To build with Generative AI in mind, whether you're creating something entirely new or layering GenAI into existing products, the decisions you make early on can shape everything, from infrastructure to user experience.
This article looks at some of the shifts engineering teams need to make like designing for uncertainty, rethinking developer roles, and focusing on real user value.
Start with the User, Not the Tech
A good place to begin is by recognizing that integrating GenAI into engineering isn’t the same as adding it to a business process or workflow. There are two key questions to ask:
First, what’s the job the user is trying to get done and how could GenAI make that more efficient? Second, are there jobs users need done that simply weren’t possible before but now can be unlocked with GenAI?
That’s the real power of this technology. It’s not just about automation or enhancement. It’s about scaling capabilities in ways that were previously out of reach. And that means product decisions should start with user needs, not with features or systems you’re trying to retrofit.
Building from Scratch vs. Retrofitting Legacy Products
To integrate GenAI, there's a significant difference between starting from scratch and working with legacy systems. Both approaches have advantages and disadvantages, and as an engineering leader, the path you choose influences everything from infrastructure to user experience.
Consider dashboards, for example. Previously, users had to rely on a predefined set of views or know how to write SQL to obtain the necessary insights. That resulted in either limited flexibility or a steep learning curve. With GenAI, we can provide something completely new: personalized data exploration via natural language. Users can ask questions like they would in a conversation and receive real-time answers suited to how they want to understand their data.
This kind of flexibility brings personalization at scale without the need to build infinite custom views. And more importantly, it offers every user the ability to move faster, save time, and reduce cost.
The Challenge of Integrating GenAI into Legacy Products
Introducing GenAI into a legacy product is a difficult engineering decision. You're dealing with an established system, a loyal user base, and a product that already provides value. Introducing something as powerful as GenAI needs a delicate balance of innovation and stability.
There's a parallel here with how keyboard layouts were designed, not because they're the most efficient, but because they worked for typewriters and we adapted to them. Similarly, legacy interfaces frequently reflect the constraints and assumptions of each period. GenAI provides an opportunity to rethink those interfaces, but it also requires changing habits and expectations.
The most significant change, in terms of engineering, is mental. Traditional software is deterministic: either something works or it doesn't. However, GenAI introduces probability-based behaviour. It's not always about whether the answer is correct, but rather whether it's correct enough for the situation. This needs new ways of thinking about testing, validation, and user confidence.
Instead of simply layering GenAI onto existing UIs, teams must take a step back and ask: what is the job to be done? What interface is appropriate for that task in a world where users can interact with the system using natural language or dynamic outputs?
That transition from black-and-white correctness to nuanced, context-aware solutions is what makes integrating GenAI into legacy products both difficult and exciting.
How GenAI Is Shaping Developer Workflows
One of the most immediate areas where GenAI is making an impact is in engineering itself. Developers across the board are starting to use GenAI tools, but adoption isn’t uniform yet.
Some teams, like frontend developers, are seeing significant productivity gains, up to 50–60% in certain cases. That’s because the tooling for web development is more mature and the codebases are more standardized, making it easier to generate useful, high-quality outputs. It’s not just about writing tests or boilerplate code, it’s also about improving consistency, code quality, and velocity.
On the other hand, areas like mobile development haven’t seen the same level of uplift yet, simply because the tools aren’t as built out. But even there, progress is happening.
What’s interesting is how GenAI is shifting the role of the developer. Instead of just writing code from scratch, developers are becoming more like editors, reviewing, refining, and elevating the work. It's similar to how better cameras didn't suddenly make everyone a great filmmaker. The tools are powerful, but it's still the developer’s skill and judgment that determine the final outcome.
GenAI allows engineers to zoom out and think more strategically about their code: how it scales, how it’s maintained, how others will read and extend it. It pushes the craft forward, enabling both deeper technical focus and broader architectural thinking.
What Happens When Developers Gain Time Back?
As GenAI tools make developers more efficient, a natural question arises: where does that time go? If writing and testing code takes less time, what fills the gap?
There are two big changes happening.
First, smaller teams, or even individual engineers, can now take on problems end-to-end. Instead of needing three or four developers to build and validate a solution, one person can often handle the full workflow. That means deeper context, faster feedback loops, and a stronger connection to the business problem being solved.
Second, iteration speed has increased dramatically. Tasks that used to take weeks, like designing and validating machine learning models, can now be done in days. Engineers aren’t just writing code faster; they’re delivering value faster.
