Artificial intelligence (AI) has the potential to significantly boost software developer productivity, freeing up their time to focus on high-value creative work. Large language models (LLMs) and coding-centric chatbots can streamline the initial coding process and translate natural language descriptions into code, saving production time and effort.

Artem Kroupenev, vice president of strategy at Augury, said AI is “fundamentally transforming” development by moving beyond “simple” code generation to fostering a collaborative relationship between human developers and intelligent systems.

“AI automates repetitive tasks, streamlines code reviews, and optimizes testing, shortening development cycles,” he said. “But the future is even more radical.”

Soon AI may enable entirely new programming paradigms, where developers serve as curators, guiding AI as it generates, iterates, and even designs solutions from scratch. “This shifts the focus from code syntax to solving real-world problems more intuitively, freeing up developers to focus on creativity and system design,” Kroupenev said.

How AI Can Help

AI currently helps developers by automating routine coding tasks, suggesting code improvements, and speeding up debugging through predictive analysis. Tools like GitHub Copilot already offer real-time assistance, generating code snippets, catching bugs, and suggesting optimizations.

Scott Bonneau, executive vice president of product and operations at Karat, likened the evolution of AI tools like GitHub Copilot to a “quantum leap forward” from traditional integrated development environments (IDEs).

They will help enhance productivity beyond what was possible with earlier syntax highlighting and autocomplete features. Software developers need to understand that while AI tools have made significant strides, they are not poised to replace software engineers. “This space is evolving extremely rapidly,” Bonneau said, noting that today’s limitations may soon be overcome.

However, he emphasized LLMs are best suited for generating boilerplate code or handling straightforward tasks—and they struggle with the complex, context-rich work that defines real-world software development. Software engineering involves more than just translating problem statements into code.

“It requires understanding large codebases, integrating various concerns like security, performance, and usability, and balancing those factors within specific industries, such as financial services,” he said. “LLMs are not yet capable of consuming all that context.”

Essential Understanding

Kevin Kelly, AWS's director of the AWS Cloud Institute, said much of developer time today is spent on undifferentiated or repetitive activities, tasks which slow down creativity and innovation. 

He said developers should work backward from their customers and ask themselves what value AI could provide customers internally and externally: “Then, prioritize leveraging AI in those areas that will provide the biggest return on the investment of learning and implementing a new AI solution.”

Kelly explained applying AI skills constructively is best done when you understand the needs of your customer in the domain or industry you build in: “You can then apply AI solutions to improve productivity, enhance the customer experience in a way that matters to that customer, or improve a process that yields value in a timely manner.”

If you are working within an organization, it’s crucial to start by checking your company’s policies and governance surrounding the use of GenAI. “Begin by following your organization’s guidance on best practices for AI adoption,” said Rob Whiteley, CEO at Coder. “This ensures that you’re aligned with company rules while exploring the technology.”

For individuals looking to dive into AI on their own, Whiteley’s advice is clear: “Jump in and start using it once you’re comfortable with the key concepts.”

It’s essential to understand the basics of generative AI, including how transformers and neural networks operate. Gaining a grasp of how LLMs are trained and the distinctions between different models is also important, as those differences determine how they are used in practice.”

Hands-on experience is likewise key; if you’re totally new to generative AI, start experimenting with tools like ChatGPT or Bard/Gemini to learn how to craft prompts that generate accurate responses

This approach helps users build foundational skills while becoming comfortable with AI in development environments. “As you build proficiency, look at how companies are integrating generative AI into their core operations,” Whiteley said. “Understanding these use cases will allow you to apply your AI knowledge more effectively in your daily work.”

AI Training: Where to Go

Kroupenev said developers can gain AI skills through platforms like Coursera, edX, and industry certifications from AWS, Google Cloud, and Microsoft. Interactive AI-based learning environments (like those seen with tools like Cursor) are also an exciting development.

“These platforms allow for more personalized, hands-on learning that evolves as you progress,” he said.

Coding platforms like GitHub Copilot and Cursor may eventually evolve into adaptive AI tutors, capable of providing instant feedback and even mentoring developers through complex AI integration projects.

AI-Centric Certifications

As a tech professional, you know that certifications can help you stand out when applying for jobs. But which certifications can prove you have sufficient AI chops for a gig? Consider:

• The AWS Certified Machine Learning Engineer – Associate certification is designed for engineers with at least one year of machine learning experience, and can help demonstrate their expertise in scaling, deploying, and maintaining AI models.
   
• AI Programming with Python Nanodegree: An Udacity certification program that teaches the fundamentals of AI, including working with Python libraries such as NumPy, pandas, and Matplotlib, and implementing neural networks.
   
• Deep Learning Specialization:For developers who want a deeper dive into AI, this specialization course from Andrew Ng at Coursera covers neural networks, deep learning, and their applications in fields like computer vision and natural language processing (NLP).

AI Plan of Action

Kroupenev said the best way to apply AI skills constructively is to start with a roadmap that begins with foundational AI concepts and grows into more complex, real-world applications: “Identify immediate opportunities within your current projects to integrate AI tools, such as automating testing or bug fixing.”

He recommended experimenting in sandbox environments and collaborating with cross-functional teams such as data scientists to understand the broader AI landscape. As AI advances, developers should shift their mindset from “using AI tools” to “co-creating with AI,” allowing AI to reimagine processes in ways previously unthinkable.

Victoria Myers, global head of talent attraction at Amdocs, added that developers can start by identifying areas in their workflow where AI can make the most impact. From there, setting clear, measurable learning goals is key—for instance, mastering how to effectively prompt GenAI tools, gaining access to new programming languages, or translating complex technical problems into natural language.

“By tracking these goals, developers can evaluate whether AI is delivering real efficiencies and adjust their approach if needed,” she explained.

Securing Executive Buy-In for Upskilling

Myers suggested that, when approaching leadership about upskilling or seeking financial support for AI training, developers should emphasize the business value that AI skills bring, highlighting increased productivity, innovation, and the ability to stay competitive.

Presenting data that shows how AI can improve workflows—such as automating a certain percentage of daily tasks—can help build a strong case.

“It’s also helpful to align this request with the company's broader AI-driven initiatives and future talent strategies,” she said.

Considering the demand for AI-skilled talent, developers should consider pursuing certifications to prove they have the knowledge and experience that employers are looking for. Kelly recommended they also ask around internally for any available AI upskilling programs.

“It’s critical that developers have the knowledge to make informed decisions about building or managing AI solutions, especially as a growing number of employers anticipate using AI-related solutions in their organizations,” Kelly said.