Generative AI is no longer a futuristic concept but a tangible reality, reshaping industries and redefining the developer’s toolkit. While the hype surrounding AI has reached a fever pitch, it’s crucial to sift through the noise and identify the genuine value it offers to software developers.

To shed light on the practical applications of generative AI, and provide actionable insights and concrete examples to help developers harness its power effectively, DevPro Journal recently spoke with Casey Kindiger, CEO of Grokstream, and Nitesh Bansal, CEO, R Systems.

Casey Kindiger, CEO of Grokstream:
In 2025 value from GenAI will extend beyond novel assistive use cases with incremental efficiency improvements for engineers and agents to augmentative use cases that model specific NetDevOps functions.

For example, GenAI will move from data synthesis to subject matter expertise in AIOps, driven by the combination of expertise encoding, retrieval-augmented generation (RAG), and LLMs. These special-purpose LLM deployments will model expert decision-making previously reserved for senior NetDevOps practitioners. This shift will mark a turning point within IT organizations, with GenAI playing a specialized function, freeing up time for innovation.

Challenges with using LLMs alone to deliver augmentative use cases in NetDevOps contexts include Hallucinations, Security, Cost, and Complexity. Adding RAG techniques to an LLM deployment balances the need to synthesize targeted responses with contextual understanding to get the best of both worlds.

RAGs can encode knowledge from public data sources, like vendor-specific knowledge bases, and internal data, like runbooks and knowledge articles.  Moreover, RAGs can encode knowledge generated by other machine learning models to understand the underlying structure and function of the network and upstream applications.  In this way, deploying an LLM with a RAG architecture delivers more accurate and useful results.

Nitesh Bansal, CEO, R Systems:
It’s no secret that generative AI has already significantly impacted businesses.

In 2025, generative AI copilots will play a more prominent role in the enterprise workflow for the SDLC (software development life cycle). As these models gain the capability to process large codebases, integrate complex documentation third-party tools, and manage vast amounts of project data, they will act as true copilots across all SDLC roles. The agentic mesh—a network of specialized AI agents—will coordinate and adapt autonomously to project needs, connecting QA, product managers, designers, architects, DevOps, and database admins in a cohesive system that anticipates and meets workflow demands.

With the agentic mesh, enterprises can deploy AI copilots that offer services such as high-fidelity UI testing, dynamic UX design, advanced prompting, and domain-specific customization. This interconnected intelligence will automate routine tasks, allowing engineers to focus on high-value, innovation-driven initiatives. Ultimately, this will enable enterprises to deliver enhanced customer experiences, agile development processes, and a new standard of efficiency and productivity.

AI has been the subject of much hype—and that will continue—but in 2025, more tech leaders will place a higher emphasis on ensuring measurable ROI—especially what can be achieved in the same fiscal year or under 12 months.