USA Trends and Developments Contributed by: Jeffrey Harvey, Randall Parks, Andrew Geyer and Cecilia Oh, Hunton Andrews Kurth LLP
Introduction In the United States outsourcing industry, devel - opments are largely incremental in 2025 with three super-trends (the same as noted in 2024) continuing their trajectories: • migration to digital operating models to capture new opportunities and savings, including through the increased use of machine learning and AI- based tools and solutions; • continued and significant investment in data pro - tection, cybersecurity and compliance resources in response to threats to digital infrastructure; and • reworking of more traditional contracting models to increase agility and prioritise results. These super-trends manifest themselves in ten key long-term strategic evolutions: • a shift to “as a service” and cloud offerings; • a shift from outsourcing service providers to managed service providers (although this shift is primarily one in nomenclature and not in practice); • a fairly massive uptick in the adoption and market - ing of generative AI-based solutions; • a general uptick in Build Operate Transfer (BOT) and Global Capability Centre (GCC) models; • the digital transformation of traditional business models and the conversion of data flows into revenue-generating products and analytical tools; • evolving security services and cybersecurity/data protection requirements; • a shift to “outcome-based” commercial models; • continuing swings in emphasis between value/ innovation and cost savings, driven by industry- specific economic conditions and opportunities; • a bias towards multi-sourcing and shorter contract durations; and • a reduced focus on achieving savings through headcount reductions and an increased focus on efficiency gains and process improvements through the use of skilled labour and the adoption of new and innovative technologies, including vari - ous forms of AI. Digital Operating Models Evolutions in technology over the past decade have dramatically changed the way information technol -
ogy services are delivered and consumed, and how firms go to market. “As a service” and cloud-based offerings continue to multiply and take market share from legacy models. These products appeal to cus - tomers who prefer to buy more-or-less standardised functionality delivered through a web browser, rather than procure and manage a complicated network of hardware, software, employees and contractors. The delivery and pricing models for these services assume that there is little variation in the services, service lev - els and the related risk allocations and contract terms. While the largest cloud and as-a-service providers are reluctant to heavily negotiate and alter the terms of their existing agreements, middle-market providers (who may leverage the services of the larger provid - ers as part of their offerings) are much more likely to do so. Providers also are increasingly integrating into their offerings robotic process automation (RPA), machine learning and various other forms of AI, including gen - erative AI and agentic AI. Most outsourcing transac - tions now include some form of these tools, although the marketing of these tools in large outsourcing deals generally outweighs their productivity (at least as of the date of publication of this guide). RPA typically is delivered through a software platform and customised machines/robots capable of performing tasks often handled by lower-cost human operators. Machine learning is geared at improving internal processes and procedures based on computers that are able to learn and improve without continued manual intervention. Generative AI takes any number and types of inputs and produces a net new output based on a particu - lar use case; agentic AI solutions operate without the human intervention required of traditional AI and exhibit far more autonomy. RPA and machine learning are relatively mature in the outsourcing space, while generative AI and agentic AI are a lot more “buzzy” at the moment. Outsourcing providers are ultimately slower in pace when adopting newer technologies for their customer base, so the majority of providers are simply promising continued investment in the genera - tive AI and agentic AI space, rather than any specific implementation of proven generative AI solutions (or, they are adopting these technologies in a “back- office” capacity to increase their own efficiency in pro - viding more traditional services). In certain instances,
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