How can you accelerate software delivery while also maintaining quality and controlling costs?
It’s a question many enterprise leaders are grappling with as they attempt to improve their software development practices and meet the demands of a competitive marketplace.
Until recently, their answers may have focused on how to squeeze more from limited resources.
But today, there’s another option – one that could fundamentally change the way software is conceived, built, tested, deployed and maintained.
In just a few years, GenAI has transformed linear, human-intensive software development into a faster, more automated process enhancing productivity, improving software quality, and accelerating time-to-market .
Salesforce’s CEO, Marc Benioff, recently stated that the company planned to freeze new hires of software engineers due to a 30% productivity boost from spending on AI agents and coding tools.
“We’re sure that the technology works and that it represents a very big opportunity,” says Ben Walker, executive partner at Reply Software, which provides consulting, system integration and digital services. “In fact, some of the things that you can do with GenAI in the software development lifecycle are already taken for granted.”
Traditionally, the software development lifecycle (SDLC) has been hampered by skills gaps, technical debt and siloed knowledge, which creates dependencies on key personnel, significant delays and prevents organisations from creating, modifying or deploying new features.
By translating requirements into technical language, automating repetitive tasks like code generation and testing, and identifying potential bugs even before deployment, AI can help to address many of these issues and unlock a much-desired outcome for any enterprise: doing more with less.
However, effectively integrating AI into the entire SDLC requires more than just investment in new tools. It demands a comprehensive strategy that addresses both technical capabilities and organisational readiness for deploying AI at scale. Crucially, this strategy should focus on where AI agents can deliver the most value during each phase of the development lifecycle.
The most significant advances in AI-augmented development will come not from isolated tools but from integrated systems of AI agents working together
Companies with phase-specific AI agents are likely to see a higher ROI on their AI investments compared to those adopting market solutions.
That’s because the tools, techniques and skills needed to translate business requirements into technical specifications, for example, differ from those needed to automate the testing and deployment of software. Integrating AI into the many different workflows and processes found at each stage of the development cycle requires a thoughtful approach that makes the most of human skills and insights.
Multi-agent AI systems effectively transform developers from coders to higher-level problem solvers, for example, while designers may shift from purely creating interfaces to orchestrating AI-driven user experiences and refining human-AI interactions.
Many may need more training and upskilling to make the most of the technology and adapt to new ways of working.
This shift may require a rethink of performance metrics and how human talent should be deployed across the organisation.
“People’s roles are getting more mixed, so enterprises will need to start evaluating what skills are available among their people and start upskilling or modifying some of them,” says Martina Paianini, associate partner at Ki Reply, a company within the Reply Group specialising in AI-supported software development.
Rather than eliminating roles, AI is rapidly evolving the responsibilities of developers, analysts, architects, testers and designers. These professionals must now expand their expertise beyond traditional silos, integrating business insights, user needs and AI-driven automation into their workflows.
For example, a developer may “need to analyse why the requirements for the software are not producing the correct results.”
AI at every phase
The benefits that AI systems can bring to software development are evident right from the beginning of the SDLC.
AI can identify ambiguities, contradictions and omissions in requirements documents, for example, thereby reducing specification errors.
By acting as a bridge between business analysts and developers, AI infused with natural language processing can also help to reduce the kind of miscommunication that can easily lead to scope creep, as well as unrealistic deadlines and misallocated resources.
Automated prototyping can also generate wireframes, UI/UX mock-ups and even functional prototypes based on user input.
“We can sit with a client, understand what they want, and have a functioning prototype within a day,” says Walker.
“Getting an idea to a place where you can quickly say, ‘that’s worth continued investment’ or alternatively ‘no, it’s not’ – that’s huge.”
During the design phase, AI can also assess proposed designs against scalability, performance and other security documents. However, these benefits depend upon a crucial element of any successful AI: context.
Without context on organisational processes, infrastructure, guidelines, security requirements and many other vital topics, AI tools are effectively flying blind. Context-aware code generation, on the other hand, ensures alignment with architectural patterns and organisational standards and governance. It’s this context that will ultimately allow AI agents to advance beyond an assistive role and handle more – or even all – of the code generation phase independently.
AI is also transforming quality assurance and testing. It can generate comprehensive test cases based on code analysis, for instance, and even identify edge cases that human testers might miss.
Test creation times can also be radically reduced, with knock-on effects across the rest of the development lifecycle. Furthermore, companies with AI-driven development workflows are likely to experience far fewer critical bugs in production environments.
Once applications and features are deployed, AI can monitor them in real time, suggesting enhancements and even predicting failures before they occur. This ultimately leads to a continuous delivery pipeline that requires much less human intervention. AI can also detect security threats and unusual system behaviours before they become critical and impact users.
On the refactoring side, meanwhile, the technology can also suggest code improvements that will enhance software efficiency and maintainability over time. As enterprises move toward greater autonomy, they can unlock progressively larger benefits in cost efficiency, quality, and speed across all these areas of the SDLC.
“The latest approach, which is the most autonomous, is where you have agents that do things by themselves and talk to other agents,” says Paianini.
The most significant advances in AI-augmented development will come not from isolated tools but from integrated systems of AI agents working together, all of them equipped with contextual awareness.
The result? Accelerated software delivery that also improves quality while reducing costs – and that, surely, is the answer that enterprise leaders are looking for today.
The AI software strategy guide: 5 considerations to measure effective transformation
The integration of AI across the software development lifecycle (SDLC) has transformed how organisations design, build, test and deploy applications.
But as these intelligent tools automate and enhance traditional development activities, leaders face a critical question: how do they measure the effectiveness of their AI implementation strategy?
It’s hard to prove the benefits of agentic AI if “you don’t have some metrics or a baseline understanding of how good your development processes were before you introduced it,” says Ben Walker, executive partner at Reply, which provides consulting, system integration and digital services to organisations.
With that in mind, the following considerations present five essential pillars against which leaders can tangibly measure their AI integration effectiveness.
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