On a product team, there are few relationships more symbiotic than the one between AI tools and the engineers who utilize them.
Like a field of flowers and a colony of honey bees, engineers integrate AI into their products and processes to boost productivity, enhance product design, identify inefficiencies and reap other benefits. At the same time, engineer ingenuity helps push the boundaries of what is possible with AI tools, ultimately expanding and improving AI’s potential use cases for future products and the engineers who will create them.
On the CreatorIQ team, various AI tools help accelerate product prototyping, and as engineers examine new ways of working, previously unattainable opportunities are becoming more viable for their product roadmap. As they look toward the future, AI can also help non-engineer teammates have an active hand in low- and no-code work environments.
At financial technology company Moov, the encouragement of using AI as a coding “co-pilot” has helped teams experiment and learn faster than before, allowing them to deploy products swiftly and find new applications for cutting-edge fintech. The company’s clear directive for employees to utilize AI tools as collaborators in their human-powered work has laid the groundwork for the Moov team to continue exploring new tech with a conscience.
Built In spoke with product leaders from both companies to understand how they have integrated AI into their engineering teams, what challenges they see so far and what excites them about the future of engineers working hand in hand with AI tools.
CreatorIQ is an enterprise-grade influencer marketing platform for thousands of innovative brands and agencies.
How does your team use AI in the engineering design process, and what benefits have you observed?
Our technical teams are exploring how domain-specific languages — LMQL, MS Guidance, etc. — and agentic frameworks like Haystack and LangChain can power semantic search and chat software outcomes at scale. Our operations teams across departments also leverage visual flow composers to rapidly prototype high-impact automation. When we prove concepts in AI workflow automation tools like n8n, we can re-evaluate our roadmap backlogs. Recently, “Won’t Do” items have been dragged back into “Evaluating.” It really is an exciting time.
“Our technical teams are exploring how domain-specific languages — LMQL, MS Guidance, etc. — and agentic frameworks like Haystack and LangChain can power semantic search and chat software outcomes at scale.”
What challenges have you encountered when implementing AI technologies in engineering design, and how have you addressed them?
The challenges of working with these systems mirror traditional machine learning operations challenges. Yes, hallucinations and reproducibility errors occur, and so do issues with fine-tuning data sets like overfitting and underfitting, data drift, etc. Applying rigor to the accurate measurement of these systems is the only way to determine whether they are good enough for production use.
What excites you most about your team’s future when it comes to leveraging AI in innovative ways?
New technologies — many rooted in the emergent capabilities of generative AI — are challenging conventional solutions architecture at CreatorIQ. Different teams are playing with tech at different levels of abstraction. And we are particularly excited about how low- and no-code platforms are democratizing automation outcomes for non-engineer colleagues.
Moov provides a single API payment platform that allows businesses to accept, store, send and spend money in one place.
How does your team use AI in the engineering design process, and what benefits have you observed?
Our team leverages AI to enhance various stages of the engineering design process. For example, we use AI-driven tools embedded into our integrated development environments to automate repetitive tasks, such as generating boilerplate code or optimizing configurations, freeing up engineers to focus on higher-value activities.
Additionally, during the design phase, AI serves as a powerful resource for education and research. Team members utilize AI to dive into industry-specific or technical information, gaining a deeper understanding of the challenges they’re solving. Whether it’s used for exploring new programming frameworks, understanding complex regulatory requirements or reviewing detailed documentation for similar systems, AI accelerates knowledge acquisition and ensures the team is well-prepared before implementation begins.
Moov uses AI to automate and enhance learning, which improves efficiency and promotes creativity. This empowers the team to tackle design challenges with clarity and confidence.
What challenges have you encountered when implementing AI technologies in engineering design, and how have you addressed them?
In the payments space, one of the biggest challenges is the limited availability of publicly accessible data sets. This makes it difficult to train AI models on relevant data or validate their outputs effectively. To address this, we encourage best practices for validating AI outputs such as cross-referencing multiple sources and relying on team expertise to scrutinize AI-generated insights. While this requires extra effort, it helps ensure that AI contributes reliable value to our engineering design process.
Another challenge is maintaining a balance between AI automation and human oversight. While AI excels at speeding up tasks like code generation or research, it occasionally makes suggestions that may not align with our design principles or goals. We mitigate this by embedding AI in a collaborative workflow where it acts as a co-pilot rather than an autonomous decision-maker.
Adopting AI tools has required cultural adjustments. Some team members were initially skeptical of relying on AI or adapting to new workflows. We’ve addressed this by promoting transparency around AI tools, offering training and encouraging experimentation, which has turned AI into a trusted ally.
“We’ve promoted transparency around AI tools, offering training and encouraging experimentation, which has turned AI into a trusted ally.”
What excites you most about your team’s future when it comes to leveraging AI in innovative ways?
What excites me most is the transformative potential of AI to redefine how we approach engineering problems. With the rapid evolution of AI capabilities, we see opportunities to build systems that adapt to change and anticipate it. Imagine an AI-powered platform that dynamically optimizes payment routing or predicts and mitigates fraud in real time — these are possibilities we’re actively contemplating.
The ability to scale innovation quickly is a thrilling prospect. AI can turn what used to take months into something achievable within days, enabling us to test and deploy new features faster than ever. Moreover, as we integrate generative AI into our development processes, we anticipate uncovering entirely new use cases that push the boundaries of what’s possible in fintech.
Ultimately, the relationship between human creativity and AI’s computational power drives our excitement, keeping us at the forefront of innovation in the payments space.
