---
id: "question-translating-productivity"
type: "open-question"
source_timestamps: ["§ How AI Might Take Jobs—and How It Probably Won't"]
tags: ["process-engineering", "best-practices"]
related: ["concept-individual-vs-process-productivity", "claim-translation-difficulty"]
resolutionPath: "Development and publication of standardized frameworks and case studies detailing successful end-to-end business process redesigns centered around generative AI."
sources: ["execution"]
sourceVaultSlug: "hbr-seg-execution"
originDay: 8
articleStem: "hbr-foci-62-layoffs-ai-potential-not-performance"
sourceUrl: "https://hbr.org/2026/01/companies-are-laying-off-workers-because-of-ais-potential-not-its-performance"
sourceTitle: "Companies Are Laying Off Workers Because of AI’s Potential—Not Its Performance"
---
# How Can Organizations Systematically Translate Individual AI Gains into Process Efficiency?

**Open question:** What are the repeatable methodologies for converting an individual-level AI gain (e.g., the 10–15% programming boost) into enterprise-scale process efficiency?

The authors call this translation 'challenging' but do not specify a proven playbook — it remains an open challenge for most organizations. This is the practical frontier of [[concept-individual-vs-process-productivity]] and [[claim-translation-difficulty]], and the reason [[action-redesign-business-processes]] is prescribed but not yet standardized.

**Resolution path:** Development and publication of standardized frameworks and documented case studies of successful end-to-end business-process redesigns centered on generative AI.

**Enrichment (partial answers forming):** BCG's end-to-end workflow-reshaping-plus-A/B-testing recommendation and McKinsey's operating-model framing are the closest available scaffolding, but neither yet constitutes a validated, generalizable methodology.