ArtificialIntelligenceDigitalTransformationQualityEngineering
Delivering a positive customer experience is a fundamental goal of software engineering – and quality, defined from the customer’s perspective, is the key to achieving it. However, quality can mean vastly different things to different people. Too often, organisations focus exclusively on functional requirements during software development (particularly early in the lifecycle), overlooking critical non-functional aspects of quality, such as performance, security, reliability, accessibility, and data quality, which are essential for end-users.
By creating a shared understanding of what quality means to your customers and end-users, you set your project up for success.
At Planit, we help our customers rapidly define quality requirements via fast-paced, collaborative, AI-enabled workshops. We assist them in identifying a range of personas that will interact with their software products, and the quality attributes that matter most to each one. Quality risks that could prevent each quality attribute from being achieved are then considered.
By identifying quality goals and potential pain points, teams can make targeted adjustments to their lifecycles to enable the consistent delivery of high-quality software for their customers. We then assist our customers with visualising the results via actionable frameworks that can be implemented and communicated to stakeholders. This shared understanding empowers teams to proactively embed quality into every stage of development, minimising rework and accelerating delivery.
Agile isn’t agile without iterative quality
Truly agile delivery depends on iterative quality – building quality in from the start and validating it continuously. Many organisations advocate for “shifting quality left,” yet delay the involvement of testers and quality engineers until development is already underway. This reactive approach means that gaps in requirements and architecture – issues that could have been identified and resolved earlier – end up embedded in designs and code. The resulting rework is rarely accounted for in project budgets, leading to slower, more expensive software delivery.
Ultimately, you can’t test quality into a system – you can only build it in. Quality should be engineered into products from the very beginning and evaluated continuously throughout every stage of the lifecycle.
Engaging quality engineers early, ideally during creation of the business case and requirements, provides key advantages:
This engagement ultimately enables a whole-team approach to quality, reducing rework and enabling truly agile delivery. The result is more frequent releases, reduced costs, and greater confidence in the software’s reliability and performance.
Adopting a one-team mindset
Delivering high quality at pace requires a one-team mindset across all contributors. Quality often deteriorates when the agile lifecycle becomes transactional and teams operate in silos, causing an increase in misunderstandings, defects and delays. The root cause isn’t a lack of tools; it’s a challenge of leadership, process, and mindset.
The solution is to proactively design your ways of working to eliminate siloed, selfish or “us and them” thinking. Whether your resources are onshore, offshore, in-house or vendor-based, it’s essential to treat everyone as a single, unified team – particularly in multi-product transformation and modernisation programs. Shared goals, joint participation in agile ceremonies and collective accountability for quality are critical to success.
Achieving this requires deliberate effort and thoughtful planning. Meetings must be scheduled at times that respect all regions, including release and sprint planning, daily stand-ups, backlog refinement and retrospectives. Collective communication must be clear, frequent, and genuinely two-way, so that context is shared and not assumed. Program test management responsibilities need to sit across teams, to create a single, unified view of the state of quality. Most importantly, trust and relationships must be built by treating everyone as one team.
Even when multiple teams, consultants, contractors and vendors are involved, adopting a one-team mindset is the key to removing blockers, improving quality, and accelerating time-to-market. The strength of your delivery lies in your ability to work as one cohesive unit.
Treating test data quality and security as non-negotiable
Test data quality and security are non-negotiable foundations for effective QE. It’s important to recognise that data is both a powerful accelerator and a potential blocker for effective software delivery. Modern teams must be equipped to rapidly mask production data or generate representative test data sets. This capability is essential for realistic system and acceptance testing, as it allows teams to validate functionality without exposing the business to regulatory or operational risk. When done well, it dramatically shortens feedback loops and enhances software quality, even as early as unit testing by developers.
Data constraints become even more critical in global delivery environments, where production data cannot be shared offshore during development and testing. Without a robust data management strategy, appropriate tooling, and the necessary expertise, testing slows down, coverage drops, business risk increases, and project delays occur.
In today’s AI-driven, automation-first environment, the importance of trustworthy, secure, and representative data cannot be overstated. Achieving high-maturity, next-generation QE is impossible without a strong foundation in data quality and security. These are not supporting concerns – they are core enablers of high quality and increased speed.
Faster, more reliable delivery starts with NextGen QE
NextGen QE delivers value only when it is embedded into the lifecycle, not bolted on at the end. There’s growing interest in NextGen QE – a modern approach to building and testing digital products that uses automation and AI throughout the entire development process. The aim is to deliver software faster, with fewer defects, lower costs, and greater reliability.
However, organisations often misunderstand how to adopt this approach. Many treat NextGen QE as something to add later in the project, introducing automation and AI-based testing only once the testing phase begins. This approach rarely delivers the required benefits.
NextGen QE delivers results only when it is built into the lifecycle, with a true shift-left foundation. Success begins with the early definition of quality, developing high-quality requirements, appointing experienced quality engineers from the start of the lifecycle, providing appropriate funding for QE activities and tooling, and ensuring the lifecycle is geared towards building and evaluating quality continuously, automatically, and with an AI-enabled approach.
Attempting to layer NextGen QE solutions onto a broken or incomplete lifecycle will not solve underlying issues. In fact, automation, AI, and quality-first approaches can amplify existing problems if the right foundations are not in place. To realise the full benefits, treat NextGen QE as an evolution of a well-designed lifecycle, not as a quick fix.
Discover the other articles in this series, which cover everything from common pitfalls in test automation to unlocking the full potential of GenAI in testing.
Read them here
Practice Director for Quality Engineering
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