When to kill a software project

· Source: LeadDev · Field: Technology & Digital — Software Development & Engineering, Software Project Management · Depth: Intermediate, medium

Summary

NinjaOne's SVP of data and AI, Joel Carusone, details a structured approach to terminating software projects, noting that only 31% succeed and 19% are canceled before production, per the CHAOS Report. Carusone advocates for setting predefined "kill criteria" and timeboxes for new initiatives, often measured in engineering hours or weeks. For example, a vulnerability tracking feature at NinjaOne required 90% confidence in data matching; failure to meet this within the timebox led to a pivot to a hybrid coding and large language model (LLM) strategy. Projects undergo regular reviews for cost, financial viability, and market competitiveness, involving product managers. A "single dissenter kill switch," a senior technical contributor, holds authority to terminate projects, asking if they would be started today with current knowledge. This strategy prevents sunk cost fallacy, maintains morale, and focuses resources, as NinjaOne invests in roughly one in ten explored ideas.

Key takeaway

For Directors of AI/ML or Product Managers overseeing new software initiatives, you should implement clear, upfront "kill criteria" and timeboxes for all projects. Designate a senior technical contributor as a "single dissenter" empowered to terminate projects if they fail to meet predefined goals or market viability checks. This approach prevents the sunk cost fallacy, preserves team morale by stopping efforts early, and ensures your valuable engineering resources are consistently focused on the most promising and strategically aligned endeavors.

Key insights

Establishing upfront kill criteria and empowering a single decision-maker optimizes software project lifecycles.

Principles

Method

Implement predefined timeboxes and kill criteria. Conduct regular reviews tracking cost and market fit. Empower a "single dissenter" to make final termination calls based on current knowledge.

In practice

Topics

Best for: Director of AI/ML, Product Manager, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by LeadDev.