When I asked ChatGPT to generate C++ code for an embedded application, it produced functional code almost instantly. It was exciting. The code worked, and I was genuinely impressed.

But then it fell flat on its face. I noticed an unnecessary divide-by-two operation, and when I questioned ChatGPT, it admitted the mistake and provided a correction. I then inquired if there were any more bugs in the code. It acknowledged there were and offered more corrections. I was taken aback. If ChatGPT could recognize the errors, why did it include them in the first place?

Artificial intelligence (AI) is seemingly everywhere, and the workplace is no exception. For machine designers and programmers, the AI revolution is both exciting and worrisome. AI can write code with very good syntax and formatting.

But, as my experience demonstrates, there are significant pitfalls to look out for. In the off-highway industry, software is quickly becoming the differentiating factor in machine design. What’s more, the number of lines of code in heavy equipment software is set to skyrocket over the next decade—as much as 30 times, according to some estimates.

At such a pivotal time for the industry, programmers need to embrace AI’s capabilities while understanding its limitations, and we need to ensure this is understood throughout our broader organizations.

Recognizing AI’s Potential

The first step to embracing AI is believing in its potential. AI-generated code is transforming machine control in the off-highway sector. It is being used to power autonomous tractors in precision agriculture, optimize hydraulic functions for excavators and bulldozers, monitor real-time sensor data in mining trucks to detect early hydraulic failures and dynamically adjust gear shifts in heavy-duty loaders and graders based on terrain and load, among many other applications.

There are considerable advantages to be realized by leveraging AI to streamline activities, automate repetitive tasks and save time. Programmers can use AI to write code, enabling them to spend time brainstorming creative solutions and refining code.

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As with any technology, widespread adoption follows proven results and quantifiable benefits. Danfoss is no exception. We started small, implementing AI where it could provide immediate value. Our first project was an internal chatbot designed to improve productivity while maintaining control of our intellectual property. It has proven invaluable, helping engineers quickly resolve technical issues in critical moments.

In one instance, it provided the necessary CANopen message sequence to activate signals for third-party sensors just before a client demonstration. In another case, it modified a Python script for prototype position sensors, saving hours of troubleshooting for someone with limited Python experience.

Testing the Waters

The best way to understand AI and see its potential is to dive in. If you’re completely new to AI, start by watching online tutorials and talking with others about their experiences. When you feel ready, experiment with ChatGPT or Copilot.

Begin with simple prompts, like asking ChatGPT to summarize a report or provide recommendations for a road trip. Build up to asking it to complete tasks such as testing recycled code or generating an essential test environment (e.g., “Create code for a canned simulator”).

As you become more comfortable, try AI for more complex programming tasks. The many uses of AI for programmers include:

• Automating repetitive tasks. AI can generate boilerplate code, refactor existing code and create test cases, freeing developers for strategic work.
• Debugging and error detection. AI can identify issues, suggest fixes and streamline debugging.
• Code generation. Ask AI to write functions or code blocks based on brief prompts.
• Multi-language support. AI can translate and support various programming languages.
• Contextual learning. Ask AI to analyze project files to provide relevant suggestions and maintain code consistency.
• Accelerated research. AI can answer technical questions, explain frameworks and provide references.

At Danfoss, we encourage programmers to use AI as a tool to enhance their skills while recognizing its limitations. Our engineers use AI to brainstorm, explore alternative coding approaches and speed up development. Tasks that once took hours can now be initiated with a few concise prompts.

Navigating AI’s Downsides

Despite the many advantages of AI, it is perhaps the cautionary tales like mine that leave programmers hesitant. Concerns surrounding trust, control and risk are valid.

Programmers must be aware of AI’s limitations and stay vigilant in reviewing, testing and refining AI-generated code. Areas to be mindful of include:

• Imperfect code generation. AI can introduce bugs or inefficiencies.
• Limited contextual understanding. AI lacks deep comprehension of project logic, leading to misaligned outputs.
• Challenges with complex problems. AI struggles with nuanced, domain-specific issues that require human expertise.
• Prompt dependency. Vague or incomplete prompts can yield flawed results.
• Adaptability issues. AI tools lack flexibility in evolving project goals.

Ultimately, AI does not make value judgments or assume responsibility for its outputs. AI can only take you so far; it needs coaching, and programmers must know enough to catch errors. You still need experience and knowledge to validate the code. Today, the power of AI is not as a replacement for a programmer, but as a collaborator. It is a great tool that requires human oversight.

Resources are available to help navigate the potential pitfalls of AI-generated code while still leveraging its benefits. Software platforms such as PLUS+1, for example, are designed to be simple, giving developers the ease of programming that AI offers, but with the added security that the code has been tested and validated by software engineers.

Moving Forward with AI

AI is no longer an optional tool; it cannot be considered a fad or an “optional” technology. To remain competitive, organizations need to embrace the opportunities and address the challenges that come with AI.

When all is said and done, success with AI in programming comes down to balance: leveraging its speed and efficiency while applying human intuition, critical thinking and oversight. Programmers and machine designers who adapt and learn to work alongside AI will not only improve their workflows but also contribute to more innovative and robust solutions.

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AI will continue to evolve, offering new capabilities that further enhance development processes. However, its effectiveness will always depend on how well it is integrated into human-led decision-making. The future of AI in programming isn’t about replacement; it’s about augmentation, allowing users to push boundaries, solve problems faster and drive technological advancements with confidence.

By staying informed, experimenting with AI responsibly and understanding both its strengths and limitations, organizations and developers can fully harness AI’s potential to build smarter, more efficient, and more reliable software for advanced machinery.