A Forrester study reveals where AI adoption in the software development lifecycle (SDLC) is widespread in name but shallow in practice.
Three quarters of global firms have begun integrating AI into their software development lifecycle. Yet Forrester Consulting’s study, commissioned by Reply and published in autumn 2025, reveals a more complicated picture: most organizations are adopting AI as a collection of isolated tools rather than driving structural transformation. While 76% of organizations have adopted AI in their SDLC to some degree, only 20% report pervasive adoption across all phases.
AI adoption is front-loaded. Coding environments and low-code platforms rank highest in uptake. What lags are the practices most likely to produce lasting value: transforming culture and processes, reviewing resource management, and reskilling developers for AI-augmented roles. Firms are optimizing the execution layer while leaving the strategic and organizational layers largely unchanged. Phases requiring human judgment — governance, planning, design, and deployment — remain at pilot stage for half of respondents or more.
The Skills Gap Is Structural
The barriers to deeper AI adoption are not primarily technical. Seventy-five percent of leaders cite a lack of skills across the full SDLC, and 72% report difficulty retaining talent with expertise in emerging development methodologies. Technology challenges follow closely: 74% identified vendor lock-in, and 72% flagged difficulty integrating AI into existing workflows.
These constraints compound each other. Without the right talent, organizations cannot govern AI-generated outputs effectively. Without governance, risk rises. Without workflow integration, productivity gains from individual tools do not translate into systemic efficiency. The result: localized benefit rather than end-to-end acceleration. Improved data-driven decision-making (41%), developer productivity (38%), and software quality (38%) are real gains — but not a transformed operating model.
Sourcing Models Under Pressure
Traditional software sourcing models are under mounting strain. Offshore outsourcing faces compliance pressure: 78% of respondents say it complicates adherence to regulations such as GDPR and HIPAA, while 76% link it to higher risk of technical debt. Insourcing brings its own costs — 84% of organizations that predominantly insource report higher labour costs, difficulty scaling quickly, and limited access to specialised skills in emerging technologies.
Co-sourcing has emerged as a pragmatic middle path, but it introduces conflicts over ownership, high coordination overhead, and unclear accountability between internal and external teams. No single sourcing model offers a clean solution — which is precisely why agentic AI is attracting serious strategic attention.
Agentic AI: A Strategic Alternative to Outsourcing
Ninety-three percent of surveyed leaders say their organizations are likely or very likely to adopt agentic AI — systems capable of autonomously executing multi-step tasks, orchestrating workflows, and adapting to real-time data — as a strategic alternative to outsourcing within the next two to three years. The expectation is that autonomous agents will handle requirements gathering, code generation and review, test suite execution, deployment management, and anomaly detection, reducing the need to scale headcount linearly with delivery volume.
Roughly 80% of respondents plan to increase AI budgets over the next 12 to 24 months. The dominant vision is human-AI collaboration: agents handling repetitive and data-intensive tasks while developers focus on architectural decisions, stakeholder alignment, and higher-order problem-solving. Most respondents agree that workflow redesign and governance transformation are prerequisites for realizing this model.
Filippo Rizzante, CTO of Reply, emphasizes: “The results of the Forrester study confirm our market observation: AI is no longer just a productivity tool, but a disruptor that requires a new operating model. The limitations of traditional offshoring models, particularly in terms of quality and compliance, are leading companies to seek greater control again. With our silicon shoring model, we combine the local expertise of our industry and AI specialists with the unlimited scalability of agent-based AI. This enables our customers to drive innovation faster without compromising security or architectural integrity.”
What Companies Must Do Now
The study points to four priorities. First, embed AI as a foundational layer of the SDLC — not through incremental tool adoption but through deliberate redesign of the delivery architecture. Second, restructure development operating models, roles, and governance to reflect AI-augmented delivery rather than layering AI onto structures built for human-led development. Third, apply the same rigour to AI-generated code as to human-written output: authorship tracking, Zero Trust principles, and compliance enforcement. Fourth, reassess talent and sourcing strategies — as AI reduces the premium on coding volume and raises the importance of architectural judgment, the core rationale for offshore outsourcing weakens.
The gap is not awareness but execution. Most organizations understand the direction of travel. The distance between firms deploying AI tools and firms that have rebuilt their development organizations around AI as a core operating principle will not narrow on its own.
Dr. Jakob Jung is Editor-in-Chief of Security Storage and Channel Germany. He has been working in IT journalism for more than 20 years. His career includes Computer Reseller News, Heise Resale, Informationweek, Techtarget (storage and data center) and ChannelBiz. He also freelances for numerous IT publications, including Computerwoche, Channelpartner, IT-Business, Storage-Insider and ZDnet. His main topics are channel, storage, security, data center, ERP and CRM.
Contact via Mail: jakob.jung@security-storage-und-channel-germany.de