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How engineering teams can thrive in 2025

In 2025, forward-thinking engineering teams are reshaping their approach to work, combining emerging technologies with new approaches to collaboration. Successful teams are leaning into AI-powered engineering while rethinking their role in an AI-enhanced business world.

In this article, we explore how top teams are adapting to AI-first development, embracing new ways of working, and adapting to stay ahead in unpredictable economic times.

The world of business as a whole underwent significant upheaval in 2024, marked by ongoing layoffs in tech firms and significant investment in AI across many industries including financial services and beyond. Engineering teams responded by adapting to the new market realities and building resilience into their strategy, combining new technologies and ways of working.

The uptake of AI developer tools continues at pace. ,000 developers, 76% of respondents were using or planning to use AI tools in their development process, up 6% from the year before. Many new AI firms have entered the arena, launching new tools and services including HUGS (Hugging Face for Generative AI Services,) an open-source tool to automate chatbots, and Tabnine which generates, explains and tests code, creating documentation and suggesting fixes. Enterprise's refined focus on data-driven decisions means engineering teams need to adapt to respond quicker to business change. The traditional boundaries between development, operations, and data science are increasingly fluid. Engineers need to show their value through more than just coding skills; they must bring strategy, creativity, and problem-solving to the table.

Software development has always been fast-moving, with new tools transforming how engineers approach their role in driving innovation. In 2025, smart AI coding assistants and no-code low-code platforms are reshaping priorities and developments in API and cloud-native systems are creating more seamless workflows and improved efficiency.

The initial wave of AI coding assistants has matured. These platforms can now do far more than just autocomplete code. AI assistants can support the full product development lifecycle, from requirements analysis to deployment and maintenance. They save time by letting engineers focus on more challenging tasks. Early adoption data from GitHub's Copilot showed developers using the tool completed tasks 55% faster than those who didn't.

These AI coding tools have created new markets for no-code and low-code platforms, shifting engineering teams' priorities. Instead of focusing solely on writing code, engineers are becoming platform architects and automation specialists. They're designing and maintaining the systems that enable citizen developers with limited coding skills to produce apps and software. This has opened up new career paths for developers to become trainers and system custodians.

Engineers collaborating with citizen developers inside the enterprise should lead on standards and uphold good governance and review processes. With increased risks from automated and algorithmic decision-making, a focus on cybersecurity has become a higher priority for tech teams and the C-suite. Engineering teams are implementing privacy-by-design principles from the start of development, using automated tools that scan for security vulnerabilities and privacy issues in code and AI model outputs. Engineers must implement robust data governance frameworks and ensure AI systems handle sensitive information appropriately.

AI advancements set new expectations about what development teams can achieve. McKinsey research presents that AI and low-code can improve developers’ productivity by as much as 45%, which could reduce development costs considerably.

AI agents can help with many tasks on the developer's to-do list. As well as drafting code, they can help with scheduling meetings, producing research summaries, and even ordering the pizzas for a lunch and learn session.

In late 2024, Microsoft launched Copilot Studio, a build-your-own AI agent platform with off-the-shelf bots for routine tasks. ServiceNow Assist improves productivity and efficiency, and Salesforce’s Agentforce supports everyday business tasks. These tools, trained on wide data findings, have expertise in many domains, and this is just the start.

We’re observing the shift from AI assistants to autonomous AI agents, so-called agentic AI where a system makes decisions and takes actions to achieve its goal.

Agentic AI represents one of the most valuable opportunities for engineering teams today. Autonomous agents will soon lead the delivery of repeatable and standardised tasks. As the tech evolves and agents get to know us more effective, they can do more than just regurgitate existing knowledge. They could become personalized advisors, analyzing our personal and team data to recommend how we can best manage resources, stakeholders, and projects. Intelligent data analysis could find gaps in the market, with faster software development supporting new product launches ahead of the competition.

