May 19, 2026
Every half-baked feature you launch becomes your customer’s problem.
AI-assisted development has compressed feature release timelines that used to be measured in two-week sprints into days or even hours. That introduces opportunity and also risk. The problem is that what’s accelerating alongside release cycles is the number of under-examined features in production.
The story I'm hearing from technology leaders right now goes something like this: An engineer runs out of queued work, listens to some customer calls on Gong, and releases a batch of requested features without thorough vetting. Meanwhile, a product manager spends a few days in Claude Code producing a prototype instead of working out the next big strategic bet. These uncoordinated efforts creep into the customers’ experience in the form of increasingly crowded feature lists, convoluted workflows, and unsupported edge cases. Over time, the product may still technically work, but it’s no longer a coherent whole, and its value to customers starts to erode.
"We ship at light speed, but we can't stay on top of all the things we need to build and why. How is it possible that our developers are constantly asking for more work, while at the same time things keep falling through the cracks?"
— B2B SaaS founder
This is the new math of AI-accelerated product development. Releasing features is cheap, but that means sloppy work reaches your customers faster than it used to.
Your customers feel it before you do
Here's what bad product decisions cost, and where those costs tend to surface:
| Who feels it | What it feels like | When it's felt |
|---|---|---|
| Customers | Confusing UX, constant changes, unmet expectations | Immediately |
| Engineering | Complexity tax, maintenance debt, brittle integrations | 2–3 sprints later |
| Product Management | Stalled strategy, unhappy stakeholders, incoherent feature set | Next planning cycle |
| Leadership | R&D spend that doesn’t drive expected returns | At board review |
The financial stakes are material. On average, R&D accounts for 20–25% of revenue at software companies. A single wrong bet that burns six weeks across a single engineering team can represent $300K in direct R&D spend before opportunity cost at a $20M ARR company. At the current pace of AI-assisted development, teams aren't making one of those bets per quarter anymore. They're making several per sprint. And each one reaches a customer before it reaches a finance spreadsheet.
What needs to change
The instinct is to slow things down and add more process, more approval gates, more documentation requirements.
I don't think that's the right response.
The teams handling this well aren't moving slower. They're making decision-making more explicit without weighing teams down.
In practice, that means:
- Decision ownership is clear before work starts, not negotiated after something ships
- The rationale travels with the work, so developers, designers, and GTM teams understand what's being built, for whom, and why
- Outcomes tie back to the decisions that drove them, so teams learn from what shipped instead of what was planned
- Context cascades over time instead of constantly being rebuilt, preserving institutional memory as teams and priorities shift
High-velocity product teams don't have those habits yet. Instead, they're improvising in Notion docs, Slack threads, and fragmented conversations that are untraceable six weeks later.
The gap between great product decision-making and the systems teams operate with has existed for a long time. AI just makes the slop visible to customers much sooner.