ISG Provider Lens™ Digital Engineering Services - Integrated Customer/User Engagement - U.S. 2025

The digital engineering services (DES) market in the U.S. experienced significant advancements in 2024, particularly with the integration of generative and agentic artificial intelligence (AI) technologies. Generative AI usecases have quickly proliferated across all aspects and phases of engineering- spanning from R&D, design, build, test, run/ operations, to aftermarkets, field services, and ultimately in the digital business platformization services. These innovations have transformed various sectors, including chip design, precision manufacturing, life sciences, and service platforms in banking, financial services, insurance, and retail. Hyperscalers—large-scale cloud service providers—have played a pivotal role in this transformation by offering advanced AI services tailored to engineering design applications.

GenAI services from Google, Azure, AWS and other providers, have been employed to accelerate design processes, enhance simulation accuracy, scale complex designs, and improve testing methodologies. The semiconductor industry for instance, at the top of the priority list in the US now, has embraced AI to manage the increasing complexity of chip design. A leading developer firm for semiconductor design for instance, introduced Agent Engineer, a technology that enables AI agents to assist human engineers in intricate tasks such as testing circuit designs. This approach addresses the challenges posed by designing AI server systems with thousands of interconnected chips, enhancing R&D capacity
without necessitating larger engineering teams. Other large providers have unveiled new technology stacks and architecture, e.g. 3.5D XDSiP from Broadcom, to enhance semiconductor speeds, meeting the growing demand for generative AI infrastructure. These advancements enable custom chip customers to increase memory and improve performance by directly connecting critical components using advanced packaging techniques.

In manufacturing, AI has been pivotal in automating processes and enhancing efficiency. Humanoid robots have the potential to become widespread in manufacturing within the next 1-3 years, significantly sooner than previously estimated. New software tools are being developed to help humanoid robots better navigate their environments, suggesting favorable economic benefits for businesses. In the pharma, R&D and healthcare and life sciences sectors, AI has been instrumental in drug discovery and development, fast and rigorous testing, genetic and hyper- personalized medicines, new medicine delivery mechanisms, healthcare services like monitoring and rapid emergency management. Agentic AI systems can autonomously analyze vast datasets and support research and references, to identify potential drug candidates, predict molecular interactions, and optimize clinical trial designs. This accelerates the development pipeline and reduces costs, leading to more efficient delivery of new therapies to the market.

Vertical Market Developments

The BFSI sector has also leveraged GenAILLMs, SLMs, fine-tuned models and knowledgepatterns, to enhance service delivery and operational efficiency. Fund managers, for instance, have adopted AI to process market data and  generate trading signals, prompting other fund managers to intensify their AI efforts. This has increased demand for coding talent and high-performance computing resources. JPMorgan Chase, for example, reported a 10 to 20 percent increase in software engineers’ efficiency by utilizing a coding assistant tool. This development has enabled the firm to reassign engineers to other projects, focusing on high-value AI and data initiatives. The company has also identified more than 400 AI use cases, which are projected to increase to 1,000 by next year. In the retail, e-commerce and other customer services-heavy industries, AI-driven design and simulation tools have enabled companies to optimize supply chains, enhance CX and personalize marketing strategies. GenAI models provide self-service support,  conversational AI interfaces and knowledge search on products and services to analyze consumer behavior to predict trends and tailor product offerings and recommendations. Agentic AI systems manage inventory and logistics autonomously, reducing operational costs and improving efficiency.

Hyperscalers have been instrumental in democratizing access to advanced AI tools, enabling organizations to integrate AI into their engineering processes without substantial infrastructure investments. Companies like Nvidia have introduced powerful GPUs and software platforms that facilitate the development and deployment of AI applications
across various industries. Nvidia’s annual developer conference showcased new products, including GPUs with greater memory capacity and faster chips, highlighting their commitment to advancing AI hardware and software.

Despite the advancements, integrating AI into engineering design presents challenges. Data quality and availability  are critical, as AI models require extensive datasets for training. Ensuring data privacy and security is paramount,
especially in sectors like BFSI and life sciences. Additionally, the energy consumption of AI workloads has raised sustainability concerns. Hyperscalers are addressing these issues by investing in energy-efficient data centers and
developing specialized hardware to optimize AI computations. The challenges of AI-skilled talent and also the culture and mindset shifts required for existing aging workforce in US, are also significant hurdles. But there are concerted efforts in way to overcome them. Hence, the trajectory of AI integration into engineering R&D and digital design  services indicates a future where AI is ubiquitous, operating seamlessly in the background to enhance various  processes. The continuous evolution of AI technologies, coupled with the support from hyperscalers, is expected to drive further innovations across industries, leading to more efficient, cost-effective, and sustainable engineering solutions.

The digital platform engineering services market in the United States has also come a long way fast, driven by the integration of generative and agentic artificial intelligence (AI) technologies. From hardware platforms to software engineering-driven platforms, to experience engineering, to complete platformization e.g. business reengineering, are becoming mainstream winning examples of all things digital. These advancements have permeated various sectors, including manufacturing, life sciences, banking, financial services, insurance, and retail, leading to enhanced productivity, flexibility and agility with composable business and services platforms, increased cost efficiencies, innovative solutions, and redefined business processes. In platform engineering, genAI and recent agentic AI technology stacks and development platforms have been harnessed to optimize and automate processes and services, optimize knowledge search and leverage, workflows and system architectures, and facilitate rapid development and deployment of applications. AI has been instrumental in enhancing product research and development platforms and
processes too. Clients have utilized generative AI to summarize procedural documents and generate rich media, such as animations, for e-learning purposes. These GenAI and agentic usecases of knowledge enablement not only
streamlines the dissemination of complex information but also aids in training, GRC and compliance, ensuring that personnel are wellinformed about procedural protocols.

The oil and gas industry has experienced a revolution in drilling operations through AI integration. AI has aided in steering drill bits, predicting potential problems and enabling drilling in previously unfeasible areas.

Companies have used AI to improve drilling efficiency and capital allocation, anticipating an increase in oil and gas production. Additionally, they have employed AI-powered drones for remote monitoring, reducing repair downtime
and enhancing operational continuity. In the energy industry, AI-driven data centers have prompted a reevaluation of power consumption and sustainability practices. As GenAI demands increased power, data centers are seeking more
reliable, cleaner energy solutions.

GenAI and agentic AI solutions have found major application opportunities in aftermarket services, field operations and production systems across various industries in the U.S. Industries such as manufacturing, automotive, pharmaceuticals, life sciences, oil and gas, energy and utilities have experienced notable advancements by leveraging GenAI and agentic tool stacks, leading to enhanced operational efficiencies, cost reductions and innovative service offerings. For instance, Accenture launched AI Refinery™ for Industry, which includes a collection of 12 industry agent
solutions designed to help organizations rapidly build and deploy a network of AI agents.

These agents enhance the workforce, address industry-specific challenges and drive business value quickly. Such industry solutions can significantly reduce the time required to build and derive value from agents — from months or
weeks to days.

There have been many notable developments in digital engineering services besides the rise of AI, as the sector and provider profiles that follow will show.

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