ISG Provider Lens™ AWS Ecosystem Partners - AWS Enterprise Data Modernization and AI Services - U.S. 2025

Driving sustainable growth and building intelligent enterprises through data-driven AWS modernization

The AWS ecosystem in the U.S. is undergoing a significant transformation, with service providers increasingly focusing on delivering advanced, value-driven cloud capabilities. This evolution is marked by substantial investments in emerging technologies, particularly generative AI (GenAI), agentic AI and data analytics, alongside continued emphasis on large-scale cloud migration, modernization and enterprise application support. Providers are developing highly specialized, often industryspecific solutions, and deepening their strategic alignment with AWS to deliver measurable business outcomes for their clients. The market is characterized by a strong drive towards innovation, operational efficiency and a refined approach to cloud adoption, moving beyond basic infrastructure provision to holistic, AIinfused digital transformations.

Accelerating cloud modernization and data center exits: Service providers are intensely focused on accelerating large-scale cloud migration and modernization initiatives, particularly driving enterprise data center exits and mainframe modernization. This involves deploying migration factories and leveraging advanced automation to achieve predictable and rapid transition of virtual machines and complex applications to AWS environments. Leading providers consider cloud migration and modernization their core business, highlighting its criticality in their revenue streams. The market is also shifting toward outcome-based and risk-sharing commercial models, where service providers commit to delivering specific business outcomes and often share in the financial risks or rewards with their clients.

Deep expertise and strategic advisory alignment: The emphasis to fully leverage AWS services such as Amazon Elastic Kubernetes Service (EKS) and Amazon Elastic Container Service (ECS) for modern application landscapes is supported by substantial internal expertise through AWS Ambassadors, whose deep technical knowledge and engagement in pre-sales cycles demonstrably increase win rates and build client confidence. Few leaders reinforce their significant market position by holding a high ranking for AWS Migration Acceleration Program (MAP), indicating a profound level of strategic alignment and early access to new AWS initiatives. Joint investment programs and the establishment of dedicated teams with AWS, sometimes targeting ambitious multiyear revenue goals, exemplify this deep, strategic alignment and long-term commitment to the AWS ecosystem in markets including the U.S. Holding premier sponsorship status for major AWS events like re:Invent further signals substantial investment and a deepening strategic engagement with AWS.

A key differentiator among U.S. service providers is their increasing ability to act as neutral advisors in complex cloud transformation scenarios, guiding clients through strategic decisions without inherent bias toward specific solutions. This advisory role often extends to embedding GenAI and agentic AI capabilities into strategic planning, implementation and post-migration optimization. Providers are leveraging AWS AI services to develop AI-powered tools for use cases ranging from automated code generation and anomaly detection to enhancing developer productivity.

Advanced regulated and other verticalized cloud solutions: Many providers are actively involved as launch providers for new AWS features and services, including those related to sovereign cloud and specific industry solutions. While there are recognized examples for European sovereign clouds, the underlying capability development is critical for meeting U.S. GovCloud and FedRAMP requirements for government and highly regulated sectors. Providers are also intensely focused on industry-specific go-to-market (GTM) strategies, with significant investments in life sciences, manufacturing, automotive, aerospace, defense, energy and technology, media and telecommunications (TMT) in the U.S., tailoring solutions to meet unique sector demands. This comprehensive approach underscores the commitment of U.S. service providers to deliver not just infrastructure, but holistic, outcomedriven cloud transformations.

Proactive and AI-powered cloud management: The AWS managed services landscape in the U.S. is rapidly evolving, driven by a growing demand for comprehensive, AIdriven operational capabilities that extend far beyond basic infrastructure oversight. Service providers increasingly focus on intelligent operations, leveraging advanced AI and GenAI to automate processes, proactively predict and prevent issues, and enhance real-time decision-making. These initiatives include the development of proprietary platforms and accelerators that serve as comprehensive frameworks for cloud operations, FinOps and SecOps. A critical area of innovation is advanced FinOps, which utilizes AI for granular cost optimization, accurate forecasting and transparent chargeback mechanisms. Providers are demonstrating significant cost reductions for clients through these capabilities, often making such financial benefits a cornerstone of their value proposition. Some providers, for example, state their ability to deliver AWS native services at a significantly lower cost because of internal automation and repeatability, making their managed services highly compelling for U.S. clients seeking financial efficiencies. This financial efficiency is coupled with a strong emphasis on cloud sustainability, with some providers beginning to track and reduce carbon footprints associated with cloud operations.

Providers are building robust security operations centers (SOCs) and frameworks to ensure resilient cloud security, with many emphasizing a security-first approach in their operations. For instance, some offer continuous compliance frameworks built on security data lakes that leverage GenAI and services like AWS Bedrock. These frameworks identify non-compliance with regulatory requirements and internal IT policies, allowing for proactive risk mitigation. Others are in the early stages of using agentic AI for FinOps and security, allowing for analysis and facilitating direct actions, which helps them enhance their intelligent operations through proprietary platforms. There is also a highlighted capability across the market in integrating security within data environments, particularly for sensitive workloads. Furthermore, providers are adapting to the complexities of hybrid and multicloud environments, developing unified management platforms capable of overseeing diverse workloads across AWS, other public clouds and on-premises infrastructure.

