ISG Provider Lens™ AI-Driven ADM Services - Application Development Outsourcing - U.S. 2025

AI-driven approaches are changing the dynamics of ADM contracting, leading to value-driven services

Including AI and related technologies has paved the way for reinventing the entire ADM lifecycle to explore possible methods and approaches to delivering services that realize significant benefits for both enterprises and service providers. These benefits realized have introduced new scenarios that they must consider; hence, they have reconsidered contractual terms to ensure that these scenarios are considered for mutual benefit.

Driven by the adoption of automation, AI/ GenAI, cloud-native modernization and stricter compliance mandates, organizations are redefining their engagement models with service providers. Traditional, effort-based contracts are giving way to agile, value-driven and innovation-aligned agreements. Key developments in this landscape include:

1. Transition to outcome-based and experience-level agreements

Outsourcing contracts are moving beyond conventional time-and-materials or fixed-price models. Increasingly, pricing structures are being linked to tangible business outcomes, such as improved release velocity, end-user satisfaction, revenue enablement or operational efficiency. Outcome-based models are expected to constitute a significant share of the market, while experience-level agreements (XLAs) are being introduced to measure performance through user-centric KPIs, not just system uptime or incident counts, indicating a shift toward customer and business value realization.

2. Greater flexibility and built-in agility

Modern contracts are embedding Agile principles and flexible change management frameworks that enable rapid adoption of new technologies, particularly AI, GenAI and hyperautomation, without exhaustive renegotiation. Iterative planning, modular deliverables and dynamic scope adjustment are becoming standard, allowing engagements to adapt to evolving enterprise priorities and emerging innovations.

3. Broadened scope and accountability

Outsourcing agreements now encompass end-to-end ownership of business processes, extending well beyond application upkeep and support. Service providers assume broader accountability, including governance, risk management and remediation of AI deployments. These agreements also include provisions for cost transparency, auditability and compliance with regulatory frameworks, particularly relevant in sensitive domains such as healthcare, BFSI and the public sector.

4. Automated performance tracking and intelligent governance

The rise of AI-powered observability platforms has enabled contracts to mandate continuous performance monitoring. Real-time KPIs such as system reliability, automation ROI, code quality and cost efficiency are being autotracked. This data-centric approach facilitates frequent reviews, enabling performancelinked incentives or penalties and enhanced transparency between a client and a provider.

Evolving enterprise expectations for ADM

In the context of AI-driven ADM and changing contractual language, enterprises expect their ADM engagements to go well beyond traditional support models. They seek strategic partnerships that drive innovation, operational resilience and long-term value creation. The following expectations define the new benchmarks for ADM service providers:

• Continuous innovation: Enterprises no longer view application maintenance as a static, reactive function. Instead, they demand continuous application evolution to align with dynamic business requirements and changing customer expectations. ADM providers are expected to embed innovation into delivery, leveraging Agile practices, AI/ GenAI, low-code platforms and emerging technologies to continuously enhance functionality and UX.

• Resilience and reliability: Stability, uptime and operational continuity are critical to business performance. Applications must be engineered and maintained with robust architectures that support high availability, proactive incident prevention and rapid recovery. Enterprises expect ADM partners to integrate observability, predictive analytics and site reliability engineering (SRE) principles to ensure uninterrupted service.

• Security and compliance: With increasing cybersecurity threats and evolving regulatory landscapes, enterprises expect ADM services to deliver an elevated security posture. This includes embedding security across the development life cycle (DevSecOps), enforcing strict access controls and ensuring continuous compliance with global data protection and industry-specific regulations.

• User-centric support: Enhancing the enduser experience is a key priority. Enterprises expect real-time support with intelligent service desks, proactive issue resolution and continuous feedback loops. ADM services must be designed to reduce friction, increase responsiveness and improve digital experiences across all touchpoints.

• Future-proofing legacy systems: Enterprises recognize the need to modernize legacy applications to stay competitive. ADM partners are expected to lead modernization initiatives such as refactoring, replatforming or re-architecting systems to support cloud adoption, new integrations and scalable digital services. The goal is to build adaptable application ecosystems that support future innovation and growth.

