a structured roadmap, providing not just what to do but why each step matters and what happens when teams skip it. Before diving into tactical steps, understanding the strategic foundation is essential. AI-Driven Vibe Coding succeeds when organizations treat it as a workflow transformation rather than a tool installation. The checklist that follows reflects this philosophy, emphasizing preparation, cultural readiness, and...