ISG Provider Lens™ Next-Gen ADM Services - AI-enabled Application Development and Maintenance (AI-ADM) - U.S. 2024

Cloud and AI play a major role in AI-enabled, infrastructure-coupled ADM cost-savingdriven transformations

The application development and maintenance (ADM) landscape has undergone continuous change to accommodate the changing needs of the enterprise’s business. From the erstwhile waterfall model to Agile to moving toward  DevOps model, ADM has solved multiple challenges/requirements from simply applications development to improving time to  market to integrating operational nuances during application development. The major shift from last year to this year for ADM has been influenced by AI and cloud, among other factors. The use of AI across the ADM lifecycle has significantly increased, and operational benefits have been realized; hence, this year, a new quadrant, AI-enabled ADM, has been added that focuses on using AI across the ADM lifecycle.

Application development deals with an infrastructure component are becoming increasingly common as businesses seek integrated solutions. These deals encompass creating software applications and managing the underlying IT infrastructure, such as cloud services, servers and networks. By bundling app development with infrastructure
management, companies ensure that their applications are optimized for performance, scalability and security from the ground up. This holistic approach also streamlines vendor management, reduces costs and enhances operational efficiency, enabling businesses to focus on innovation and growth while maintaining robust, resilient IT environments.
According to ISG Index Q2 2024, contracts for ADM bundled with infrastructure have seen 57 percent growth.

Optimal fitment of infrastructure, data and applications on the enterprise IT landscape is essential for achieving operational efficiency, scalability and competitive advantage. It begins with a strategic alignment of IT infrastructure — whether on premises, cloud based or hybrid — with the organization’s business objectives. This infrastructure must
be robust, flexible and scalable to support current and future needs, ensuring it can handle varying workloads while maintaining high availability and security. Data management plays a pivotal role in this landscape. Proper data governance frameworks are crucial for ensuring data quality, security and compliance. This involves integrating data sources across the organization, enabling seamless access and flow of information. Effective data management
supports real-time decision-making and underpins analytics, AI and machine learning initiatives that drive innovation. Applications, on the other hand, must be optimally deployed to leverage this infrastructure and data foundation. Whether these are off-the-shelf solutions or custom built, they should be designed or selected to align with business
processes and user needs. Integrating DevOps practices, continuous integration/continuous deployment (CI/CD) and microservices architecture ensures that applications are Agile, scalable and responsive to changing business requirements. The combination of infrastructure, data and applications is pivotal in rightsizing the enterprise IT landscape for optimal performance.

The adoption of cloud-native development is accelerating as businesses seek agility and innovation. Organizations are increasingly migrating to cloud environments such as AWS, Azure and Google Cloud, enabling them to respond quickly to market changes and customer demands. The rise of Kubernetes and other orchestration tools has further facilitated this shift, simplifying the management of complex applications across distributed environments. Cloud-native application development utilizes microservices architecture, containerization and DevOps practices to create scalable, resilient and flexible applications.

AI and generative AI (GenAI) have significantly enhanced the ADM lifecycle by automating tasks, improving quality and accelerating delivery. The image below shows the use cases of AI and GenAI across the ADM lifecycle. Some benefits include reduced time to market/resolution, developer productivity improvement, improved application efficiency, minimized downtime, proactive maintenance and improvement in overall operational efficiency.

The key trends across each quadrant include:

Agile Application Development Outsourcing

Service providers are embracing an automation-first strategy that integrates platforms, generative AI and low-code solutions to revolutionize their business operations and software development. This approach optimizes processes, reduces manual intervention and accelerates product delivery timelines. GenAI helps transform content creation, design and coding, while low-code platforms empower developers to develop applications rapidly, driving innovation and agility. Providers also use it to deploy next-generation platforms that integrate automation or AI. This enables
significant economies of scale by centralizing the development of intellectual property (IP) that can be replicated in multiple projects. This allows providers to develop solutions once and deploy them across multiple clients, facilitating
uniform quality and performance while eliminating redundant efforts. Service providers are focusing on improving capabilities across specific industries. As industries increasingly focus on these transformative technologies, investments in advanced platforms and AI capabilities set new benchmarks for innovation, operational excellence and
customer satisfaction.