And as a result, developers get closer to the impact. They’re not just shipping features, they’re solving real problems. That proximity to the business opens up new opportunities: picking up more projects, influencing product direction, and, in some cases, stepping into more functional roles.
There’s no shortage of problems to solve. The difference now is that GenAI gives developers the time, tools, and autonomy to go after them.
From Rules Engines to Natural Language
In the past, rules engines were touted as a way to offload business logic from developers. The idea was to give non-engineers the tools to define behaviors and conditions and freeing up engineering time for more complex work. But in reality, rules engines never became mainstream. Most teams stuck with hardcoded logic because the alternative often felt clunky, limited, or too disconnected from the real engineering flow.
Now, with GenAI and better tooling, we’re seeing a new kind of direction—not toward traditional rules engines, but toward smarter scaffolding. Think of it like frameworks evolving: at first, everything had to be built from scratch. Then came structured approaches that helped teams move faster without reinventing the wheel.
That’s where we’re heading again. GenAI is reducing repetitive tasks and allowing engineers to focus on higher-order thinking, mapping business needs into scalable technical solutions. You spend less time wiring things together, and more time understanding where to personalize, where to abstract, and where to scale.
Measuring Success with GenAI
With all the hype around GenAI, one of the most important questions is: How do you measure success?
Whether it’s a proof of concept or a production release, every GenAI project should be evaluated by its impact on the user or in simpler terms what customer value it brings to the table:
- Are you unlocking something new?
- Is the user experience better?
- Are admins and decision-makers happier?
Key metrics like NPS, adoption rates, and customer satisfaction are what matter. If those numbers aren’t moving, you’re not doing it right.
The good news? Cost is becoming less of a blocker. The economics of GenAI are improving rapidly. The bigger problem now isn't money, it's being clear on what the goal is. Know the problem you're trying to solve and who you're trying to solve it for.
Lessons Learned from the Journey
As teams go deeper into integrating GenAI, there are definitely some lessons that start to emerge, things you only realize once you’re in the thick of it.
One big insight: once you get GenAI working well in one language, translation to other languages becomes almost effortless. That’s a huge advantage, especially for companies looking to scale globally. GenAI helps you build smarter tools and opens up markets you might not have reached otherwise.
Another key lesson is around guardrails. In traditional engineering, you build strict, rule-based systems to catch every possible failure. But with GenAI, that’s not always practical. A smarter approach is to use another GenAI model to monitor behavior. Instead of trying to predict every way things could go wrong, you create systems that can recognize and correct issues dynamically. It’s a shift in mindset, and it often leads to more resilient solutions.
And maybe the biggest takeaway: don’t try to straddle both worlds. If you treat GenAI as a bolt-on or run half your stack in old-school mode and half in GenAI mode, things get messy fast. But if you go all-in, if you design from the ground up with these tools in mind, it gets simpler and more powerful. The tooling works better, the architecture makes more sense, and the benefits are more obvious.
That’s the real power of committing to the path: you start seeing possibilities you couldn’t even imagine before.
Final Thoughts
From legacy system integration to improving developer workflows, from shifting team mindsets to designing entirely new interfaces, this article barely scratched the surface of what’s possible when you bring GenAI into engineering. There’s a long road ahead for every company in this space
Building with Generative AI is about rethinking how we build software ranging from interfaces to infrastructure.
To succeed, engineering teams need to:
- Start with user needs, not features
- Redesign workflows for probabilistic outcomes
- Embrace the evolving role of developers
- Go all in on GenAI-first architecture
- Stay laser-focused on delivering customer value
How Solwey Can Help
Solwey is a boutique agency established in 2016 focusing on customers' success through excellence in our work. Often, businesses require simple solutions, but those solutions are far from simple to build. They need years of expertise, an eye for architecture and strategy of execution, and an agile process-oriented approach to turn a very complex solution into a streamlined and easy-to-use product.
That's where Solwey comes in.
Our seasoned team of experts blends innovation with a deep understanding of technology to create solutions that are as unique as your business. Whether you're looking for cutting-edge ecommerce development or strategic custom software consulting, our team can deliver a top-quality product that addresses your business challenges quickly and affordably.
If you're looking for an expert to help you integrate AI into your thriving business or funded startup get in touch with us today to learn more about how Solwey can help you unlock your full potential in the digital realm. Let's begin this journey together, towards success.