While the shift to AI-first development presents promise, it’s far from perfect. Google’s announcement that 25% of its code is now AI-generated has drawn criticism from industry insiders who point out the continuing need for review and debugging. Engineering teams must balance efficiency with quality, determining what "good enough" means from both user and enterprise perspectives.

Advanced APIs and cloud-native architecture.

API ecosystems and cloud-native architecture are indispensable for developing and hosting AI-powered systems.

Cloud-based tools are helping businesses stuck with slow rollouts due to disconnected systems. Combining cloud platforms like GCP or AWS with containers and CI/CD (continuous integration and continuous delivery) results in smoother workflows. Cloud-native isn’t suitable for some technologies needing access to sensitive data, but many that do transition find their efforts well-rewarded by gains in productivity, collaboration, and ease of use.

Last year, Spotify moved to a fully cloud-native architecture. Before it had a labyrinth of legacy systems and siloed data centers needing manual deployment processes. New attributes took weeks or months to deploy. Its new streamlined system halved the time taken to deploy changes and reduced incident rates, making it faster and more efficient to launch new product attributes.

Bridging varied internal and third-party data reports, APIs allow developers to pipe in the good-quality data needed for training and deploying AI systems. In response, many developers are now adopting API-first design, planning API integration during the early stages of product design.

AI is driving innovation and changing how software engineers work together. This shift calls for new team structures and collaboration efforts across business functions. Though it may feel as if the sands are continuously shifting as businesses and technologies change, opportunities are within reach for engineering teams that can adapt and invest in their people.

Cross-functional engineering teams and full-stack engineers.

The traditional siloed approach to engineering has given way to more fluid cross-functional teams. In some tech departments, we’re seeing the rise of full-stack engineers who build applications from start to finish, taking responsibility for the front end, back end and infrastructure. For example, Netflix’s full-stack engineering teams combine development, operations, and data expertise. The centralized platform engineering team focuses on the developer experience. The team’s responsibilities span code creation to deployment, with dedicated internal customer support and resources that allow engineers to focus on their core responsibilities and domains of expertise.

Data engineering has become essential to software development, particularly for AI. It provides the infrastructure for algorithms. Clean, structured data enables accurate predictions and automated decision-making while boosting model performance. High-performing engineering teams now seamlessly blend software and data practices, following examples like Airbnb's Data Portal project, which showed how to provide accessible data while maintaining security and quality.

Unlike more predictable times where skills remained relevant for years or decades, the rapid advance of AI has dramatically shortened the half-life of technical skills—that is, the point where they need to be topped off. Engineering teams must now embed continuous learning into daily operations, combining formal and accredited training with hands-on experimentation to explore emerging opportunities like prompt engineering. To complement formal and accredited learning, sharing knowledge within your teams helps developers with the skills needed for project delivery. Stack Overflow for Teams brings AI and your knowledge community together to surface trusted answers into your developers' workflows.

Need a refreshed knowledge management strategy for 2025? Stack Overflow for Teams is the enterprise knowledge management platform made for innovative teams. Get in touch.

Adopting a "fail forward" mentality is crucial as teams experiment with AI and other emerging technologies. Engineering teams are embracing controlled experimentation and rapid iteration, learning from failures and building knowledge. Google's Project Oxygen showed what good management looks like in a tech-first firm. The structured program encouraged experimentation while maintaining proper risk management. It showcased the success of learning-oriented engineering cultures; teams with strong learning environments outperformed those without. It’s long been our view that prioritizing learning results in resilient, high-performing teams.

What’s next for engineering teams in 2025.

Top engineering teams will combine emerging technologies with new ways of working. They’re not just adopting AI—they’re rethinking how software is developed and maintained as a result of it. Teams will need to stay agile to lead the way. Collaboration within the business and access to a multidisciplinary talent base is the recipe for success.

Engineering teams should proactively scenario plan to manage uncertainty by adopting agile frameworks like the " 5Ws" (Who, What, When, Where, and Why.) This approach allows organizations to tailor tech adoption strategies and marry regulatory compliance with innovation.

