The last 12-15 months have marked a revolutionary period in application development and maintenance (ADM) services, driven largely by advancements in AI. Most notably, the integration of generative AI (GenAI) and the emergence of agentic AI have redefined the landscape.
AI-Augmented Development Workflows
The traditional approach to application development — a marathon of manual coding, debugging and iterative processes — has been upended by AI-driven tools. In 2025, development timelines are expected to shorten dramatically, with organizations reporting productivity gains of 35-45% in coding tasks due to GenAI. Through tools such as GitHub Copilot and OpenAI’s Codex, updated AI tools have made their way into mainstream usage. These GenAI tools leverage large language models (LLMs) trained on vast repositories of code, enabling rapid prototyping and reducing manual effort by generating code snippets, autocompleting functions and even drafting entire modules based on natural language prompts.
But the revolution does not stop there. Agentic AI takes automation a step further by autonomously managing multi-step development workflows, from interpreting requirements to writing code and integrating it into existing systems. This emerging technology is reducing the need for constant human oversight, fundamentally altering how software development takes place.
The Transformation of Application Maintenance
Traditional application maintenance and support (AMS), which heavily relied on human teams for bug fixing, patching and system monitoring, is on the decline. Organizations are progressively transitioning toward AIOps and cloud-native maintenance models. ML-powered predictive maintenance tools have been particularly effective in preempting potential issues, reducing the need for reactive support by 20-25% in certain sectors. GenAI contributes by generating automated scripts and documentation for maintenance tasks, while agentic AI systems autonomously handle maintenance duties, such as applying patches and optimizing configurations based on real-time system data.
Democratization of App Development and Enhanced Testing
Low-code/no-code platforms have continued to gain traction, enabling non-technical users to develop and maintain applications. These platforms have been increasingly infused with AI capabilities, making application development more accessible than ever. GenAI integrates with these platforms, translating natural language inputs into functional applications complete with user interfaces and backend logic. This democratization of application development has significantly reduced reliance on specialized developers, fostering a new era of innovation led by citizen developers.
Testing and quality assurance have likewise transformed from manual or semi-automated processes to fully AI-driven frameworks, especially for validating AI components within applications. Test coverage and speed have significantly improved. GenAI plays a crucial role by generating test cases, scripts and synthetic data for testing AI models and application logic, reducing test creation time considerably. Tools from Hugging Face and Amazon SageMaker have been instrumental in validating model outputs. Meanwhile, agentic AI systems orchestrate end-to-end testing — running tests, analyzing results and iterating fixes autonomously — particularly for complex, multiplatform applications.
Workforce Evolution and Economic Efficiency
Workforce dynamics and skill requirements have also shifted. The demand for traditional developers and maintenance engineers has waned, giving way to AI engineers, prompt engineers and AIOps specialists. GenAI has automated many routine coding tasks formerly performed by junior developers, freeing up senior developers to focus on innovation and architecture. Training programs are increasingly emphasizing proficiency in AI tools. The advent of agentic AI has also shifted hiring toward experts who design, govern and oversee these systems rather than those who handle manual tasks.
Service providers have been able to increase productivity by over 20% by using GenAI. However, no impact has been observed on operational costs at the enterprise side, as service providers are using this to reduce their technical debt and improve quality assurance.
The initial investments in AI tools and training have been substantial, and the long-term savings and efficiency gains are impressive. GenAI lowers development costs by expediting coding and documentation processes, while AI-driven maintenance models cut operational expenses dramatically through predictive analytics. Agentic AI further amplifies these savings by automating entire workflows and minimizing labor and error-related costs.
Broader Trends and Future Implications
As we navigate this transformative period, it is clear that AI, especially GenAI and agentic AI, is driving the reshaped ADM landscape. AI technologies have optimized operations, GenAI has accelerated development and maintenance and agentic AI is paving the way for fully autonomous systems. Collectively, these advancements reduce costs, shorten development timelines and shift workforce needs, positioning ADM providers to deliver greater value in a competitive, AI-driven market.
ISG AI-Driven ADM Services IPL Study 2025
We invite all leading ADM service providers to participate in the latest ISG Provider Lens™ study, which will comprehensively analyze and showcase the rapid advancements reshaping the ADM landscape. By participating, service providers will gain valuable insights into current market expectations, trends and the opportunities that AI, GenAI and agentic AI present.
This study offers a unique platform to highlight expertise, demonstrate innovative capabilities and showcase how providers integrate advanced AI technologies into their ADM offerings to deliver measurable business outcomes. Readers will have access to important research to inform their buying decisions in a rapidly evolving ADM market.
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