Engineering's Future: Building with AI

When the CEO of Salesforce recently stated that the company would halt engineering hires in 2025 due to AI-driven productivity gains, the tech world took notice. Initial reactions suggested the end was near for engineers, with AI poised to take over. However, this perspective misses the fundamental shift occurring within engineering itself.

Gartner has identified agentic AI as a leading tech trend for the year. The firm projects that 33% of enterprise software applications will incorporate agentic AI by 2028, indicating a progressive integration rather than complete displacement. The real challenge lies in engineers adapting to the evolving nature of their work, as the demand for engineers with AI skills surges throughout the tech sector. Professional services firms are actively seeking engineers experienced in generative AI, while tech companies are introducing new engineering roles focused on AI implementation. The market for professionals skilled in utilizing AI tools is highly competitive.

While claims of AI-driven productivity improvements may have some basis, they often reflect the pressure to demonstrate profitability to investors. Many companies are skilled at presenting themselves as leaders in enterprise AI, aligning with market expectations. The interplay between AI and engineering is progressing along four key dimensions, each enhancing human engineering capabilities rather than substituting them.

AI's Augmentation of Engineering

AI excels at summarizing extensive codebases, documentation, and technical specifications, providing engineers with actionable insights. Instead of spending hours reviewing documentation, engineers can leverage AI-generated summaries to concentrate on implementation. Furthermore, AI's inferencing capabilities enable it to analyze code and system patterns, proactively recommending optimizations. This allows engineers to identify potential errors and make informed decisions more efficiently.

AI has demonstrated a remarkable ability to translate code between different languages. This is proving to be extremely useful as organizations modernize their tech infrastructure while preserving the knowledge contained within older systems. The true potential of generative AI lies in its expansion capabilities, which allow for the creation of new content such as code, documentation, or even system designs. Engineers are utilizing AI to explore a wider range of possibilities, transforming engineering practices across various industries.

AI Across Industries

In healthcare, AI is used to develop personalized medical instruction systems that adapt to a patient’s specific conditions and medical history. In pharmaceutical manufacturing, AI-enhanced systems optimize production schedules to minimize waste and ensure an adequate supply of essential medications. Major banks have been investing in generative AI for quite some time, developing systems that aid in managing complex compliance requirements while enhancing customer service.

New Specializations and Skills

As AI transforms engineering, it is generating new, sought-after specializations and skills, such as the ability to effectively communicate with AI systems. Engineers who are proficient at working with AI can achieve significantly improved results. Similar to the emergence of DevOps as a discipline, large language model operations (LLMOps) focuses on the deployment, monitoring, and optimization of LLMs in production environments. LLMOps practitioners monitor model drift, assess alternative models, and ensure the consistent quality of AI-generated outputs.

Establishing standardized environments for the safe and effective deployment of AI tools is becoming increasingly important. Platform engineering offers templates and guidelines that enable engineers to build AI-enhanced applications more efficiently. This standardization promotes consistency, security, and maintainability across an organization’s AI implementations. Human-AI collaboration ranges from AI providing recommendations that humans can disregard, to fully autonomous systems that operate independently. The most effective engineers understand when and how to apply the appropriate level of AI autonomy based on the context and consequences of the task.

Effective AI governance frameworks establish clear guidelines while fostering innovation. These frameworks address ethical considerations, regulatory compliance, and risk management without stifling creativity. Rather than treating security as an afterthought, successful organizations integrate it into their AI systems from the outset. This includes rigorous testing for vulnerabilities such as hallucinations, prompt injection, and data leakage. By incorporating security considerations into the development process, organizations can accelerate progress without compromising safety.

Engineers who can design agentic AI systems generate significant value. We’re seeing systems where one AI model handles natural language understanding, another performs reasoning, and a third generates appropriate responses, all working together to deliver superior results. Looking ahead, the relationship between engineers and AI systems will likely evolve from a tool-user dynamic to a more symbiotic one. Today’s AI systems are powerful but limited; they lack true understanding and rely heavily on human guidance. Future systems may evolve into true collaborators, proposing novel solutions and identifying potential risks. However, the core role of the engineer – understanding requirements, making ethical judgments, and translating human needs into technological solutions – will remain essential. This partnership holds the potential to address previously unsolvable problems.