Strategic innovations by service providers

To meet evolving enterprise demands and ensure long-term application sustainability, service providers are adopting advanced, strategic approaches to minimize technical debt and optimize the entire application lifecycle. These initiatives are transforming traditional ADM into a proactive, innovationled function:

1. Automated code quality and continuous improvement

Leading providers leverage advanced intelligence platforms to conduct continuous assessments of code quality against industry benchmarks. These tools enable early identification of suboptimal coding practices, reduce rework and improve maintainability. For instance, Accenture’s CoE utilizes such capabilities to help clients reduce technical debt by approximately 16 percent per application, through systematic quality evaluations and continuous optimization.

2. Proactive modernization and refactoring initiatives

Moving beyond reactive maintenance, providers are increasingly engaging in structured modernization programs such as refactoring legacy code, adopting microservices-based architectures and updating key components incrementally. These efforts simplify application landscapes, reduce technical debt and improve agility. Market leaders such as Microsoft, Netflix and Salesforce exemplify this approach through phased refactoring and cloud migration strategies.

3. Business-aligned transformation strategies

Technical debt reduction is increasingly integrated with broader digital transformation goals. Providers codevelop transformation road maps with clients, balancing modernization of legacy systems with forwardlooking innovation. This alignment ensures that debt remediation contributes directly to improved business outcomes, operational efficiency and enterprise agility.

4. AI and ML for scalable automation

Providers utilize AI for intelligent code analysis, automated testing and large-scale refactoring. A notable example is using large language models (LLMs) to execute significant codebase migrations, reducing project timelines from years to weeks. Such capabilities accelerate remediation and enhance accuracy.

5. Institutionalizing technical debt management

Supported by their service partners, progressive organisations embed formalized technical debt governance into their development lifecycle. This includes tracking debt KPIs, assigning technical debt champions and integrating debt reduction into sprint planning. This disciplined approach fosters a culture of continuous improvement and prevents the unchecked accumulation of legacy burdens.

Service providers are increasingly building advanced AI, GenAI and agent-enabled tools and platforms to transform application development outsourcing. These platforms leverage AI to accelerate productivity across the software development lifecycle (SDLC) by automating tasks such as code generation, test case creation, defect prediction and resolution. GenAI plays a critical role in cocreating application components, generating documentation, modernizing legacy systems and enabling low-code/no-code development, thus empowering both technical and nontechnical users. In parallel, service providers are deploying AI agents, or autonomous digital workers, to manage continuous integration and delivery (CI/CD) pipelines, orchestrate testing, monitor application performance and even handle incident management. These capabilities are integrated into intelligent DevSecOps platforms to ensure continuous delivery, proactive security and automated compliance.

Additionally, AI is being used to streamline program governance and collaboration by summarizing updates, automating reporting and enhancing traceability across development artifacts. The platforms are modular, cloudnative and self-learning, allowing for seamless integration and scalability across diverse environments. Ultimately, by embedding AI, GenAI and agents into their outsourcing offerings, service providers are reducing time to market, improving code quality, optimizing operational efficiency and positioning themselves as strategic enablers of enterprise digital transformation.

Based on the tools, accelerators and solutions offered, the providers vary in their approach to offering price reductions and benefits:

• Direct price reduction: Some providers, such as Innominds, Sutherland and Mphasis, clearly identify direct price reductions as part of their offerings. These reductions are often tied to specific use cases, measurable productivity gains or volume discounts.

• Indirect cost benefits: Providers such as Hexaware and Visionet focus more on indirect benefits that enhance efficiency and reduce long-term costs, rather than directly reducing upfront prices.

• Efficient resource usage and enhanced quality: Many providers highlight AI capabilities to improve efficiency, quality and resource usage, thereby indirectly lowering costs.

• Outcome-based and scalability approaches: Some companies implement outcome-based pricing models that align with realized efficiencies, scaling capabilities and strategic decisions.

Each provider utilizes AI uniquely to bring about cost savings, efficiency and quality improvements. The specifics of price reduction depend on the project scope, AI integration level, contractual terms and technology platforms used.

