Tech & Sourcing @ Morgan Lewis

Organizations are increasingly leveraging a range of delivery models to provide business services, including internal teams, Global Business Services (GBS), Global Capability Centers (GCCs), outsourcing providers, and managed services. As organizations combine these approaches with AI-enabled delivery, important governance considerations include accountability, decision making, performance, and contractual obligations across an interconnected ecosystem of suppliers, internal teams, and AI systems.

As AI drives demand for greater computing power, businesses have more options for accessing the technology needed to support AI workloads. One option is GPU as a service (GPUaaS), which allows customers to access specialized computing resources without purchasing the underlying hardware. While GPUaaS shares similarities with traditional cloud services, it also invites comparison with data center colocation arrangements.

As AI continues to shape the technology industry, computing equipment housed in data centers is evolving to support a broad range of workloads. While traditional general-purpose servers remain essential for everyday business operations, AI workloads may run on conventional servers, accelerator-equipped servers, or more integrated computing systems. The resulting differences in equipment architecture and deployment can provide useful context for understanding data center operation and colocation arrangements.

A company licenses an artificial intelligence–enabled service after evaluating its functionality, security, performance, and compliance characteristics. Six months later, the provider upgrades the underlying model or replaces it altogether. The product may look the same, but the technology beneath it may have changed in ways that affect performance, data processing, regulatory compliance, or the customer’s downstream obligations.

A teenager opens an app. The feed is personalized based on earlier activity. The next video starts automatically. A companion chatbot remembers yesterday’s conversation. To the user, these may appear to be features of one service. Under a new package of California laws, they are separate design choices that can carry different compliance obligations.

As artificial intelligence (AI) becomes increasingly embedded in development services, outsourcing arrangements, and other commercial and technology transactions, customers and vendors are confronting a deceptively simple question: How should risk be allocated when a deliverable is created using AI?

Join intellectual property (IP) partner Colleen Ganin for Fashioning the Law: A Panel Discussion and Q&A, presented by Volunteer Lawyers for the Arts and Morgan Lewis. Colleen will be joined by Ayisha Morgan, General Counsel of Elite World Group, and David Eshmoili, General Counsel and Corporate Secretary of David Yurman, for a discussion of the legal and business issues shaping the fashion industry, from brand protection and intellectual property to the growing impact of AI on fashion and creative businesses.

As AI continues to be incorporated into service offerings, outsourcing arrangements, and other commercial transactions, companies may find they are contracting for more than a discrete technology tool. They may also be creating an operational dependency on a provider’s AI models, data practices, supporting technology, and continued performance.

Cyber insurance traditionally assumes a familiar sequence: an attacker gains access to a system, data is stolen or operations are disrupted, and a claim follows. Autonomous systems may complicate every step, including whether the event qualifies as a cyber incident at all.

A traveler asks an AI concierge to book a quiet oceanfront hotel in Naples for the weekend. Only after the confirmations arrive does the traveler discover that the bot selected Naples, Florida, while the flight is headed to Rome. The hotel is nonrefundable. As AI concierge services move beyond suggesting restaurants and into booking flights, hotels, rental cars, and activities, errors can quickly result in actual charges and disputes over responsibility. When a bot selects the wrong dates, overlooks a cancellation restriction, or books a hotel it was intended merely to recommend, the question becomes who is responsible for the error and the resulting costs.