Next-generation solutions leveraging agentic AI capabilities and secure data foundations: The U.S. market for AWS enterprise data modernization and AI services is hyperfocused on GenAI and agentic AI, with service providers making substantial investments in developing and deploying these emerging capabilities. A drastic change in market approach is observed, as providers reorient their core strategies around AI, with a significant portion of their professional training and development now concentrating on AI certifications.

A fundamental step for providers is to actively modernize clients’ data estates, building robust data lakes, data warehouses, and comprehensive data integration pipelines to ensure data readiness and quality for sophisticated AI initiatives. This foundational work is critical, as the quality of AI output is directly linked to the underlying data strategy.

A significant and evolving trend is creating and deploying custom agents, agent factories and multiagent systems, often leveraging AWS Bedrock and other AWS AI services such as Amazon Q, Amazon Nova and Amazon Titan. Some service providers are positioned as launch partners for many of AWS’ upcoming agentic AI announcements. Some are also developing orchestration layers designed to manage and facilitate interactions between agents from different technology providers, addressing the proliferating nature of agents in enterprise environments. Unique commercial models for custom agents have been introduced, with pricing based on complexity, alongside suites of AI accelerators that include tools for regulatory compliance. Service providers are exploring the Amazon Bedrock AgentCore to deploy fleets of specialized AI agents at scale.

Top leaders in GenAI are expanding their work from proof of concept (PoC) to at-scale, end-to-end customer solutions. Engagements with large enterprise clients demonstrate how GenAI, when properly implemented with enterprise data, can leverage decades of engineering knowledge to change how organizations innovate fundamentally. Providers also actively build conversational interfaces for engineering and manufacturing departments using AWS Nova models.

Governance, an essential frontier in enterprise AI: Beyond productivity, providers are building frameworks for responsible AI and governance to address concerns around bias, hallucination and data privacy. Some offer secure hub environments for customers to explore various large language models (LLMs), emphasizing security. This holistic approach signifies that AWS ecosystem providers are not just adopting AI, but are actively shaping its responsible and impactful deployment across the U.S. enterprise landscape.

RISE adoption, AI infusion and verticalization for SAP workloads on AWS: In the U.S., the AWS SAP workloads market is heavily concentrated on the RISE with SAP AWS offering. Service providers strategically facilitate these transitions, often promoting near-zero-cost S/4HANA conversion initiatives by combining financial incentives from both AWS and SAP to make migration highly appealing for clients. Partners increasingly leverage joint frameworks, solutions and funding for greenfield deployments. There is a notable uptick in discussions and active engagements for SAP S/4HANA Private Cloud Edition.

The scope of SAP workload modernization on AWS extends significantly beyond basic hosting, now deeply integrating downstream data strategies and infusing advanced AI capabilities directly into ERP processes. Some providers, for instance, have developed foundational LLMs trained with extensive ERP content to power GenAI tools and agents for ERP-centric solutions. Similarly, other providers are at the forefront of driving AI-led lifecycle automation for deployments, aiming to provide real-time intelligence across ERP landscapes. Service providers deploy specialized migration factories and accelerators to streamline and expedite SAP modernization journeys on AWS. There is an increased traction for industry-specific SAP on AWS solutions, aligning with AWS’ strategy to address unique vertical challenges for sectors like manufacturing, healthcare, finance and public services.

Partner road map for GenAI leveraging Bedrock, Guardrails and Trainium: The AWS ecosystem partner space is expected to experience the widespread adoption and scaling of GenAI applications, with inference becoming as fundamental as compute and storage. Amazon Bedrock is a central component of this trend, which provides partners with a broad selection of fully managed foundation models from various leading providers, including AWS’ own Amazon Nova family, optimized for quality, cost and speed. Customizing models by incorporating proprietary data through retrieval-augmented generation (RAG) will be crucial, with Bedrock knowledge bases now supporting structured and multimodal data. Furthermore, trust and safety in GenAI will be paramount, with Bedrock Guardrails offering essential features to mitigate unwanted outputs and hallucinations through automated reasoning and mathematical proofs. Cost optimization for GenAI inferencing is another key focus, addressed through innovations like model distillation and intelligent prompt routing, which enable faster and cheaper performance with minimal accuracy loss. The emergence of AI agents represents a transformative trend, allowing partners to build applications that can plan, act and self-reflect, extending to tasks like new code creation and bug tracking via tools such as Amazon Q Developer. The latest AWS Trainium2 chips provide the performance for building and fine-tuning large language and multimodal models for their clients faster and at a significantly lower cost.

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