Developers must upskill to stay relevant amid rapid AI and automation changes
AI is transforming how software is written and delivered. Entire categories of manual coding work are already getting automated, or at least accelerated, by generative AI tools. Platforms that can write, refactor, or debug code in real-time are pushing developers to change how they operate. In short: if you’re writing software the same way you did two years ago, you’re already behind.
Companies can’t ignore this shift. The broader consequence is a restructuring of the value chain in technology development. Over 100,000 tech workers were laid off in 2025, according to layoffs.fyi. Those numbers reflect a system-wide adjustment. Jobs are evolving, and developers must now think beyond syntax and frameworks. They need to grasp how AI and automation integrate across the software development life cycle.
From a business perspective, the message is simple: teams that don’t adapt quickly will lose relevance even faster. Developers who take the initiative to learn emerging skills, whether that’s using AI-assisted pipelines, working with low-code platforms, or understanding integration layers, position themselves at the front of this change. Employers should encourage that behavior across every engineering team.
If you’re in a leadership position, support ongoing learning and give teams both the time and space to experiment at the edge of this shift. The organizations that transition well will capture more value from automation while maintaining strong in-house talent. Ignore the shift, and you may find your product velocity, and your talent advantage, stall out.
Prioritizing ecosystems that foster collaboration, adaptability, and continuous learning
Choosing the right tool is no longer the game. Picking the right ecosystem is. Developers need to thrive in environments that have to be collaborative, resilient, and fast-moving. You want your team working with platforms that reward experimentation and make iteration frictionless.
Gloria Ramchandani, SVP of Product at Copado, put it well: developers should focus on environments that combine low-code, cloud, and automation tools with support for peer learning and community engagement. Why? Because technology moves fast, but ecosystems scale ideas and skills faster.
The companies building these ecosystems, whether product-led growth companies or platform players, aren’t just offering features. They’re enabling behaviors. They push developers to share, iterate, test, and ship with more velocity.
As an executive, this should inform your investment strategy. Favor vendors that support open APIs, transparent roadmaps, and active developer communities. These aren’t bells and whistles, they’re structure for scaling innovation. The future of development is hybrid: low-code platforms working with pro-code components, AI agents acting as co-developers, iterative feedback loops compressed from weeks to hours.
When you build on ecosystems designed for speed, collaboration, and change, your teams stay sharp. Your costs drop. Your ability to capture market opportunities increases. And your development organization becomes a driver of business adaptability, which is the only enduring advantage in tech.
Junior developers should build depth and breadth using accessible, practical learning resources
If you’re early in your development career, speed of learning matters as much as what you’re learning. The landscape is shifting too fast to rely solely on academic depth or formal training programs. Junior developers make the most progress when they act fast, learning tools that solve real problems, using resources that show what actually works in practice.
Matthew Makai, VP of Developer Relations at Digital Ocean, recommends developers use rich video tutorials to learn tools like low-code platforms, IDEs, and developer services. His point is solid: watching someone solve a problem in real-time shortens your learning curve. It’s not just about understanding how the tool works, it’s about seeing how experienced engineers think while using it.
Developers at this level shouldn’t wait for structure, they should build their own. Learn APIs. Play with data pipelines. Connect systems through integration layers. Test how AI agents perform basic dev tasks. These are areas where companies are hiring and where opportunity will grow.
Facundo Giuliani, Developer Relations Engineer at Storyblok, gives important advice here: focus on durable skills. System thinking, data architecture, reasoning across services, that knowledge travels with you, regardless of which tools stay or fall away. Low-code and AI platforms are changing. But critical thinking, composability, and the ability to design across layers remain relevant.
For business leaders, this is an opportunity. Junior developers who learn fast and think broadly are an investment. Give them room to explore modern tools and learn transferable systems. They become internal innovation engines quickly, and that speed is a long-term asset.
Senior developers must deepen software engineering expertise to future-proof their careers
Senior developers can’t afford to just stay current, they need to lead the shift. As AI takes over repetitive tasks, the work senior engineers do must become more strategic. That means focusing on areas where experience and decision-making still deliver exponential value: abstraction modeling, API orchestration, architecture design, and secure implementation.
Phillip Goericke, CTO at NMI, makes the case clearly. Developers rising into senior roles should pursue platforms and environments that give them control over integration, extensibility, modularity, and prototyping. These are high-leverage areas. Problems here aren’t solved with simple fixes, they demand structured thinking and experience-driven solutions.
Josh Mason, CTO at Recordpoint, adds key reinforcement: skills that scale include machine learning frameworks, data modeling, and secure API design. These capabilities let engineers shape high-value capabilities across product lines. They’re also hard to delegate to less experienced team members, or to AI assistants.
Simon Margolis, Associate CTO of AI and Machine Learning at SADA, draws clear lines between traditional IDE workflows and AI-assisted development pipelines. His view: AI tools are just another interface layer. The core goal remains, use logic to drive results. Whether through Python, English-language prompt engineering, or command-line interfaces, the outcome orientation stays constant.
For decision-makers, the what-to-support part is clear. Encourage infrastructure that promotes abstract design, modular architecture, and automation integration. Reward experimentation with AI-assisted development. Push for engagements that stretch senior technical staff across design, implementation, and mentoring. That’s where their value compounds, and where the next generation of leadership is shaped.