Quadrant-specific Trends:

Application Development Outsourcing

In today’s rapidly evolving digital landscape, businesses are increasingly turning to AIdriven application development outsourcing as a strategic move to enhance efficiency and remain competitive. The concept of AI First enterprise is at the forefront of this transformation, where AI becomes a central component of the application development process. By embedding AI at the core of operations, organizations can optimize data management, service delivery and governance structures, ensuring a seamless value stream across all aspects of development.

One notable trend in this realm is the adoption of a GenAI-led talent strategy on the provider side, promoting a hybrid workforce that combines traditional human resources with AI agents and GenAI. This approach enhances productivity and facilitates smarter decisionmaking, empowering teams to deliver better outcomes more swiftly.

A full-stack approach to provider partnerships with vendor highlights the integration of applications, AI models, data and infrastructure, ensuring that every facet of the development process is interconnected and efficient. With the introduction of agentic AI into the SDLC, organizations can leverage an agentic SDLC platform that compresses development timelines through AI-powered automation, streamlining processes from planning and coding to testing and deployment.

Additionally, AI-powered technical debt remediation frameworks enable real-time discovery, prioritization and resolution of technical debt within enterprise landscapes. This proactive approach helps organizations maintain system integrity and performance, ultimately improving overall project outcomes.

The value delivered through application development outsourcing empowered by AI is substantial. Organizations can expect reduced operational costs, improved time to market, expanded margins and increased revenue, all culminating in a significant competitive advantage. By leveraging these AI-driven strategies, businesses are advancing their application development capabilities and positioning themselves for sustained growth in an increasingly complex marketplace.

Application Development Projects

Organizations increasingly shift toward consumption-based or outcome-driven contracts as they seek more proactive and insight-driven services. This transformation is heavily influenced by the integration of AI, which plays a critical role in continuously monitoring application health, user behavior and essential business KPIs. By leveraging AI, service providers can offer real-time insights that drive better decision-making and enhance overall application performance.

In parallel, clients prioritize legacy modernization, utilizing AI to expedite processes such as code analysis, refactoring and migration. GenAI assists in reverse engineering, facilitating swift test case generation and conducting thorough code quality assessments. This approach significantly accelerates the transformation to cloud-native or microservices architectures, while effectively reducing costs and minimizing technical debt.

To further streamline development efforts, integrating AI with DevOps and Agile methodologies has become imperative. Clients demand AI-driven ADM and testing services that seamlessly align with their DevOps and Agile workflows. Achieving this requires close collaboration among development, operations and testing teams to enhance communication, reduce bottlenecks, and enable faster development cycles and continuous delivery. By adopting outcome-based cognitive service models and integrating AI throughout the application development process, organizations can meet their current challenges and position themselves for future growth and innovation in an increasingly competitive landscape.

Application Managed Services - GSIs

In an increasingly competitive landscape, organizations prioritize application management that emphasizes AI-driven responsiveness and frictionless operations. Clients seek resilient application environments that operate with minimal downtime and optimized performance. To meet these demands, service providers are harnessing advanced AI-powered platforms that deliver real-time monitoring, predictive issue resolution and intelligent incident triage.

The incorporation of AI technologies allows for significantly reduced mean time to recovery and enhanced service-level agreement (SLA) adherence. These capabilities are critical in helping organizations maintain seamless operations and drive overall business continuity. By embedding agentic AI within support workflows, service providers can facilitate frictionless, self-healing systems that anticipate and address potential issues proactively.

The shift toward AI-driven responsiveness and frictionless operations in application management is redefining how organizations approach their IT environments. As businesses navigate the complexities of a digital-first era, leveraging AI capabilities will be essential for achieving operational excellence and sustaining competitive advantage. This strategic alignment between AI technology and application management will shape the future of business operations.

Global capability centers (GCCs) are increasingly leveraging application managed services (AMS) to shift the focus from routine operations to innovation and strategic growth. GCCs gain access to AI-powered, scalable platforms offered by service providers that ensure system resilience, faster incident resolution and improved SLA compliance. This enables them to enhance agility, reduce operational costs and drive digital transformation. Service providers bring standardized processes, automation and global expertise, allowing GCCs to modernize legacy systems, adopt new technologies faster and support 24/7 operations, positioning them as innovation hubs within an enterprise.

