Are we proceeding in the right direction to establish a 'good' QE practices?"

In the ever-evolving realm of AI, a great shift has unfolded. Companies are charging ahead with AI-first products, aiming to supercharge output, productivity, and revenue to high proportions. But here's the kicker: this is just the tip of the iceberg. The investments have only just begun, and by investments, I mean investments of time, effort, and the relentless pursuit of groundbreaking ideas to empower AI for both themselves and their customers.

This monumental shift has stirred ripples of intrigue within the Quality Engineering (QE) domain. QE isn't a novelty; it's a dynamic force evolving before our very eyes, promising even greater transformations in the years ahead. It's the linchpin that every organization needs to unlock growth, fitting into the puzzle with three pivotal pieces:

Culture.

Visibility.

Efficiency.

Now, let's take a closer look. But before we dive in, let's unravel some of the challenges lurking within the grand tapestry of our current journey.

1. The QE vs. QA Divide: A Grand Canyon of Perspectives

QE is all about "Building it right," while QA centers on "Testing if it's right." Diverse viewpoints and interpretations abound in this realm. Imagine a day when we all converge to discuss this; we might ignite debates.

2. Unifying Force of QE vs. QA's Solitude

To be brutally honest, I'm perplexed by the designation of teams as "QA." Who are they to bear the sole responsibility for product and service quality? Shouldn't quality be everyone's concern? The challenge emerges when we endeavor to persuade other teams to participate in planning and strategic dialogues.

3. A Balancing Act: Customers, Efficiency, and the Uncertain Path

Ideally, there should be no debate—our focus should laser in on delivering value to customers while engineering an efficient, future-ready landscape. However, talk is one thing; the path to action isn't always straightforward because there's no one-size-fits-all approach.

Now, let's circle back to the core pillars that make Quality Engineering the lifeblood of our endeavors.

The enigma of culture hinges on the curtains of individuals, their ideologies, and their backgrounds. Diverse minds unite to solve problems, but what worked in the past may no longer suffice. The crux lies not in the problem itself but in the mindset—the illusion that some ideas are eternally relevant when, in fact, they are not.

It's time to pose critical questions:

- Are we equipping our teams with the right arsenal of tools, methodologies, and technical acumen to seamlessly infuse Quality Engineering practices?

- Are we nurturing and dedicating adequate time to cultivate this mindset throughout their engineering journey?

- Do we possess the visionary leadership required to craft products that are both reliable and delightful for customers?

- Are we prioritizing engineering objectives at the expense of the user experience?

We've traversed this path before. There's no clear beginning, and there's no final destination. Progress is our constant companion. It's never too late for change; indeed, change is the essence of progress.

Culture

"If vision is where you are going, culture is what makes sure you can get there" - Spotify

We all thrive; every single one of us is thinking and talking about culture. This is a good thing because it means we are all willing to embrace change and attempt to find the sweet spot in defining what constitutes a "good culture." There is no one-size-fits-all answer to this complex question.

To make progress, we need to identify some fundamental principles and determine when and where to incorporate them.

When it comes to engineering teams, culture is of paramount importance. It is intricately tied to one of the most critical aspects of Quality Engineering: readiness. The concept revolves around bringing a state of readiness to every phase of the development lifecycle, including development, testing, deployment, monitoring, and the feedback loop.

This readiness-driven culture ensures that teams are not only proactive in addressing quality issues but also prepared to adapt swiftly to changing requirements and challenges. It fosters a mindset of continuous improvement and a commitment to delivering products and services that not only meet but exceed customer expectations.

Incorporating this culture into engineering teams at the right moment and in the right way can have a significant impact on their ability to navigate the ever-evolving landscape of AI and Quality Engineering, ensuring that they remain agile, responsive, and aligned with the dynamic needs of the industry.

A prime example of this is the ability to allow developers to choose the tools or to find a partner who can facilitate this with whatever works best.

Visibility

There is more emphasis on visibility across the engineering pipeline than ever before. Currently, there are numerous platforms available worldwide that enable teams to provide comprehensive data insights that permeate throughout the company. There is a vast realm of untapped potential to explore, but rest assured, we are steadily making progress.

Visibility is the linchpin that enhances our resiliency. This, in turn, bolsters our ability to react swiftly and prioritize effectively. In doing so, we become adept at solving problems with remarkable speed.

However, there is a downside to this relentless pursuit of visibility. Striking the right balance between metrics and meaningful insights is paramount. This directly ties into the above point: CULTURE.

Cultivating a culture that values insightful data and judiciously selects metrics for decision-making can have a more profound impact than blindly adhering to a set of predefined numbers. It's the harmonious blend of visibility and culture that ultimately propels an organization towards success.

Efficiency

This topic often generates a lot of controversy due to all the buzz surrounding AI. However, it's important to clarify that this isn't about replacing people's jobs or accomplishing tasks with just one engineer instead of five. Such a perspective is fundamentally flawed.

Any viable solution demands a significant degree of confronting our current engineering practices head-on, every day. This practice enables us to continually question how we can optimize various aspects of our engineering lifecycle.

List > Question > Experiment > and Rollout.

Often, cross-functional teams can provide added value by challenging fundamental assumptions and pushing the boundaries of what's possible.

The path is not linear, and there is no defined beginning or end. Progress is our constant companion. By aligning culture, visibility, and efficiency, We can thrive in the ever-evolving AI landscape, ensuring we remain agile, responsive, and customer-centric.

I'm excited to be a part of this change and to enable organizations to make informed decisions about selecting the right combination of tools, frameworks, and people to enable Quality Engineering (QE)!