SD Times Column by Jason English
I’m always looking for more time and space to get things done. For every useful unit of actual hands-on-keys work time I spend writing thought leadership pieces, or time on briefing and advisory calls with innovative vendors, there must be a corresponding amount of time away from the computer to realign my thinking and grasp the connections between technology categories and their value to end customers.
As humans, we never expected to be as fully engaged as we are now, with per-worker productivity at an all time high, a pocket supercomputer that constantly nags us for attention wherever we go, and some AI coming in to scoop up our ‘think time’ by repeating our collective thoughts back at us.
But I wouldn’t let that short-change my non-AI-generated thoughts on the impact of AI on software development, as I’m constantly analyzing this topic.
AI is driving the tool chain
While we find vendor claims of “AI-driven” software development everywhere we look, there are many different flavors of AI already in common use.
AIOps is really the grandparent of them all, a “ghost in the machine” sifting through millions of logs for security and observability anomalies, or auto-adjusting infrastructure profiles to optimize performance without requiring manual intervention.
Code co-pilots. Lookahead code recommendations have been with us inside our IDEs for years, but with AI input, the suggested or auto-generated code is hopefully becoming more context-sensitive to application requirements.
Testing automation and simulation have also been going on for more than a decade, and AI can help deal with massive scenario volumes, allowing SDETs and QA teams to focus on the most intractable problem areas.
Business process extension. Growing out of low-code and RPA development spaces, these specially tuned AI models handle workflows such as security threat hunting, code modernization, or for industry-specific inferences for workflows like fraud detection, document processing or property insurance claims analysis.
Documentation and code explanation are huge tasks that most shops fall short on, and perhaps the most natural place for LLMs to add value and make good technical writers way more productive.
Data. Walking the floor at AWS re:Invent you couldn’t help but notice how many vendors were now “the ultimate home for AI data” with AI query bolt-ons and data managers, in addition to AWS’s own RAG and ML offerings to maintain parity with other hyperscaler services from Azure and GCS. Buyers will need to look…
Read the whole SD Times Analyst Corner column here: https://sdtimes.com/ai/taking-think-time-about-the-future-of-ai-for-development/
