Elli Peterson's TechCrunch

The biopharmaceutical industry faces a stark paradox: while R&D expenditures have surged beyond $200 billion annually across major players like Pfizer, Roche, and Novartis, the number of approved new molecular entities per billion dollars invested has declined approximately 80% since 1950—a phenomenon known as Eroom's Law. Phase II and Phase III clinical trial failure rates consistently exceed 65%, with oncology programs reaching 75% failure at pivotal stages. Regulatory submission cycles stretch 18-24 months from database lock to approval, while patent cliff pressures intensify as biosimilars erode blockbuster revenues. Against this backdrop, Generative AI in Biopharma has emerged not as speculative technology but as a quantifiable lever for reversing productivity decline across discovery biology, clinical development operations, and regulatory affairs. The economic case for Generative AI in Biopharma rests on measurable gains in three critical dimensions: cycle time compress...

When I first proposed deploying generative AI capabilities within our retail banking operations three years ago, the executive team's skepticism was palpable. The head of risk management questioned whether we were chasing a trend rather than solving real problems. Fast forward to today, and those same stakeholders are championing further investment in AI-driven workflows. The journey from doubt to adoption taught me more about Generative AI in Financial Operations than any whitepaper or vendor pitch ever could. These are the real lessons from the trenches—mistakes made, wins celebrated, and the unglamorous middle ground where transformation actually happens. Our initial proof of concept focused on transaction monitoring—a function drowning in false positives and compliance alerts. The AML team was processing roughly 18,000 alerts monthly, with only 4% warranting escalation. We partnered with our data science group to deploy a Generative AI in Financial Operations framework that co...

Three years ago, I stood in front of our executive team explaining why our conversion rates had plateaued despite significant investment in traditional personalization tools. Our customer lifetime value wasn't growing, cart abandonment remained stubbornly high, and our multichannel selling strategy felt increasingly fragmented. That presentation marked the beginning of our journey into generative AI—a journey that would fundamentally transform not just our technology stack, but how we approached customer experience optimization, inventory management, and checkout process engineering. The lessons we learned weren't found in vendor whitepapers or conference presentations. They emerged from real failures, unexpected successes, and countless iterations that taught us what actually works when deploying AI in a competitive e-commerce environment. Our first exploration into Generative AI in E-commerce began modestly—almost cautiously. We had a catalog of 47,000 products, many with ou...

When a leading international law firm's operations director first introduced generative AI into their contract review workflow, the initial pilot revealed something unexpected: the technology wasn't the bottleneck. The real challenge lay in changing decades-old habits around document handling, billable hours tracking, and client communication protocols. This experience mirrors what many corporate law departments and Am Law 100 firms are discovering as they integrate AI into legal operations. The transformation isn't just technological—it's cultural, procedural, and deeply human. The journey toward modernizing legal operations through artificial intelligence has become a defining challenge for corporate law firms worldwide. As firms like Clifford Chance and Latham & Watkins pioneer new approaches, the lessons learned offer invaluable guidance for legal departments at every stage of adoption. Generative AI for Legal Operations represents not merely a technological up...

Implementing generative AI in asset management represents one of the most significant technological transformations our industry has undertaken since the introduction of algorithmic trading. The potential benefits—enhanced research capabilities, more efficient client servicing, improved risk assessment, and accelerated alpha generation—are substantial. Yet the path from initial exploration to successful deployment is filled with technical, organizational, and regulatory challenges that can derail even well-funded initiatives. This comprehensive checklist distills best practices from firms that have successfully navigated this transformation, providing a structured framework for portfolio managers, technology leaders, and compliance professionals planning their own implementations. The foundation of successful Generative AI Asset Management deployment begins with strategic clarity about objectives, constraints, and success criteria. Too many firms approach AI as a technology solution s...

The adoption of Generative AI Legal Automation within corporate law firms is no longer a speculative investment—it is a fundamental restructuring of how legal work gets executed at scale. For attorneys managing discovery management, contract analysis, and due diligence simultaneously, understanding the technical mechanics behind these systems is critical. Unlike traditional rules-based automation that simply follows predetermined decision trees, generative AI systems leverage large language models (LLMs) trained on millions of legal documents to interpret, draft, and analyze text with contextual awareness. This shift represents a departure from keyword matching to semantic comprehension, fundamentally changing how legal professionals approach billable hours, case management, and client onboarding. At the heart of Generative AI Legal Automation lies the transformer architecture—a neural network design that excels at processing sequential data like legal text. When a corporate attorney ...

The telecommunications industry is undergoing a fundamental transformation as generative AI technologies move from experimental pilots to production-grade systems handling millions of customer interactions, network optimizations, and operational decisions daily. Unlike traditional rule-based automation, generative AI in telecom environments operates through sophisticated neural architectures that learn patterns from massive datasets, generate contextually relevant responses, and adapt to changing network conditions in real-time. Understanding the actual mechanisms behind these systems reveals why they represent such a significant leap forward for an industry grappling with exponential data growth, increasing service complexity, and rising customer expectations. The mechanics of Generative AI Telecommunications deployment differ substantially from consumer-facing AI applications. Telecom implementations require specialized model architectures that can process streaming network telemetr...