Application Managed Services - Local SIs

Application managed services (AMS) offered by system integrators (SIs) are undergoing a significant transformation, driven by the integration of AI, automation and cloud-native technologies. Most service providers in this segment are globally headquartered, with primary operations in the U.S.

Traditional application maintenance models are rapidly evolving into intelligent, predictive and proactive service frameworks. One of the most notable shifts is the adoption of AIOps. AIOps platforms harness ML to predict and preempt system issues before they impact operations. These capabilities reduce the need for reactive support by enabling predictive maintenance, anomaly detection and automated incident resolution. GenAI further enhances efficiency by creating automated scripts and documentation, streamlining routine maintenance tasks and lowering operational overhead.

Another major trend is the hyperautomation of IT operations, where clients increasingly expect AI-powered tools for intelligent ticket routing, auto-remediation and root cause analysis. These tools significantly reduce mean time to repair (MTTR), minimize human intervention and improve service reliability. AI-led service desks and self-healing systems are becoming the norm, setting new benchmarks for operational excellence. Hyperpersonalization is also emerging as a critical expectation. Enterprises are leveraging NLP, GenAI and adaptive interfaces to deliver personalized UX and EX. This enhances productivity, satisfaction and the overall digital experience quality.

The future of AMS lies in intelligent automation, AI-driven operations and experience-centric support models. These trends enable organizations to build resilient, efficient and user-friendly application environments that align with modern digital business needs.

Application Quality Assurance

Application quality assurance (QA) is undergoing a strategic transformation to meet the demands of modern digital enterprises.

A key trend is the integration of AI across the SDLC, where AI-assisted development is driving significant productivity gains. By automating tasks such as code generation, test creation and defect prediction, organizations are accelerating their journey from experimentation to production, enabling faster innovation cycles.

Another major development is the shift toward dynamic, technology-aligned testing. Enterprises are moving beyond static QA processes to adopt flexible testing approaches that align with evolving tech stacks, while maximizing the reuse of existing assets. Continuous testing for applications developed before deployment is becoming foundational, enabling early detection of issues and ensuring consistent quality across rapid release cycles. Quality engineering (QE) is also becoming more industry- and package-specific, tailored to support complex transformation initiatives involving platforms such as SAP, Salesforce and Oracle. This domain-focused approach ensures that QA strategies are aligned with the unique requirements of each business sector and technology ecosystem.

An integrated QE strategy is now essential, covering applications, devices, platforms and data to ensure comprehensive quality coverage. This holistic approach eliminates silos, improves traceability and enhances the enduser experience. Organizations are increasingly relying on QE advisors to drive transformation. These advisors play a pivotal role in aligning QA efforts with broader business and IT goals, ensuring that quality becomes a strategic enabler rather than an afterthought. Together, these trends reflect a shift toward intelligent, agile and value-driven QA practices that support enterprisewide digital transformation.

Continuous Testing Specialists

Advancements in AI and ML in continuous testing are emerging as a transformative trend. One significant application is in the requirement understanding phase, where domain-adapted GenAI converts high-level product features into precise user stories, fostering a shared understanding among product, development and QA teams. The landscape of QA is undergoing a significant transformation as organizations shift from traditional models to continuous quality practices. Continuous testing has become essential to accommodate Agile development cycles, supporting weekly or even daily release schedules.

AI and automation play crucial roles in this evolution, with increasing adoption of AI, ML and GenAI. These technologies enhance various QA processes, including test case design, defect prediction and test optimization. Innovations such as self-healing automation and cognitive QA are gaining traction, allowing teams to minimize manual effort while improving test coverage. Moreover, investments in QA are increasingly linked to KPIs such as defect leakage rates, test cycle times and their impact on user satisfaction and churn. Clients are now favoring outcome-based or risk-reward models that align QA services directly with business results.

As enterprises seek deeper partnerships in their quality assurance efforts, the demand for domain-aware testing solutions grows. Organizations prefer testing partners with industry-specific knowledge rather than those relying solely on generic tools and scripts. This trend emphasizes the importance of business process validation, CX assurance and compliance-focused testing, particularly in sectors such as banking, financial services, insurance (BFSI), healthcare and retail.

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