Don’t just “assure” quality after the fact; “engineer” quality into the software development lifecycle

Quality assurance is at a crossroads. It must support the new complexities of cloud-native transformation and AI, even though many organizations have just a moderate maturity in quality and are reluctant to make the necessary investments. Consequently, a commodity- and technology-centric mindset endangers progress on organizations’ transformation journeys. While it is comparatively easy to postulate innovative concepts, it is difficult to drive transformation. Therefore, what can we learn from organizations that have successfully progressed in their transformations? What lessons have they learned, and which best practices can benefit other organizations?

There is a scarcity of compelling thought leadership on progressing from quality assurance to quality engineering. To better understand how organizations capture business value from their investments in quality, HFS Research, in partnership with Wipro, in Q1 2023, expanded on its research for the HFS Horizon: Assuring the Generative Enterprise™, 2024 by interviewing quality assurance leaders of Global 2000 enterprises. The goal was to learn from their transformation journeys and capture best practices. Supplementing this were deep-dive interviews and discussions with Wipro’s quality engineering and cloud leadership teams, who shared their experiences and findings on best practices.

Key findings

- North Star must be aligned to maturity levels
  As with cloud-native transformation, the North Star on quality assurance for each enterprise has to be specific to the industry challenges and the maturity of the organization’s quality assurance function. Critically, that vision must focus on aligning technology and business objectives.
- Quality engineering must be embedded in the software development lifecycle
  For most enterprises, becoming cloud native is a significant part of that North Star. AI is the extension of those desired capabilities. Yet, there must be a realization that moving to quality engineering is not only about upskilling talent. It is about working differently. It is about owning the products and services delivered and collaborating among cross-functional teams with flat hierarchies. It is about a completely different working culture.
- Reskilling remains the most significant challenge
  As the quality engineering leader of a UK bank compellingly explained, moving to quality engineering is not about upskilling talent but evolving how professionals work. People need to progress from primarily being manual testers to becoming quality engineers in a world of increasingly AI-enabled automation, where shrinking work units must be reassembled to align with business objectives.
- GenAI will augment operating model change
  Progressing to quality engineering requires defining a North Star that blends business objectives with a realistic talent strategy. GenAI adds more complexity to those challenges. While the enterprise adoption of GenAI in quality assurance is still nascent, there are profound implications for designing the operating model. Perhaps the most significant impact will be the compression of innovation cycles and software development. Without first progressing to quality engineering, enterprises will be incapable of leveraging those new capabilities.
- Organizations must prepare for profound AI-related disruption
  To achieve those outcomes, they must design their target operating model. However, they must also prepare for disruption through the broader evolution of AI, not just a myopic focus on GenAI. The ultimate goal must be to progress toward business assurance to ensure the outcomes of services delivered by AI.

Quality assurance must pivot to business objectives that support the disruptive shift to cloud-native transformation and AI. This requires large-scale reskilling of workforces and complex new operating models. To help enterprise leaders develop more effective strategies and enable progress on their transformation journeys, HFS has curated best practices from organizations that have successfully progressed toward their North Star. These best practices are built on the belief that progressing to quality engineering is aligned with becoming cloud native. Ultimately, it is about business transformation. Success on that journey requires aligning technology and business objectives.

The bottom line is equally clear: People and culture must be top of mind for a productive and successful pivot to business transformation. Organizations must avoid becoming bogged down in capabilities discussions. This is the time to define and align business and technology objectives decisively. The biggest and most powerful levers for ensuring progress on transformation are reskilling quality engineers and designing operating models that can support the new complexities of cloud-native transformation and AI.

Don’t just “assure” quality after the fact; “engineer” quality into the software development lifecycle

Key findings

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