Indeed, a survey completed by software company Freshworks found the proportion of Australians using AI daily at work increased from 18% to 27% during the second half of 2024. This rapid growth rate is showing no sign of slowing.
What makes this AI trend different from previous technological booms, however, is the pivotal role being played by developers. In the past, developers have tended to be the unseen architects who brought the ideas of others to life. Now, powered by GenAI, they are the principal innovators steering its evolution.
Application-driven innovation
While many breakthroughs begin with a grand vision, true innovations are application-driven. Progress arises from the bottom up, fuelled not by a single central idea but by collaboration and the diverse contributions of a community. For example, Linus Torvalds created the Linux kernel in 1991, but it was the worldwide community of developers that expanded it into a massive open-source ecosystem.
Within most modern organisations, it’s developers who are constantly finding new and creative ways of solving business problems, fuelling change, and enabling firms to adapt and thrive in the evolving tech landscape. For instance, Google famously introduced a “20% time” program for developers to work on anything they wanted, sparking new products like Gmail, Google News, and AdSense.
GenAI offers particularly fertile ground for developers, so it is essential to empower them to explore the emerging possibilities on offer. It’s through investment in AI literacy and allowing for safe exploration that organisations can better understand GenAI’s potential and guard against missteps.
The important role of GraphRAG
Developer-led discovery and innovation depend on two ingredients: an opportunity and new technologies or patterns applied differently.
GraphRAG is such an example where technology was used to creatively solve a challenge. This challenge was that GenAI applications were hallucinating, operating as a black box, and had no awareness of what an end user is allowed to see or what is sensitive or private data.
While vector-based RAG offered some assistance in overcoming this problem, it wasn’t sufficient for many use cases. In mid-2023, developers independently conceived the idea of integrating knowledge graphs into GenAI pipelines, leading to GraphRAG.
GraphRAG elevates GenAI by fusing vector similarity searches with knowledge graphs. This approach not only adds authoritative knowledge and context but also yields more accurate, understandable, and transparent outcomes.
The rise of AI-focused engineering
This example demonstrates how the role of the developer has been morphing. Software developers are now becoming AI engineers, integrating AI into modern applications.
As AI becomes essential to modern applications, developers are integrating LLMs and creating innovative architectures – such as GraphRAG and agentic frameworks - to overcome their limitations. Agentic systems embody how developers innovate around core AI models; guiding LLM reasoning, orchestrating multiple roles, and preserving context for more effective outcomes.
The software gives users the ability to pause and review context later, so teams can refine and resume tasks at any time, without losing sight of the broader objectives. This evolution enhances both employee and customer experiences.
Tools like LangChain, LlamaIndex, and AG2 streamline the process, making AI adoption more accessible and modular. While the vast options might seem overwhelming, they actually ease the workload, making AI integration more accessible and transforming application development into a modular, GenAI-assisted process.
The question isn’t how intelligent large language models will become, but rather what developers will do with the evolving toolkit. Ways to encourage AI-driven progress include:
- Allow the freedom to experiment: Even if it’s just an hour of their workday, giving your developer teams licence to experiment makes innovation happen. One example is the free and open-source Knowledge Graph LLM Builder, which brings together a variety of open components that help anyone get into the basics of GraphRAG in minutes.
- Provide empowerment:
Empower developers by aligning resources and strategies with the organisation’s GenAI objectives. While building a GenAI application is a start; ensuring its accuracy, transparency, and explainability is another. CIOs need to architect and scale with these goals in mind. Align with developers on the best tools vital for GenAI adoption.
- Think holistically:
Focus on the entire developer experience and not just their productivity. Developers do more than write code; they design, diagnose, debug, and fix. Unlike automation tools, they make software do what humans need. CIOs can prioritise efficiencies with GenAI and build innovations that impact the top line.
A GenAI-based revolution
Developers are now at the core of GenAI’s business impact, ensuring technology is adopted responsibly, securely, and ethically. With their practical expertise, they help organisations avoid risks while building trust.
It’s important to ensure that this value is understood by senior managers. This will result in developer teams receiving the resources, time, and encouragement they need to extract the most business value from GenAI.
