Humans at the Center: Redefining the Role of Developers in an AI-Powered Future

In a previous blog, we discussed how AI is reshaping software development at every level. This shift means developers need new skills to stay effective. In fact, Gartner predicts that generative AI will require 80% of the engineering workforce to upskill through 2027. 

So what can today’s developers do to stay ahead? Here are a few steps to consider.

Step one: Welcome AI as part of your everyday development process. 

AI is here to help, not replace, but this is only true for developers who keep learning and adapting. Now is the time to be fearless, explore, and learn. Developers who resist AI-enabled tools risk falling behind.

Step two: Build new skills for the AI era.

Even as AI agents take on more tasks, as they are expected to do, development still requires the benefit of human judgment and oversight, including having an engineer who can:

• Guide AI output: This involves understanding context and constraints for a task, mastering natural language prompt engineering, and eventually adopting retrieval-augmented generation (RAG) skills when coding through agents becomes more prominent.

• Review AI-generated code: The AI system that generated your code has many potential “failures.” From flawed training data and hallucinations to poor prompts, AI-generated code often needs careful review. Engineers must deeply and thoroughly analyze code for functionality, quality, and security.

• Design AI-powered applications: Generative code is only part of the picture. Engineers need to understand how to construct AI-driven features, orchestrate agentic systems, and navigate the emerging AI stack. While the industry is actively working to increase simplicity and empower non-data scientists to build AI applications, the more you know how AI works, the better equipped you are to build powerful and secure applications.

Step three: Learn together, not alone. AI is evolving fast, opening up new ways to build and ship software.

However, the volume of influencer observations and announcements from industry vendors is more than any single developer should try to track. To distribute the effort and build collaboration, create routines around team learning and experimentation:

1. Formalize a process for prioritizing and vetting new AI features and tooling.

2. Create a shared space to discuss what works and what doesn’t with AI tools.

3. Codify what is worthy of standardization across the teams. Productivity gains are most significant when paired with mature engineering practices.

4. Start considering an AI-first culture for software development!

Step four: Secure AI workflows from the inside out.

Developer workflows are changing fast. You need developer-first application security that matches that pace. This requires security that’s seamlessly embedded in the new AI environments developers are using daily. Snyk is expanding integrations with AI-powered IDEs to meet developers where they work, starting with our jointly engineered native integration into Google Cloud Gemini Code Assist. (AI partners interested in integrating with Snyk should check out Snyk Studio.) 

Integrating with AI code generators is one element of a broader AI Trust Platform that Snyk recently announced. As part of the new built-in AI capabilities, developers can use Snyk Assist. This AI assistant delivers real-time security intelligence, vulnerability insights, and interactive learning, right in the development workflow. Snyk Agent is also available, going beyond recommendations to execute changes on behalf of the user based on the level of autonomy the developer has granted. These features give teams the guidance and safeguards they need to code confidently in AI workflows. 

Check out our latest eBook Building AI Trust: Securing Code in the Age of Autonomous Development, and start building tomorrow’s innovations today.