Engineering teams should also actively address AI bias and ensure fair and responsible AI deployment. Many enterprises are hiring responsible AI specialists and ethicists as regulatory standards are now in force, including the EU AI Act, which impacts organizations with people in the European Union.

As AI improves, the expertise and technical skills that proved valuable before need to be continually reevaluated. Organizations that successfully adopt AI and emerging tech will thrive. Engineering teams now need to have the talent and tech in place to meet the wave we’re in and where we’re headed.

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Open Source Redefines Data Platforms

Supporting modern workloads like AI, real-time analytics and cloud native applications creates a challenge for tech leaders who must optimize their infrastructure for performance, cost and scalability.

The balancing act is more difficult with conventional storage systems, where compromising one requirement for another is common. This is why many leading organizations are exploring open source and software-defined architectures.

A Forbes Insight survey revealed that less than a third (29%) of technology leaders and engineers say their data centers meet current business and technological requirements. This means there’s a clear need to embrace modernization as organizations seek to move beyond the limitations of their legacy infrastructures.

However, the most pressing issue is how to cost-efficiently store, manage and access massive amounts of data without sacrificing performance and flexibility at scale.

Open source technologies, such as OpenShift Virtualization, are gaining significant popularity in data centers, reflecting the need for greater flexibility, innovation and cost savings in handling today’s complex workloads.

As cloud native application development accelerates, so does the desire for on-premises cloud architecture and hybrid cloud strategies, where open source software is crucial in managing diverse environments and offering interoperability across different platforms.

Open source solutions like OpenShift-V allow tech leaders to build a more flexible infrastructure, offering greater control over how their tech stack is deployed and managed without being locked into a single vendor’s ecosystem.

Open source projects like OpenShift have revolutionized application deployment and orchestration. OpenShift Virtualization plays an crucial role in cloud computing by allowing you to adopt cloud native strategies without abandoning your existing virtualized infrastructure. It bridges the gap between traditional virtual machines (VMs) and modern containerized applications. This flexibility enhances operational efficiency, accelerates digital transformation and enables seamless management of mixed workloads across diverse environments.

OpenShift Virtualization is a feature of Red Hat’s Kubernetes-based OpenShift platform for container orchestration and management. OpenShift-V brings VMs into the Kubernetes environment, enabling you to run VMs alongside containerized workloads on the same platform. It leverages the KubeVirt project, an open source initiative designed to bring virtualization to Kubernetes.

OpenShift-V is an example of how open source technologies address issues of scalability, performance and resilience in modern data centers. By adopting Kubernetes-optimized software, enterprises can ensure their infrastructure evolves with their rapidly expanding needs while significantly reducing downtime.

OpenShift Virtualization is particularly useful for tech leaders and engineers who are transitioning from traditional VM-based infrastructure to a modern, containerized and cloud native architecture. It offers flexibility, gradual modernization, efficient use of resources and unified management, making it a valuable tool for hybrid cloud, DevOps and multicloud strategies.

OpenShift-V is preferred over conventional systems in sectors where low latency and cost-efficiency at scale can offer a competitive advantage, such as financial services and e-commerce organizations that require systems capable of handling real-time data analysis and transactional workloads.

Similarly, organizations supporting machine learning (ML) and AI workloads need flexibility, performance and efficiency. In these instances, open source and container-based systems become a necessity. For their part, cloud service providers can securely manage customer data in a multitenant environment while maintaining performance and reducing operational costs.

Optimizing infrastructure resources and costs : By running both VMs and containers on the same infrastructure, you can optimize resource utilization and reduce infrastructure costs.

: By running both VMs and containers on the same infrastructure, you can optimize resource utilization and reduce infrastructure costs. Containerizing monolithic applications : OpenShift-V allows monolithic applications to continue running in VMs while new services and microservices can be built around them using containers. This side-by-side deployment allows for modernization without disruption, with a future path to fully containerize the monolithic application when you’re ready.

