---
id: "claim-production-cost-spike"
type: "claim"
source_timestamps: ["§ Stage 4: Scale & Operate Portfolio (Navigate)"]
source_url: "https://hbr.org/2026/01/manage-your-ai-investments-like-a-portfolio"
source_title: "Manage Your AI Investments Like a Portfolio"
tags: ["production-ai", "cost-management", "scaling"]
related: ["framework-four-portfolio-stages", "action-track-tco-and-impact"]
confidence: "high"
testable: true
speakers: ["Faisal Hoque", "Erik Nelson", "Tom Davenport", "Paul Scade"]
sources: ["spine"]
sourceVaultSlug: "hbr-seg-spine"
originDay: 1
articleStem: "hbr-foci-61-ai-investments-portfolio"
sourceUrl: "https://hbr.org/2026/01/manage-your-ai-investments-like-a-portfolio"
sourceTitle: "Manage Your AI Investments Like a Portfolio"
---
# Production Deployment Entails Massive Cost and Time Increases

> **Confidence:** high · **Testable:** yes

Moving an AI project from the experimental phase into production deployment marks a fundamental shift in focus and usually entails a considerable increase in both cost and development time. This is because production requires:

- Scaling the system for multiple users.
- Upskilling the employee base.
- Redesigning business processes to leverage the new capabilities.
- Executing complex integrations with the existing technical environment.

This defines Stage 4 (Navigate) of the [[framework-four-portfolio-stages]] and is why [[action-track-tco-and-impact]] (TCO + mission-impact tracking) becomes essential in production.

**External validation:** Deloitte and others find satisfactory ROI on a typical AI use case often takes **2–4 years** — longer than typical tech expectations — precisely because scaling, integration, and change management are nontrivial. HBR/Appian research similarly ties realized AI value to legacy modernization and process integration.