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 closely at what active use cases development shops are employing.

From bots to agents. The hype of generative AI was matched by the equivalently hyped rise of agentic AI (basically, a fancy new term for AI bots with some degree of autonomy). Allowing developers to use generative and agentic AI services may augment productivity, but governing these agents as they proliferate through the organization will become a new problem.

Conversational Analytics can rapidly abstract variations of SQL and other query types with natural language queries and responses, and generate beautifully written reports and visualizations.

GenAI is not going to take the place of good developers

For the last time, the English language is useful for talking to humans, and an interesting way to conversationally interface with systems, but it is a terribly idiosyncratic way to provide computer instructions.

Pay no attention to the jackass on X bragging about how an LLM built him a fully functional spaceship  game in 2 minutes, it probably just scraped a flight simulator from someone else’s project repo and swapped in a starfield for the sky, with no concern for attribution or intellectual property rights. Further, this approach would never work for a mission critical business application.

As a skilled developer or tester, you will still have to know what you are looking for within any code you get back from an AI. Placing too much trust in AI can cause a virtual ‘brain drain’ for the organization, when problems arise within the application estate.

The Intellyx Take

If a company tried to incentivize development productivity or measure value by “lines of code written” they would quickly find themselves with an exponentially larger volume of highly recursive code. So saying AI would churn out lines of code faster or more efficiently doesn’t add value, any more than developers that type faster would add value.

Despite the GenAI and Agentic hype and funding, a number of high-profile failures of rogue AI usage will cause enterprise customers and employers to rethink their whole strategy around incorporating AI dev tools into their businesses.

Successful AI adoption will come down to governance. Wise companies will put forth a clear AI mission statement, documented policies, and automated usage detection and change management and control tooling (sort of like a UEBA for AI), under the authority of an interdepartmental AI governance board (or similar).

That’s all the thoughts I had time to think for this little column!

Copyright ©2025 Intellyx B.V.  Intellyx is an industry analysis and advisory firm focused on enterprise digital transformation. Covering every angle of enterprise IT from mainframes to artificial intelligence, our broad focus across technologies allows business executives and IT professionals to connect the dots among disruptive trends. As of the time of writing, none of the organizations mentioned in this article is an Intellyx customer. No AI chatbot was used to write this article. Image source: Adobe Image Express