---
id: "framework-building-ai-with-workers"
type: "framework"
source_timestamps: ["¶3", "§ They Reduce Uncertainty", "§ They Train People in the Context of Real Work", "§ They Measure Real-World Performance"]
tags: ["implementation-strategy", "change-management", "workforce-transformation"]
related: ["concept-dynamic-skill-and-task-mapping", "concept-learning-in-the-flow-of-work", "action-track-human-ai-handoffs", "concept-co-learning", "claim-ai-enabled-not-ai-run"]
steps: ["\\\"Reduce Uncertainty: Use dynamic skill and task mapping with direct input from the shop floor to explicitly define how roles will shift", "capturing tacit knowledge and clarifying new accountabilities.\\\"", "\\\"Train in the Context of Real Work: Abandon isolated classroom training in favor of 'learning in the flow of work", "' using real-time analytics to coach workers on the line as they interact with AI tools.\\\"", "\\\"Measure Real-World Performance: Replace participation-based metrics (hours logged) with operational signals that track how humans and AI operate together (e.g.", "speed of handoffs", "exception resolution).\\\""]
sourceUrl: "https://hbr.org/2026/05/the-best-manufacturers-build-ai-with-workers-not-for-them"
sourceTitle: "The Best Manufacturers Build AI with Workers, Not for Them"
sources: ["adoption"]
sourceVaultSlug: "hbr-seg-adoption"
originDay: 9
articleStem: "hbr-cl-78-build-ai-with-workers"
---
# The "Build AI With Workers" Framework

The **Build AI With Workers** framework is a three-pillar approach for manufacturers to close the gap between executive optimism and frontline worker skepticism when deploying AI. Its central paradigm shift: stop deploying technology *for* workers and start co-creating workflows *with* them.

### Pillar 1 — Reduce Uncertainty
Use [[concept-dynamic-skill-and-task-mapping]] with direct input from the shop floor to explicitly define how roles will shift, capture tacit knowledge, and clarify new accountabilities (who owns which decision, when to escalate). This directly counters [[claim-exec-uncertainty-travels-downstream]]. Operationalized by [[action-implement-dynamic-mapping]]; requires [[prereq-psychological-safety-d78]].

### Pillar 2 — Train People in the Context of Real Work
Abandon isolated classroom training in favor of [[concept-learning-in-the-flow-of-work]], using real-time analytics to coach workers on the line as they interact with AI tools. This is where [[concept-co-learning]] happens. Operationalized by [[action-shift-to-in-flow-training]]; requires [[prereq-real-time-data-infrastructure]].

### Pillar 3 — Measure Real-World Performance
Replace participation-based metrics (hours logged, courses completed — see [[claim-traditional-training-metrics-fail]]) with operational signals that track how humans and AI operate together: speed and accuracy of handoffs, time-to-resolve exceptions, and how often operators validate or correct system output. Operationalized by [[action-track-human-ai-handoffs]]; grounded in [[claim-adoption-is-continuous]]; exemplified by [[entity-ford-motor-company]].

### Destination
All three pillars aim workers toward [[concept-software-defined-factory-roles]] and the [[claim-ai-enabled-not-ai-run]] end state. The steps are sequential in emphasis but continuous in practice — each pillar feeds the next in an ongoing co-evolution loop, not a linear rollout.


## Related across articles
- [[action-co-create-ai-tools]]
- [[concept-pull-vs-push-adoption]]
- [[framework-five-approaches-ai-trust]]