---
id: "claim-waiting-is-dangerous"
type: "claim"
source_timestamps: ["¶2", "§ Value-creation opportunities exist now."]
tags: ["strategy", "adoption"]
related: ["contrarian-focus-on-usefulness-not-intelligence", "quote-benchmark-not-perfection"]
confidence: "high"
testable: true
speakers: ["Bharat N. Anand", "Andy Wu"]
source_url: "https://hbr.org/2025/11/the-gen-ai-playbook-for-organizations"
source_title: "The Gen AI Playbook for Organizations"
sources: ["agentic"]
sourceVaultSlug: "hbr-seg-agentic"
originDay: 6
articleStem: "hbr-cl-87-genai-playbook-orgs"
sourceUrl: "https://hbr.org/2025/11/the-gen-ai-playbook-for-organizations"
sourceTitle: "The Gen AI Playbook for Organizations"
---
# Waiting for flawless AI is a strategic mistake

**Claim (confidence: high · testable):** A cautious 'wait and see' approach — motivated by gen AI's current flaws such as hallucinations — is dangerous.

Holding off because the output isn't perfect *misunderstands the opportunity*. The benchmark should not be perfection but **relative efficiency compared with current ways of working** (see [[quote-benchmark-not-perfection]]). Gen AI can already deliver meaningful improvements today. The correct mindset is the [[contrarian-focus-on-usefulness-not-intelligence|contrarian stance: focus on usefulness today, not the trajectory of AI intelligence]] — and to constrain deployment to appropriate tasks via the [[framework-gen-ai-deployment|deployment framework]] (start in the [[concept-no-regrets-zone|No Regrets Zone]]).

**Enrichment / empirical support:** The HBR article explicitly calls the wait-and-see stance 'potentially dangerous.' The breakthrough of gen AI is *access*, not perfect intelligence — non-technical employees can already use it productively without data-science or central-IT support. Randomized controlled trials in knowledge work (customer-support reps, consultants) report **~10–35% productivity and quality gains** even with imperfect outputs, and studies show hallucinations don't negate net benefit when systems are used on bounded, well-structured tasks with human review.

**Assessment:** Strongly supported by the article and aligned with emerging empirical research — waiting for 'flawless' AI likely sacrifices near-term gains, *provided* deployment is scoped to appropriate tasks. Note the important caveat that this holds only when the task's [[concept-cost-of-errors|cost of errors]] is low or a human-in-the-loop is present.