Mastering enterprise SaaS and low-code platforms expands career opportunities
Enterprise SaaS platforms are becoming full development ecosystems. They now support not just business process execution but advanced automation, AI agent orchestration, and full-scale solution development. Developers who embrace these systems boost their capacity to contribute in high-impact enterprise environments, especially in large organizations reliant on these platforms for operations and innovation.
SAP, Salesforce, and Workday have matured into powerful development platforms. SAP’s Business Technology Platform (BTP) includes tools like SAP Build for agent development, SAP Integration Suite for system connectivity, and Business Data Cloud for managing distributed data. Salesforce extends capabilities with AgentForce 360, Mulesoft integrations, and Data 360 for third-party data pipelines. Workday’s developer stack, Extend, Orchestrate, Flowise, and Data Cloud, offers similar depth for automating workflows and enabling zero-copy data access.
These platforms don’t limit developers, they offer operational leverage. Developers who know how to work within enterprise security models, data management rules, and the nuances of multi-system workflows bring measurable value to large businesses. That’s where the job openings are. That’s where scalable solutions ship.
Matthew Grippo, SVP of Core Software at Workday, points out what actually trips up developers: it’s rarely the toolset. What takes deeper insight is understanding the environment, business models, data ownership permissions, access flows, compliance. Developers who can operate at that level don’t just write code. They unlock business capabilities.
SAP’s CTO Dr. Philipp Herzig makes the point directly, AI can write code, but understanding what’s happening under the hood still matters. AI-generated solutions won’t ship at enterprise scale without strong attention to context, quality, and architecture. That’s where experienced developers stand out.
For executives, the strategy is obvious. Equip your teams with skills in these scalable platforms. Encourage them to go deeper into how data flows and processes are managed under enterprise constraints. The results are faster iteration, fewer deployment risks, and a development organization that aligns with core operational needs.
Developers must focus on outcomes and roles beyond mere coding
The definition of a developer is expanding fast. Developers are no longer evaluated just on how much code they write, they’re expected to shape customer experiences, drive product decisions, automate internal workflows, and extract value from data. Solving real problems is the metric that matters.
This shift demands one clear adjustment: developers must align their skills with business outcomes. Coding is still essential, but it’s a tool, not the whole job. Developers working on products today are expected to bring critical thinking, user empathy, fluency with data, and the ability to collaborate with non-technical stakeholders. Business and technology are converging, and developers are increasingly at that intersection.
Christian Birkhold, VP of Product Management at KNIME, laid this out clearly: technical expertise only delivers results when paired with purpose, context, and a tight connection to what the software is meant to achieve. Mastering AI speeds things up, but staying close to the intent, and the code, ensures alignment with what the business actually needs.
For executives, that means adopting a broader view when hiring and developing engineering talent. Prioritize those who are curious, self-directed, and oriented toward product impact. Encourage developers to join planning meetings, contribute to product design early, and stay engaged through delivery. That upstream engagement improves timelines, improves the output, and surfaces smarter technical decisions earlier.
The advantage goes to organizations that treat software engineering as a full-stack role, from requirements through design, execution, and iteration, not just lines of code written. When developers anchor their work to goals that matter to the business, product cycles accelerate, and outcomes become measurable.
Lifelong learning is essential in an AI-driven, rapidly evolving tech landscape
The pace of change in software development isn’t slowing down, it’s accelerating. New frameworks, AI development tools, and automation platforms are entering the market continuously. Developers who treat learning as optional will fall behind. Those who embed learning into how they work will stay ahead.
With rapid advancements in generative AI, coding assistance, integration platforms, and software orchestration tools, today’s solutions won’t solve tomorrow’s problems. That applies to individuals and to companies. Developers who continually refresh their knowledge aren’t just improving their skills, they’re upgrading their ability to think, debug, deliver, and lead in unfamiliar contexts.
This isn’t about checking boxes in online courses. It’s about actively exploring how AI tools shape software delivery, how platforms integrate across business systems, and how to build reusable, composable solutions at scale. Developers need time, space, and executive support to do that, and they need it often.
For leadership, the responsibility is clear. Build a work culture that values progress over perfection. Make room for your engineering teams to test new tools, review emerging APIs, assess new IDEs, and document improvements with intention. The return on that investment is a workforce that gets more productive, faster, smarter, and more aligned with your strategic roadmap.
As your competitors automate workflows and shift effort toward outcome-focused roles, the teams that stay curious, driven, and connected to change will move faster. That speed compounds. And in high-velocity markets, that difference always turns into a competitive advantage.
Recap
Technology keeps accelerating, but the fundamentals remain unchanged, what matters is who adapts, how fast, and with what intent. Developers who invest in continuous learning, engage with collaborative ecosystems, and understand the business context behind the code will outperform the rest.
For executives, the takeaway is clear. Create environments where your technical teams can explore, fail fast, and iterate with purpose. Back platforms that enable scale, speed, and flexibility. Prioritize skills that stretch beyond code, from system design and automation to AI fluency and business literacy.
The organizations that thrive won’t be the ones with the most tools. They’ll be the ones with talent that knows where to build, how to pivot, and when to push boundaries. Enable that, and engineering becomes a force multiplier, not just for delivery, but for innovation at scale.