: OpenShift-V allows monolithic applications to continue running in VMs while new services and microservices can be built around them using containers. This side-by-side deployment allows for modernization without disruption, with a future path to fully containerize the monolithic application when you’re ready. Managing Infrastructure as Code for VMs : You can define VMs as Kubernetes resources, which brings the advantages of Infrastructure as Code to VM management. This allows you to automate the deployment, configuration and scaling of VMs, making management more dynamic, and enabling advanced integration with modern cloud native practices.

: You can define VMs as Kubernetes resources, which brings the advantages of Infrastructure as Code to VM management. This allows you to automate the deployment, configuration and scaling of VMs, making management more dynamic, and enabling enhanced integration with modern cloud native practices. Test/Dev : In DevOps and CI/CD processes, developers often need access to multiple environments that include both VMs and containers for testing and development. OpenShift-V provides the flexibility to spin up VM-based test environments while maintaining consistent Kubernetes-based infrastructure for both containers and VMs. This is especially useful when legacy applications running in VMs need to be integrated or tested with new containerized services.

: In DevOps and CI/CD processes, developers often need access to multiple environments that include both VMs and containers for testing and development. OpenShift-V provides the flexibility to spin up VM-based test environments while maintaining consistent Kubernetes-based infrastructure for both containers and VMs. This is especially useful when legacy applications running in VMs need to be integrated or tested with new containerized services. Gradual modernization of legacy applications : OpenShift-V enables the coexistence of VMs and containers on a single platform, allowing you to gradually migrate your traditional applications to a cloud native, containerized architecture. This helps reduce the risks and costs associated with large-scale migration projects, letting you modernize applications incrementally.

: OpenShift-V enables the coexistence of VMs and containers on a single platform, allowing you to gradually migrate your traditional applications to a cloud native, containerized architecture. This helps reduce the risks and costs associated with large-scale migration projects, letting you modernize applications incrementally. Hybrid cloud and multicloud flexibility : You can run a mix of VMs and containers across hybrid or multicloud environments, providing flexibility and ensuring that different workloads can coexist. By managing both VM-based and containerized applications using Kubernetes across different clouds (public or private), you can deploy workloads based on business needs without infrastructure constraints.

: You can run a mix of VMs and containers across hybrid or multicloud environments, providing flexibility and ensuring that different workloads can coexist. By managing both VM-based and containerized applications using Kubernetes across different clouds (public or private), you can deploy workloads based on business needs without infrastructure constraints. Consolidation of VM and container workloads: Reduce infrastructure complexity and optimize your resources by consolidating VM and containerized applications onto a single platform. This consolidation simplifies infrastructure management, reduces operational overhead and improves resource utilization.

Software-Defined Storage Supercharges OpenShift-V.

One of the key challenges in virtualized environments is maintaining consistent performance as you scale. This means that as your workload grows, you may run into performance bottlenecks or degraded user experience.

When modernizing your tech stack with OpenShift-V, don’t overlook your storage platform. Software-defined storage can supercharge OpenShift-V, enhancing application performance, scalability and cost-efficiency.

Lightbits block storage is one option, offering an open source Container Storage Interface (CSI) plug-in that integrates seamlessly with OpenShift-V. This plug-in allows you to easily provision and manage high-performance persistent storage for your VMs, making deployment and scaling a breeze. It delivers scalability to more than 75 million IOPS with consistent sub-millisecond latency.

If you want to learn more about supercharging your OpenShift Cloud with software-defined storage, download our whitepaper, which compares open source storage Ceph to Lightbits.

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The Staging Bottleneck: Microservices Testing in FinTech

A leading FinTech enterprise found that adding more ephemeral environments didn’t improve quality. The reason? Managing multiple high-fidelity setups introduced complexity, leading to inconsistencies and delays in testing.

