In today’s hyper-competitive digital economy, enterprises face mounting pressure to accelerate software innovation.
Yet bottlenecks and inefficiencies across the traditional Software Development Life Cycle (SDLC), such as legacy technologies and siloed knowledge, can prevent the organisations from rapidly creating, modifying or deploying new software and features.
As enterprises race to keep up with or stay-ahead of competitors, technical debt can pile up. Overstretched development teams may prioritise new features over refactoring, while weak or inconsistent coding standards can only make matters worse.
The result? A never-ending maintenance cycle. Manual code reviews, last-minute fixes and rushed testing can all delay deployments and introduce security risks that should have been caught earlier.
Despite rapid innovation, the core approach to software development - ideation, design, coding, testing, and deployment - has remained largely unchanged. But this is now changing. A new era of software development is emerging - one that tackles technical debt at its root and redefines how software is built.
GenAI adoption
The arrival of GenAI presents an opportunity to radically overhaul current inefficiencies across the SDLC.
In fact, it is already helping countless enterprises design, code, test and deploy innovative solutions at unprecedented speed, with investment in the technology only likely to increase in future.
Research by Reply, which provides consulting, system integration and digital services to organisations, found that 86% of software development investments will be AI-related by 2027, compared to just 18% in 2022.
GenAI tools like GitHub Copilot and Amazon Codewhisperer have already been widely adopted by developers.
AI agents on the market are now reducing the learning curve for new developers by providing insightful assistance
As well as providing real-time suggestions as you type, they can complete entire lines or blocks of code and answer questions in natural language – providing, of course, you give them the right prompt.
AI agents on the market are now reducing the learning curve for new developers by providing insightful assistance and information on generic best practices such as code readability, error handling and security best practice. However, they do have a major flaw.
“The tooling market is evolving quickly,” says Ben Walker, executive partner at Reply. “They’re good tools – they’re supportive, they can give you prompts and ideas – but currently they have limited contextual awareness and a lack of customisation”
He explains: “Just by building agent-based tools that have some knowledge, context and awareness of the environment that they’re operating in, like the rules that the organisation has set down for how they want things to be written, their security requirements, or maybe something unique and specific to organisation A versus organisation B, we can immediately demonstrate a 25% improvement.”
Reply envisions a world where teams of these contextually aware and ad-hoc customised agents take on the personas of multiple roles along the SDLC, with each agent autonomously handing off to the next in the chain.
Unlike today’s GenAI tools, which are typically designed to provide isolated assistance during a specific phase of the SDLC, these multi-agent teams can complete complex tasks involving multiple steps – and potentially even handle the entire development process autonomously.
Aside from anything else, the contextual awareness of these agents would remove a lot of the mundane bottlenecks that occur during software development today, such as “looking in manuals, trying to speak to the security team to understand what their requirements are, or what the architects want in terms of the platform environment – all of those conversations don’t need to happen anymore,” says Walker.
Instead, a contextually aware agent can “come in and immediately cross-check code and say, ‘well, okay, but what about the integrations that it needs to have with other systems?’”
Achieving hyper-automation
As these agents interact with each other and begin to operate autonomously across entire development workflows, companies could reach a state of hyper-automation where software is designed, created, tested and deployed at unprecedented speed and scale.
The beginnings of this shift can already be seen in AI’s transformative impact on testing and quality assurance.
“You can really raise the level of quality checks,” says Martina Paianini, associate partner at Ki Reply, a company within the Reply Group specialising in AI-supported software development.
“Up to now, you needed to have people writing the test cases and thinking of all the options for them, which can take a long time. Now you can do that first part very fast, and instead of 10 use cases for testing you can define 10,000 – and you can test them all because everything is done automatically at speed.”
The ability to scale testing within reasonable costs can lead to significant enhancements in software quality without additional expense – a win-win for enterprises that want to innovate at speed without spending more on additional engineers.
Improved software quality at the point of deployment also reduces the need for costly post-release patches, saving enterprises further time and money.
In fact, with the right AI tools, you can quickly identify and resolve bugs and security vulnerabilities that would have been difficult – if not impossible – to detect otherwise.
Testing at this level can facilitate a shift away from legacy technologies that have restricted innovation and agility for years or even decades.
“For example, take the business case for moving away from legacy mainframe systems. The change part was never that complicated but it was expensive, potentially scary due to both the potential scale of change and number of people needed to be involved,” says Walker.
“AI can help to significantly shorten the reverse engineering and recoding process, so you can spend more time on QA and making the business comfortable with the change. We see massive opportunities here to tackle a bunch of things that people previously couldn’t afford to tackle.”