Agile Application Development Projects

Service providers prioritize co-innovation with partners to develop cutting-edge solutions and accelerators that align with market demands. By leveraging various partners' combined expertise and resources, providers accelerate development timelines and ensure robust and scalable solutions. Providers also invest in talent and enhance  employees' skills in a rapidly evolving technology landscape. Providers should focus on continuous learning and development programs to equip their workforce with the latest technologies and methodologies, improving service quality and employee retention. The firms should focus on accelerating the Agile transformation journey across cross-functional teams, fostering a culture of collaboration, agility and continuous improvement.

Adopting Agile frameworks and promoting transparent communication enables providers to respond swiftly to market changes and deliver faster, enhancing customer satisfaction. Finally, leveraging AI for productivity improvements can significantly reduce operational costs and optimize processes. AI-driven tools enable providers to automate routine tasks and provide valuable insights, allowing them to focus on strategic initiatives and maintain a competitive edge.

Application managed services

Site reliability engineering (SRE)-led AIOps represent a pivotal shift in IT operations, integrating site reliability engineering practices with the advanced capabilities of AI for IT operations (AIOps). This strategic approach utilizes ML and advanced analytics to sift through vast amounts of operational data, enabling site reliability engineering teams to proactively detect and resolve potential issues, thus ensuring uninterrupted service delivery. The enhancement of observability through SRE-led AIOps provides comprehensive insights into system performance, revealing intricate
correlations between events and identifying root causes of incidents with greater precision. This improved visibility allows for quicker response times and more effective problemsolving. Automation, a key feature of AIOps, streamlines routine tasks, freeing SRE teams to focus on strategic initiatives and continuous system improvement. For service providers, embracing SRE-led AIOps means delivering robust, reliable services that enhance customer satisfaction and secure a competitive edge.

Application Quality Assurance

Service providers focus on offering industry-specific solutions by leveraging the domain expertise of their quality engineers. Understanding various industries' unique challenges and compliance standards allows quality assurance (QA) teams to customize testing processes and tools, ensuring application reliability, security and performance. GenAI-driven QA transforms traditional methodologies by automating test case creation and scenario simulation.

This accelerates the testing process and enhances coverage, identifying potential issues that manual testing might overlook. Integrating AI and ML for test case prioritization and anomaly detection is essential. AI can analyze historical data to predict which test cases reveal new defects, optimizing the testing focus. Furthermore, anomaly detection algorithms swiftly pinpoint unexpected behaviors during testing, facilitating quicker issue resolution. This AI and ML integration enhances testing efficiency and maintains high-quality standards, providing a competitive edge in a dynamic application landscape.

Continuous Testing Services

Continuous testing services increasingly include Agile and DevOps practices in their service offerings. This shift emphasizes integrating tools and frameworks that support continuous integration, delivery and testing. The focus on automation reduces time to market, enhancing the efficiency and speed of application delivery and enabling faster,
high-quality software deployment.

Cloud-based testing is increasingly essential for clients seeking scalability, flexibility and cost-effectiveness. Given the growing threats of cyberattacks and data breaches, security testing and compliance have become critical. These practices ensure that applications are secure, compliant and trustworthy, enhancing client confidence in their software solutions.

AI-driven Application Development and Maintenance (AI-ADM)

The adoption of AI-driven application development and maintenance (AI-ADM) services by enterprise IT, particularly within sourcing and technology teams, is transforming operational dynamics. This evolution demands the implementation of robust compliance guardrails and stringent intellectual property protection measures to ensure regulatory alignment and safeguard proprietary assets. By doing so, enterprises can confidently embrace AI-ADM technologies while mitigating data security and IP risks. Rapidly enhancing the accuracy of AI-ADM methods is critical,
achieved by leveraging customer-specific code, data and artifacts. This fine-tuning accelerates development cycles and customizes AI solutions to meet the unique requirements of each enterprise. Investing in workforce training on generative AI technologies is essential, enabling teams to deploy and manage AI-ADM solutions effectively and fostering a culture of continuous innovation.

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