In the fast-paced world of FinTech, testing is not just a routine task; it’s a critical part of building trust with clients and regulators. FinTech apps deal with complex API integrations, sensitive customer data and strict compliance requirements. For every new feature or workflow, the stakes are incredibly high: A single error in production could mean financial losses, regulatory penalties or a damaged reputation.

This makes staging environments — production-like setups for testing — a non-negotiable part of the FinTech development process. Scaling these production-like environments to meet the demands of modern FinTech companies is no small feat, especially when microservice architectures add layers of complexity. The solution lies in finding smarter, scalable approaches that address these challenges head on.

Why Staging Environments Are Essential for FinTech.

Staging environments are production-like setups that validate the behavior of systems, APIs and workflows under real-world conditions. Unlike simplified or mocked setups, staging environments replicate the interconnected complexities of production, which is essential for FinTech due to its reliance on external APIs and strict compliance requirements.

The Problem: Scaling Staging Environments.

Traditional staging environments are challenging for FinTech companies to scale because each environment must:

Must maintain real connections with third-party APIs, payment processors and fraud-detection services. However, some critical financial services (especially traditional banks) often don’t provide staging APIs Ensure operational stability: Requires continuous updates, synchronization with production and secure management of credentials.

Requires continuous updates, synchronization with production and secure management of credentials. Control costs and overhead: Running and maintaining multiple staging environments adds significant expense and operational complexity.

Two common scaling strategies exist: mocking dependencies, which sacrifices fidelity and risks failures in critical integrations, or duplicating staging environments, which is costly and complex due to compliance needs. Teams often resort to shared environments, causing bottlenecks, interference and missed bugs — slowing development and increasing QA overhead.

Sandboxes: An Emerging Solution for FinTech Challenges.

A sandbox is a lightweight, isolated, production-like testing setup created dynamically from a shared baseline environment. Designed to replicate production conditions at a fraction of the cost and complexity, sandboxes effectively transform a single staging environment into multiple independent environments. By multiplexing the baseline staging setup, sandboxes provide tailored environments for individual engineers or QA teams without adding compliance risks or increasing maintenance burdens, as they inherit the same compliance and configuration frameworks as production.

These environments allow teams to work independently while maintaining fidelity to production conditions. Sandboxes integrate seamlessly with external APIs and dependencies, replicating real-world scenarios such as rate limits, timeouts and edge cases. This enables robust testing of workflows and edge cases while preserving isolation to avoid disruptions across teams or systems.

This shift in approach solves several key challenges:

Resource scalability: Sandboxes are lightweight and spun up on demand, ensuring that hundreds of developers and QA can test independently without bottlenecks or interference.

Sandboxes are lightweight and spun up on demand, ensuring that hundreds of developers and QA can test independently without bottlenecks or interference. Operational simplicity: Maintaining just one high-fidelity-compliant environment (baseline) with third-party integrations and data setup reduces operational overhead significantly.

Maintaining just one high-fidelity-compliant environment (baseline) with third-party integrations and data setup reduces operational overhead significantly. Speed and quality: Teams can quickly test their code against real-world scenarios, uncover edge cases and release capabilities confidently — all while reducing overhead and delays.

By adopting sandboxes, FinTech organizations can enable high-quality, efficient development cycles, ensuring compliance while unlocking innovation at scale. This paradigm shift away from monolithic staging environments toward dynamic, scalable sandboxes gives FinTech companies a critical competitive advantage.

Brex, a leading FinTech business, adopted Signadot’s sandboxing solution to scale developer testing across hundreds of engineers. With sandboxes, Brex significantly reduced environment costs and eliminated the operational challenges of managing multiple staging environments, all while maintaining compliance and real external API integrations. This enabled faster development cycles, improved developer satisfaction and provided a competitive edge in delivering robust attributes efficiently.

Dynamic sandboxes on existing environments offer a scalable way to achieve high-fidelity testing while keeping operations simple. For FinTech teams looking to improve testing quality, streamline workflows and accelerate development, adopting this approach can make a significant difference.

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