The roles evolve within SDLC
As AI agents assume more responsibility across the SDLC, the role of developers as well as analysts, architects, testers and designers among others is likely to change significantly.
Rather than focusing on writing code, for example, they will increasingly function as domain experts and orchestrators of multiple AI agents that have effectively taken on roles previously assigned to humans.
In other words, they will not only need to know how to prompt AI to write code but also direct them across a wide range of activities that make up the development lifecycle, review their work, and ultimately ensure that it adheres to organisational safeguards and standards.
This will require developers to embrace system thinking and develop a deeper understanding of high-level business problems and opportunities.
Although there may be some trepidation about AI’s impact on jobs and current SDLC processes, it seems likely that many of them will find this higher-level work more engaging and satisfying.
But it’s fair to say this shift will mean enterprises and other institutions must rethink current approaches to education, skills and professional development.
AI can help to significantly shorten the reverse engineering and recoding process
“In the end, the role of developer and business analyst will merge,” says Paianini. “It’s not going to happen overnight, but that is the direction we’re heading in.”
By translating technical concepts for business stakeholders and business concepts for technical teams, AI is already helping to improve cross-functional collaboration and alignment.
For business analysts, AI’s democratisation of technical knowledge will require them to expand their capabilities into areas traditionally reserved for developers, mirroring the shift the latter will experience.
“If you think of a business analyst today, they generally focus on writing analysis from requirements. Instead, in the future, if they want to modify the application in some way they will be able to give that task to the specific AI agent and the modification will be done automatically,” Paianini explains.
Already today, fully functioning prototypes can be created at previously impossible speeds, allowing analysts to test a greater number and variety of ideas before committing further resources.
Traditionally, prototyping methods have focused on visual design without functioning backends, with significant additional development time needed to implement working features.
“Now we can sit with a client, understand what they want and have a functioning prototype ready quite quickly,” says Walker. “Before, it could have taken a month and still not really demonstrated enough depth.”
This capability could transform not only development timelines but an organisation’s entire approach to innovation and experimentation.
Businesses can now react to market shifts and competitor moves in weeks instead of months or years.
But as Paianini warns, with go-to-market speeds accelerating, companies must be strategic. Flooding the market with rushed, copycat solutions risks confusing customers and diluting brand value. The winners will be those who balance speed with originality and real customer impact.
Looking ahead
We’re now fast approaching a world where any business stakeholder can describe a solution, feature or improvement to an AI and see it created and deployed in minutes or less.
Self-optimising systems that automatically refactor and rewrite code based on changing user, technology or business requirements – without any human intervention whatsoever – are on the horizon.
However, for leaders to trust AI to operate autonomously, these systems must have a deep understanding of organisational context. While the possibilities are compelling, successful implementation demands careful planning and strategic change management.
The transition to a fully autonomous SDLC is a journey, requiring a fundamental transformation of operating models, role definitions, workforce upskilling and process redesign.
“It is both a journey and a commitment,” explains Paianini. “It’s a commitment because it takes six to twelve months to complete, and organisations will need to address many challenges along the way. The best partner for this journey is someone like Reply - someone that is deeply knowledgeable and highly competent in these areas.”
Beyond technical implementation, enterprises will need strong partners to navigate this shift. Choosing the right AI agents - whether off-the-shelf solutions or custom-built, specialised models - is crucial. Equally important is managing the organisational impact, ensuring employees are supported through change, and developing strategies to integrate AI into existing workflows without disruption. Partners who provide both technical expertise and structured change management will be essential in guiding businesses through this transformation.
As AI becomes embedded across the SDLC, competitive differentiation will no longer come from having the latest tools or the best grasp of development methodologies. Instead, the real advantage will lie in the quality and uniqueness of an organisation’s data. “Your data becomes the differentiator, and that’s the most fundamental part of this shift,” Walker emphasises.
Contextually aware AI agents are not just a technological enhancement or a way to speed up processes - they are a strategic imperative. Organisations that embrace this shift thoughtfully and with the right support will be the ones that sustain market leadership in an AI-driven future.
For more information please visit reply.com
In today's hyper-competitive digital economy, enterprises face mounting pressure to accelerate software innovation.
Yet bottlenecks and inefficiencies across the traditional Software Development Life Cycle (SDLC), such as legacy technologies and siloed knowledge, can prevent the organisations from rapidly creating, modifying or deploying new software and features.
As enterprises race to keep up with or stay-ahead of competitors, technical debt can pile up. Overstretched development teams may prioritise new features over refactoring, while weak or inconsistent coding standards can only make matters worse.
