Elli Peterson's TechCrunch

The finance automation market buzzes with vendor promises of 70% efficiency gains, three-day closes, and elimination of manual reconciliations. Yet a sobering reality persists beneath the marketing enthusiasm: independent research indicates that 60-65% of finance automation initiatives fail to deliver promised benefits, with many organizations abandoning implementations after investing millions. This failure rate should alarm any CFO considering transformation, but the underlying causes reveal essential truths about successful automation that vendors rarely discuss. After analyzing dozens of Record to Report Automation deployments across industries, a clear pattern emerges: technology capability rarely causes failure. Instead, organizations stumble over fundamental misconceptions about automation's nature, unrealistic expectations about implementation complexity, and critical underinvestment in organizational change. This contrarian perspective challenges conventional automation w...

The landscape of enterprise commerce is undergoing a seismic shift as organizations reimagine how orders flow from inception to fulfillment. Traditional manual processes that once defined operational workflows are rapidly giving way to intelligent systems that anticipate demand, orchestrate resources, and execute transactions with minimal human intervention. This transformation is not merely incremental improvement but a fundamental reimagining of how businesses interact with customers, manage inventory, and coordinate complex supply chain ecosystems. As we stand at the threshold of an era defined by unprecedented technological convergence, understanding the trajectory of these systems becomes essential for leaders seeking competitive advantage. The acceleration of Order Management Automation over the next five years will reshape every dimension of commercial operations. Organizations that historically relied on siloed systems and manual coordination are discovering that integrated, i...

The landscape of business technology is undergoing a seismic shift as organizations move beyond traditional automation toward genuinely autonomous systems. As we stand at the threshold of a new era, understanding the trajectory of artificial intelligence in the enterprise context becomes essential for business leaders planning their digital transformation strategies. The systems emerging today will fundamentally reshape how organizations operate, make decisions, and compete in global markets over the next half-decade. The emergence of Enterprise AI Agents represents more than an incremental improvement in business software—it signals a paradigm shift in how work itself is conceived and executed. These intelligent systems are poised to evolve from simple task executors into strategic partners capable of complex reasoning, contextual understanding, and adaptive learning. The next five years will witness unprecedented advances in agent capabilities, deployment models, and integration arc...

The evolution of artificial intelligence has brought us to a critical juncture where the limitations of stateless, reactive systems are becoming increasingly apparent. Traditional AI implementations operate in isolation, processing inputs and generating outputs without any memory of previous interactions. This fundamental constraint has driven the development of a new paradigm: systems that can remember, learn, and evolve across multiple sessions. Understanding the inner workings of these advanced architectures reveals why they represent such a significant leap forward in AI capability and practical application. The concept of Persistent AI Agents fundamentally changes how we approach automation and intelligent systems. Unlike their predecessors, these agents maintain continuity across sessions, accumulating knowledge and refining their operations over time. This persistence transforms AI from a tool that provides isolated responses into a collaborative partner that understands contex...

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...

Three years ago, I watched our M&A team miss a critical window on a $2.3 billion senior debt offering because our due diligence workflow relied on seventeen separate Excel files, six email chains, and manual data entry across three legacy systems. The deal closed eventually, but the reputational damage and the hours burned on reconciliation taught us something vital: in investment banking, speed and accuracy are not opposing forces when you have the right automation infrastructure. That near-miss became the catalyst for our firm's transformation journey into intelligent process automation. What we discovered over the following thirty-six months fundamentally reshaped how we approach trade execution, risk management, and client onboarding. Intelligent Automation in Investment Banking is not about replacing bankers with algorithms; it is about eliminating the friction that prevents skilled professionals from doing what they do best. The lessons we learned came at a cost—in time,...

The landscape of investment banking has undergone a seismic shift with the advent of intelligent automation. This transformation is not merely a trend but a necessity driven by rising client expectations, enhanced regulatory scrutiny, and the relentless pursuit of operational efficiency. As firms like J.P. Morgan and Goldman Sachs race to innovate, they increasingly rely on automation to streamline their processes, mitigate risks, and ultimately deliver superior service. In this article, we will explore how intelligent automation is integrated into trade execution and risk management, paving the way for more agile and compliant banking practices. Understanding Intelligent Automation in Investment Banking begins with recognizing the vital roles of trade execution and risk management within the sector. Both functions are critical to maintaining competitive advantage while ensuring adherence to regulatory standards. By employing automation technologies, investment banks can significantly...

Intelligent Automation has become a buzzword, but the reality of its implementation can often be complex and layered. Having worked on several projects involving Intelligent Automation, I have witnessed firsthand the profound impact it can have on project delivery. This article aims to share invaluable lessons learned through real-world applications, showcasing how organizations can leverage Intelligent Automation effectively. In today's fast-paced business landscape, companies are turning to Intelligent Automation as a means to enhance efficiency and reduce costs. However, the journey is not without its challenges. From navigating resistance to change among employees to ensuring seamless technology integration, every organization has a unique story. Understanding the Need for Intelligent Automation The first lesson learned is the critical importance of understanding why Intelligent Automation is needed. In my experiences, organizations often struggle to articulate their pain poin...

In an era where customer expectations are soaring, the integration of Intelligent Automation into customer support strategies has become not just an advantage but a necessity. Companies are now leveraging technology to improve efficiency, enhance customer satisfaction, and streamline operations. Through my journey in the tech industry, I've witnessed how Intelligent Automation can reshape the customer experience. As organizations embark on this transformative path, exploring practical use cases and learning from the industry’s best practices can lay the foundation for a successful implementation. This becomes even clearer when examining case studies from various sectors that illustrate the benefits of adopting Intelligent Automation in their operations. The Importance of Intelligent Automation in Customer Support Intelligent Automation plays a critical role in refining the customer support process. It enables organizations to handle vast volumes of inquiries without compromising t...

When a mid-sized pharmaceutical distributor faced a crisis after shipping expired medications to three major hospital networks, the root cause wasn't human negligence or system failure in the traditional sense. It was the invisible gap between their warehouse management software and their compliance tracking system—a gap that Intelligent Automation could have bridged seamlessly. This incident, which cost the company over two million dollars in recalls and penalties, represents just one of countless scenarios where organizations learned the hard way that manual coordination between digital systems creates dangerous blind spots. The stories that follow reveal the transformative power of automated intelligence when applied thoughtfully, and the costly mistakes that precede genuine adoption. The journey toward Intelligent Automation rarely begins with visionary leadership or strategic planning. Instead, it typically starts with a crisis, a near-miss, or a competitor's sudden advan...