Elli Peterson's TechCrunch

The corporate and institutional banking sector is no stranger to transformation. Lately, its focus has shifted towards Intelligent Automation in Finance to elevate processes such as trade finance, syndicated lending, and liquidity management to new heights. This automation not only promises to streamline operations but also to redefine how financial decisions are made, improving both speed and accuracy. In the realm of Intelligent Automation in Finance , automation technologies enable deeper data analysis and real-time insights. These technologies help banks like J.P. Morgan and Barclays transform compliance processes and reduce operational risks. Enhancing Data Analysis through Automation One of the core benefits of intelligent automation is its ability to process vast amounts of data quickly and accurately. This capability is critical in corporate banking, where analyzing client transactions and market trends swiftly can provide a significant competitive edge. By leveraging data-driv...

Three years ago, our treasury team faced a recurring nightmare: the monthly financial close stretched across twelve grueling days, requiring manual reconciliation of over 40,000 invoice records across seventeen subsidiaries. Payment exceptions piled up in spreadsheets, unapplied cash sat unmatched for weeks, and our Days Sales Outstanding climbed steadily toward 52 days. The breaking point came during a Q4 audit when we discovered $3.2 million in misallocated payments that had slipped through our manual controls. That crisis became the catalyst for our journey into intelligent automation—a journey that taught us hard lessons about what works, what fails, and how to build systems that genuinely adapt to the chaotic reality of corporate finance operations. The promise of Adaptive Enterprise AI sounded compelling in vendor presentations: self-learning systems that would streamline our Procure-to-Pay cycle, automate invoice processing, and accelerate reconciliation without constant human ...

Intelligent Order Lifecycle Automation is transforming corporate and investment banking by enhancing the speed and accuracy of transaction processing, a crucial requirement in today's complex financial ecosystem. The ability to streamline trade settlement while ensuring compliance with Basel III and other regulatory frameworks is at the forefront of bank operations such as those at JPMorgan Chase and Goldman Sachs. Intelligent Order Lifecycle Automation is more than just a technological upgrade; it is an operational evolution that adeptly handles trade volumes and regulatory demands while minimizing risks and errors. Efficiency Gains in Trade Settlement Trade settlement in investment banking often suffers from inefficiencies that Intelligent Order Lifecycle Automation can resolve. By adopting sophisticated algorithms and automated workflows, financial institutions can reduce the time taken from transaction initiation to settlement, thereby decreasing counterparty risk. Statistical...

The Procure-to-Pay cycle represents one of the most complex yet essential operational frameworks in enterprise procurement. From requisition creation through invoice payment, organizations managing thousands of suppliers and processing millions in annual spend face mounting pressure to eliminate manual touchpoints, reduce processing costs, and gain real-time visibility into spend under management. As procurement teams grapple with maverick spending, compliance requirements, and supplier relationship management demands, automation has evolved from a competitive advantage to an operational imperative. Modern enterprises are discovering that successful Procure-to-Pay Automation depends on strategic decisions across technology, process design, and organizational alignment. Leading platforms from providers like Coupa Software, SAP Ariba, and Jaggaer demonstrate that the difference between transformative results and incremental improvements often comes down to how organizations prioritize a...

Automotive manufacturers face an unprecedented convergence of operational pressures: labor shortages threatening production capacity, supply chain disruptions creating material volatility, regulatory demands tightening emissions and safety standards, and competitive forces demanding faster innovation cycles. Traditional approaches to these challenges—adding more workers, carrying larger inventory buffers, or extending lead times—no longer provide viable solutions. The complexity of modern automotive manufacturing requires intelligent systems that can navigate uncertainty, optimize across competing objectives, and adapt to rapidly changing conditions. The emergence of Intelligent Automation in Production offers multiple pathways for addressing these challenges. Rather than a single monolithic solution, automotive manufacturers are deploying targeted automation strategies tailored to their specific pain points and operational contexts. Understanding the range of approaches available—and...

Implementing automation in M&A advisory requires more than adopting new software—it demands a fundamental rethinking of how we approach deal flow, due diligence, valuation analysis, and integration planning. After guiding multiple advisory teams through this transformation, I've distilled the critical elements into a comprehensive checklist that addresses both technical implementation and the cultural shifts required for success. This structured approach ensures that automation enhances rather than disrupts your existing capabilities, ultimately delivering faster deal execution, deeper insights, and more predictable synergy realization. The framework presented here draws from implementations across bulge bracket firms and boutique advisories, encompassing transactions ranging from $50 million bolt-ons to multi-billion dollar transformational mergers. Each checklist item includes rationale based on what separates successful Intelligent Automation in M&A implementations from...

The journey toward intelligent automation in manufacturing is rarely a straight line. After spending years working alongside production teams, engineers, and plant managers implementing automation strategies across multiple facilities, I've witnessed firsthand how transformative these technologies can be—and how challenging the path often proves. The stories from the factory floor reveal patterns that no white paper or vendor presentation can fully capture: the moments when legacy SCADA systems refuse to communicate with new IIoT sensors, the shift supervisor who becomes your greatest advocate after initial resistance, and the unexpected efficiency gains that emerge from places you never anticipated. These real experiences shape how we approach digital transformation in modern manufacturing environments. One of the most instructive experiences came during a brownfield implementation at a mid-sized automotive components facility. The plant had been operating for nearly four decades,...

Three years ago, our quality control department was drowning in defect reports. Despite employing twenty inspectors working double shifts, we were still shipping products with microscopic weld inconsistencies that warranty claims revealed months later. The wake-up call came when a major automotive client threatened to pull a $40 million contract over a 0.3% defect rate. That crisis forced us to confront a truth many manufacturing veterans resist: human visual inspection, no matter how skilled or diligent, has biological limitations that modern production demands now exceed. Our journey into AI Visual Inspection Systems began not with technology enthusiasm but with operational desperation. What started as a pilot program on a single CNC machining line transformed into a company-wide quality revolution that cut our defect rate to 0.04%, reduced inspection labor costs by 68%, and ultimately saved that automotive contract. But the path from crisis to success taught us lessons that no vend...

Implementing advanced automation technologies across enterprise environments requires systematic planning and execution. Organizations that approach these initiatives with comprehensive preparation consistently achieve better outcomes than those that rush into deployment without adequate groundwork. A structured checklist provides the framework for navigating complex technical, organizational, and strategic considerations that determine whether automation efforts deliver transformational value or become costly experiments. The following comprehensive guide outlines essential steps for successful Intelligent Automation deployment, with detailed rationale explaining why each element matters and how it contributes to overall initiative success. This checklist reflects proven practices from organizations that have navigated automation journeys across diverse industries and operational contexts. Strategic Foundation Assessment Define Clear Business Objectives Before evaluating any automati...