Elli Peterson's TechCrunch

Despite the measurable success that leading automotive manufacturers have achieved through artificial intelligence deployment in procurement operations—from Toyota's predictive supplier risk systems to Continental AG's automated RFQ platforms—persistent misconceptions continue to delay adoption across much of the automotive supply chain. These myths, often rooted in outdated assumptions about AI technology or misunderstandings about how modern procurement systems actually function, lead sourcing leaders to either postpone necessary digital transformation or pursue implementations that fail because they're based on flawed premises. The consequences extend beyond missed cost reduction opportunities: organizations clinging to manual processes face growing competitive disadvantage as their rivals leverage machine learning to compress sourcing cycle times, improve supplier quality performance measured in parts-per-million defects, and navigate supply chain disruptions with great...

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...

Deploying Ambient AI Agents across enterprise operations represents one of the most significant technology transformations organizations can undertake. Unlike traditional automation projects with defined endpoints, ambient intelligence creates persistent, learning systems that evolve alongside your business. The complexity of this transformation demands methodical preparation, systematic execution, and continuous governance. Too many implementations fail not from technical inadequacy but from overlooking foundational requirements that determine whether autonomous AI operates as strategic asset or operational liability. This comprehensive checklist synthesizes insights from dozens of successful deployments, documenting the critical steps that separate transformative implementations from disappointing pilots. Each item addresses a specific failure point observed in real-world Ambient AI Agents projects, providing rationale grounded in operational experience rather than theoretical best ...

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...

The convergence of artificial intelligence with manufacturing operations has moved beyond pilot programs into measurable, enterprise-wide transformations. Today's manufacturing leaders face a critical inflection point: organizations that successfully implement Smart Manufacturing AI solutions are demonstrating quantifiable advantages in operational efficiency, product quality, and supply chain resilience. Yet adoption patterns reveal stark disparities in how companies translate AI investments into bottom-line results. Understanding the data behind these implementations provides manufacturing decision-makers with essential benchmarks for planning their own digital transformation initiatives and measuring success against industry standards. The manufacturing sector's embrace of Smart Manufacturing AI has accelerated dramatically, with recent industry studies showing that 72% of manufacturers now classify AI as either critical or very important to their competitive strategy. This...

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...

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...