Your Next Enterprise Software Feature Won’t Be Shipped—It Will Be Generated
September 1, 2026 – 10:56 am | Credit: RAG
TL;DR
Enterprise software is on the cusp of a significant shift where AI assistants generate features on demand rather than shipping them through product backlogs. Instead of submitting a request and waiting months, users will describe their needs, and the system will create a small program to fulfill it. This article explores the implications for product teams, security, governance, and the very definition of a ‘feature’.
The traditional enterprise feature backlog is where well-intentioned user requests go to wait. That wait is about to get significantly shorter—not because product teams are working faster, but because the missing features can now be generated at request time and discarded promptly.
Consider this scenario:
A support team lead opens her helpdesk application and asks:
“Show me every ticket this month that was resolved and then reopened within 48 hours, grouped by the agent who first closed it.”
No existing report, filter, or dashboard tile can fulfill this request.
Years ago, this would have been a ticket for the product team, diligently logged, prioritized, and often delivered late or not at all. More recently, an AI assistant might retrieve a relevant knowledge article or guide her through a spreadsheet analysis.
Now, there’s a third option: The assistant generates a small program tailored to this exact request. This program scans ticket records, cross-references resolutions with subsequent reopenings, checks for gaps under 48 hours, and presents a grouped summary.
The product team doesn’t build the report; the user doesn’t write code. For that specific task, the missing feature appears exactly where it’s needed.
This is not merely better retrieval; it’s the dawn of programmable context. Retrieval selects from existing evidence (documents, passages, rows), while code computes new evidence based on procedures: filtering, joining, calculating, and summarizing across sources.
Many enterprise questions aren’t answered by existing artifacts but require procedures:
- Which tickets were reopened only after being transferred to a different team?
- Which expense reports exceed the department average by more than half?
- Which deployments failed this week that also failed last week—and what changed between deployments?
Product teams can prebuild common procedures, but they can’t anticipate all of them. That’s where programmable context steps in: The model translates user intent into a temporary program executed against approved capabilities, returning only the computed result to the context window. This isn’t a general-purpose code interpreter with open access; the application defines exactly which operations generated code can call.
The code might exist for a single request, generated, executed, logged, and discarded. Users will decide what the AI actually sees, defining their computational needs by describing the desired outcome.