Why engineering quality is the new frontier of brand trust

In today’s crowded digital landscape, trust is one of the most powerful brand differentiators. It’s not just a nice-to-have or a bolt-on, it must be engineered into every interaction, every touchpoint, every pixel. From an engineering perspective, this means ensuring that your digital brand experience is both  technically reliable and ethically sound. This is the foundation of how we must build. 

Trust starts with the basics: your website loads smoothly, your app doesn’t crash, your kiosk interface is intuitive. These seemingly mundane details are, in fact, critical. When a brand’s digital experience falters, even momentarily, it erodes user confidence. And, in a market where consumers have infinite choice, confidence is currency. 

A company like Bunnings deeply understands this. Their trustworthiness isn’t just built on strong branding or excellent customer service – it’s grounded in seamless, consistent technical performance and relatively clear data handling practices. Every interaction seeks to affirm that the brand can be relied upon. 

A tale of two attitudes 

Recently coming to Australia from Germany, I was struck by the cultural difference in attitudes toward personal data. In Germany, there’s a deeply rooted caution when it comes to sharing private information – an attitude shaped by historical context and a strong emphasis on individual privacy. Signal, the end-to-end encrypted messaging app, has significantly higher adoption rates in Germany than it does in Australia. 

By contrast, Australians seem more relaxed about what brands know about them. But that may be changing. With the phasing out of third-party cookies and the rise of stricter privacy regulations globally, brands are now being forced to rethink how they build digital relationships. This isn’t just a shift in marketing – it’s a shift in trust. Consumers are asking new questions: What are you doing with my data? Are you using it responsibly? Can I trust you? 

Miriam Healy

Engineering trust, not just experiences 

For engineers, trust isn’t just a philosophical concept: it’s practical and tangible. It lives in our codebases, our deployment processes, and our data strategies. Bugs, security vulnerabilities, unpredictable behaviour, no matter how minor, can damage a user’s perception of a brand. That’s why, as an engineering director, I treat quality engineering not as a luxury but as a prerequisite for trust. 

The rise of generative AI coding tools adds a new dimension to the trust equation. One of the key tenets of trust is transparency, but large language models are non-deterministic. They don’t always produce the same output twice, and their decision-making is difficult to interpret. What they’re not good at is admitting when they don’t know something, and instead, they often make it up. And when you catch them out, it’s a real trust killer.  

So how do we uphold trust when the systems we build are increasingly complex and, at times, unpredictable? The answer lies in intentional, transparent governance. In my team, we anchor this in mature quality frameworks – practices that don’t just catch issues but build confidence. Even as AI reshapes how we develop, one truth remains: quality in still means quality out. Generative tools may accelerate the work, but they don’t replace the fundamentals. Great engineering prompts come from engineers who know what good looks like – who understand scalability, security, performance, and maintainability at their core. In a fitting loop, we do use AI to test AI – using LLM-as-a-judge, we can now stress-test our systems across a far wider range of scenarios, surfacing edge cases and strengthening resilience. For us, trust is earned through clarity, craft, and a commitment to quality at every level of the experience. 

“Open Box” development builds better software and stronger relationships 

One of the ways we build trust with our clients is through radical transparency. We invite client engineers to embed directly with our teams, encouraging participation at all levels within that one team. We share real-time access to test coverage and results, so everyone is aware of the quality of the software as we engineer it. This “open box” approach invites dialogue and co-creation. It leads to better software, but more importantly, it fosters deeper conversations about what’s possible when trust is mutual. 

When it comes to generative AI coding tools, this level of oversight becomes even more critical. Our engineers, with the explicit permission of our clients, are using these tools to accelerate delivery, but within guardrails. AI can write code impressively fast; but if you skip the quality and governance step, you’re not saving time, you’re simply deferring the cost to a messy debugging process down the line. Trust in AI tools isn’t about magic; it’s about process. 

The road ahead: Trust, AI, and human oversight 

Will we ever take the human hand off the wheel entirely, as we’re starting to with autonomous vehicles? Maybe. But if we do, it will need to be backed by hard evidence that AI is more reliable than we are. Right now, it’s brilliant at coding and getting better at engineering – but it’s not there yet. Great engineering is about solving problems in inventive ways. AI tools can reliably generate code that’s been written a thousand times before, but that’s not where the creativity lives. 

Until then, the future of brand trust will be built the way it always has been: through transparency, accountability, and consistency. As engineering leaders, we have a responsibility to ensure that the systems we design are not only innovative but also trustworthy. Because every line of code, every AI output, every user interaction is an opportunity to reinforce that trust, or to lose it. 

In today’s era of informed and sceptical consumers, trust is no longer just a value, it’s a strategy. And the brands that earn it consistently, through both their words and their engineering, will be the ones that stand apart. 

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