The rise of autonomous, AI-driven agents in economic settings raises critical questions about their emergent strategic behavior. This paper investigates these dynamics in the cooperative context of a multi-echelon supply chain, a system famously prone to instabilities like the bullwhip effect. We conduct computational experiments with generative AI agents, powered by Large Language Models (LLMs), within a controlled supply chain simulation designed to isolate their behavioral tendencies. Our central finding is the "collaboration paradox": a novel, catastrophic failure mode where theoretically superior collaborative AI agents, designed with Vendor-Managed Inventory (VMI) principles, perform even worse than non-AI baselines. We demonstrate that this paradox arises from an operational flaw where agents hoard inventory, starving the system. We then show that resilience is only achieved through a synthesis of two distinct layers: high-level, AI-driven proactive policy-setting to establish robust operational targets, and a low-level, collaborative execution protocol with proactive downstream replenishment to maintain stability. Our final framework, which implements this synthesis, can autonomously generate, evaluate, and quantify a portfolio of viable strategic choices. The work provides a crucial insight into the emergent behaviors of collaborative AI agents and offers a blueprint for designing stable, effective AI-driven systems for business analytics.

ManpowerGroup reported in 2018 that 70% of companies interviewed in a survey said that they expected a skills shortage in field service personnel over the next 10 years. Field service workers are those who travel to locations to repair goods and services onsite. Among the contributing factors were an aging (and retiring) workforce and a lack of enthusiasm among millennials for field service careers. "For large enterprises, field service must address an entire lifecycle of a product or service," said Arka Prava Dhar, CEO and founder of Zinier, which provides field service AI solutions. Field service starts with work orders, then the work is done by the field service worker, then it's either verified or forwarded to an expert, Dhar said.

For some, artificial intelligence and data science are fantastic technologies that will benefit people and society. The point is, these latest innovations are evolving fast in the hotbeds of business, science and government, and it can be difficult for regular citizens and civil society to keep up, particularly nonprofits and others involved in addressing the spectrum of society's needs. What is philanthropy's role in the growth, use and regulation of these powerful and protean technologies? These are some of the questions Vilas Dhar considers in his role as president of the Patrick J. McGovern Foundation, a relatively young grantmaker whose late founder built a fortune in publishing and industry research, tracking the expanding computer industry in the late 20th and early 21st centuries. McGovern's International Data Group published a number of popular computer industry magazines such as Computerworld, PC World and InfoWorld.

TransUnion, a global information and insights company, processes more than 1,000 transactions per second, and Chief Information and Technology Officer Abhi Dhar says that each and every one is important. TransUnion's business customers rely on it for instant access to information that allows them to make immediate financing decisions. And consumers need and deserve fast answers, Dhar says. That's why he's using machine learning to bolster TransUnion's IT uptime. Machine learning, a subset of artificial intelligence, allows the company's IT team to predict problems in advance, so they can proactively resolve issues before outages occur.

Machine learning is not as easy to apply to finance as it is to a domain that has a lot of structure in it, Vasant Dhar, SCT Capital founder, professor at NYU's Stern School of Business, and director of the PhD program in the Center for Data Science at NYU, told Real Vision's The Interview. He thinks machine learning has been somewhat oversold in that sense. "It's not like you just put in data and magic appears at the other end," he said. "There's a lot of care that has to go into how you formulate the problem, how you create the data, whether you have a process in place." However, he did say that the technology is perfect for higher-frequency trading.

Zinier, a company that uses artificial intelligence (AI) to automate field work, has raised $90 million in a Series C funding round, bringing its total amount raised to $120 million. The startup plays heavily in the telecom sector -- 80% of its existing customers are in the space, including network operators, equipment vendors and suppliers, contractors, and engineers, according to Zinier's co-founder and CEO Arka Dhar. That's also reflected by the firms that returned to invest in this latest round, including Nokia-backed NGP Capital and Qualcomm Ventures. New investor Iconiq Capital led the round with participation from Tiger Global Management, Accel, Founders Fund, and Newfund Capital. "Zinier is going to play a very, very important role there," Dhar said in a phone interview.

Artificial Intelligence has spread rapidly across markets in recent years as traders constantly strive to gain the upper hand, while regulators have given a guarded welcome to the cutting-edge technology. High-frequency trading propelled by algorithms has reigned over the past decade, as banks and funds take advantage of small price fluctuations on many markets to carry out thousands of deals in a fraction of a second. Complex mathematical equations have long been used to conduct certain operations -- for example, selling or buying a security if it breaches a certain level. Yet algorithms have come under fierce criticism over "flash crashes", such as a dizzying slump in the British pound in October 2016 that was widely blamed on high-frequency deals. Artificial Intelligence now seeks to take trading into new realms, where "machine learning" (ML) software compares dozens of databases in the blink of an eye to monitor risk.

When can we truly trust machines? From SIRI to self-driving cars, Google's search algorithms to autonomous weapons and drones, the past few decades have witnessed some of the fastest, almost meteoric, rises in Artificial Intelligence (AI). Today, as different nation-states make choices around data, AI is shaping the privacy rights and governance rhetoric. Conversely, the "data" policies that nation-states form are also shaping the type of AI that emerges. With this backdrop, Vasant Dhar, Professor, Stern School of Business, New York University, discussed the ascent of Artificial Intelligence and its uses and implications for nation-states during a Development Seminar at Brookings India.

When can we truly trust machines? From SIRI to self-driving cars, Google's search algorithms to autonomous weapons and drones, the past few decades have witnessed some of the fastest, almost meteoric, rises in Artificial Intelligence (AI). Today, as different nation-states make choices around data, AI is shaping the privacy rights and governance rhetoric. Conversely, the "data" policies that nation-states form are also shaping the type of AI that emerges. With this backdrop, Vasant Dhar, Professor, Stern School of Business, New York University, discussed the ascent of Artificial Intelligence and its uses and implications for nation-states during a Development Seminar at Brookings India.

Artificial intelligence (AI) is cropping up everywhere in business, as organizations deploy the technology to gain insights into customers, markets and competitors, and to automate processes in almost every facet of operations. But AI presents a wide range of hidden dangers for companies, especially in areas such as regulatory compliance, law, privacy and ethics. There is little visibility into how AI and machine learning technologies come to their conclusions in solving problems or addressing a need, leaving practitioners in a variety of industries flying blind into significant business risks. The concerns are especially relevant for companies in industries such as healthcare and financial services, which have to comply with a number of government and industry regulations. "Context, ethics, and data quality are issues that affect the value and reliability of AI, particularly in highly regulated industries," says Dan Farris, co-chairman of the technology practice at law firm Fox Rothschild, and a former software engineer who focuses his legal practice on technology, privacy, data security, and infrastructure matters.