What Executives Get Wrong About AI
🔗 **Source:** [What Executives Get Wrong About AI](https://hbr.org/2025/11/what-executives-get-wrong-about-ai)
> [!note]+ Document Notes
> tags: [AI, Strategy, Business-models]
> author: [John Winsor, Sangeet Paul Choudary]
> source: [Harvard Business Review]
> reference: [https://hbr.org/2025/11/what-executives-get-wrong-about-ai]
> date created: 2025-11-10 00:00
>
> # 🏷️ Title: What Executives Get Wrong About AI
>
<!-- deep-read:sections start -->
## 🧭 Table of Contents
- [📝 Summary](#-summary)
- [📖 Main Content](#-main-content)
- [🗒️ Notes](#️-notes)
- [📚 References](#-references)
- [🗣️ Paraphrase](#️-paraphrase)
- [📰 Newsletter](#-newsletter)
- [✅ Action Items](#-action-items)
- [🧪 Hypotheses](#-hypotheses)
- [📊 Methodology](#-methodology)
- [❓ Questions](#-questions)
- [🔮 Future Research](#-future-research)
- [💡 Implications](#-implications)
- [🧠 Learning Styles Assessment](#-learning-styles-assessment)
- [📊 Accessibility Assessment](#-accessibility-assessment)
- [🌍 DEIB Assessment](#-deib-assessment)
- [✅ Logical Validity](#-logical-validity)
- [🛡️ DARVO Analysis](#-darvo-analysis)
- [🎯 Conclusion](#-conclusion)
### 🔗 References
- Include links cited in or relevant to the article.
## 📝 Summary
- What happened.
- The authors argue most firms treat AI as an add-on, not a catalyst to reinvent value creation and operating models.
- Why it matters.
- “With AI…reinvention, not adoption, should be your goal.”
- Key stakeholders.
- Executives, product/org leaders, ecosystem partners, creators, enterprise buyers.
- Risks and tradeoffs.
- Adoption without reframing erodes margins and power; reinvention requires capability redeployment and complement investments.
- Bottom line.
- Reframe objectives, redeploy distinctive capabilities, and build complements to shift power and capture durable advantage.
## 📖 Main Content
- Core arguments
- Adoption vs reinvention: “They’re buying the shiny new thing and bolting it on to yesterday’s business model.”
- Technology shifts reallocate power across value chains; winners redesign architecture, not just tools.
- Historical contrasts
- Kodak adopted digital but kept a substitution mindset; failed to build complements (sharing, storage, editing).
- Fujifilm redeployed chemistry/imaging capabilities into healthcare, cosmetics, electronics; reduced legacy dependence.
- Kmart used barcodes for checkout speed; Walmart rebuilt information/logistics architecture and dictated supplier standards.
- Contemporary example
- Stock photo incumbents assume scarcity; genAI collapses scarcity and trust/rights.
- Shutterstock reframed to governance (trust, rights, indemnity), partnered on data licensing, and created contributor compensation.
- Key terms
- Complements: ecosystem assets (platforms, partnerships, compensation/indemnity) that unlock value from the core tech.
- Reallocation of power: who sets rules, standards, and captures margins post-shift.
- Uncertainties
- Long-term sustainability of compensation/indemnity models; regulatory evolution around training data and copyright.
### 🗒️ Notes
- “Ninety-five percent of AI projects fail…if you scope your project using the wrong objectives…you might…still set yourself up to lose.”
- Kodak: digital killed scarcity; margins migrated from capture to organize/share/store; Ofoto reframed as printing—missed platform play.
- Fujifilm: redeployed capabilities to skincare, diagnostics, LCD/pharma; proactively shrank film reliance; profitable by 2012.
- Stock photos: genAI undermines scarcity; real issue is trust, authorship, rights.
- Shutterstock: reframed as governance engine; data-licensing with OpenAI/NVIDIA; contributor pay + customer indemnity.
- Barcodes: Kmart local optimization vs Walmart system-level rewiring (EDI, satellite, centralized logistics) → supplier standard-setting.
- Prescription: Reframe, redeploy distinctive capabilities, build complements; aim to shift power, not just efficiency.
## 📚 References
- What Executives Get Wrong About AI (HBR) (https://hbr.org/2025/11/what-executives-get-wrong-about-ai)
- Shutterstock data partnerships announcement (OpenAI) (https://openai.com/blog/shutterstock-partnership)
- NVIDIA generative AI data partnerships (https://www.nvidia.com/en-us/industries/media-entertainment/)
- Walmart retail barcode/EDI history overview (https://corporate.walmart.com/newsroom)
## 🗣️ Paraphrase
- Paragraph A paraphrase.
- Most firms chase AI adoption metrics; without reframing objectives, they optimize the old model and erode their future position.
- Paragraph B paraphrase.
- Advantage comes from reorganizing work, reallocating decisions, redeploying capabilities, and assembling complements that shift power.
## 📰 Newsletter
- This week: Why AI ROI stalls. HBR’s Winsor and Choudary show wins come from reinvention—not pilots. Learn from Kodak vs Fujifilm, Kmart vs Walmart, and how Shutterstock became a governance layer for synthetic media. Resources: HBR analysis, OpenAI–Shutterstock licensing, NVIDIA media stack. Tip: Map your complements (trust, rights, indemnity) before shipping “AI features.”
## ✅ Action Items
#### DevRel
- Launch examples showing “complements” (governance, indemnity) not just models.
- Build quickstarts for data licensing, audit trails, provenance.
- Measure developer adoption of governance SDKs, not just inference calls.
#### OSPO
- Define policy for training-data licensing and contributor compensation.
- Set review cadence for AI bill of materials and provenance signing.
- Establish embargo/incident playbooks for model misuse.
#### Community
- Run roadmap clinics on “reframing outcomes.”
- Pair contributors on trust/rights modules; publish impact reports.
- Convene cross-project drills on coordinated disclosures.
#### WordPress
- Core AI: evaluation sets, red-team pilots, provenance hooks.
- Docs: examples-first patterns for AI complements (logging, indemnity).
- Plugins/Themes: provenance metadata, usage disclosures, opt-in telemetry.
- Accessibility: add TTS summaries; caption quality checks for media AI.
#### The WPOCC
- Fund governance artifacts (charters, operator agreements).
- Sponsor contributor stipends for AI provenance and rights tooling.
### FAIR
- Enforce signature checks and local metadata stores.
- Publish funding transparency and sunset clauses for AI pilots.
## 🧪 Hypotheses
- Firms that invest in complements (provenance, indemnity, compensation) capture higher post-AI margins than feature-only adopters.
- Redeploying distinctive capabilities predicts new profit pools within 24 months.
- AI programs with “power shift” KPIs outperform those with efficiency-only KPIs.
- Data-licensing partnerships reduce legal incidents and increase enterprise adoption.
### 📊 Methodology
- Comparative case studies (matched pairs) on margin shifts post-AI.
- Event studies on partnership announcements vs enterprise uptake.
- Governance-maturity scoring vs revenue mix changes.
- Legal incident rate analysis pre/post indemnity launch.
### ❓ Questions
- Which complements most reliably create switching costs in AI-heavy markets?
- How should firms measure “power reallocation” leading indicators?
- What compensation models sustain creator ecosystems long term?
### 🔮 Future Research
- Standardized AI provenance schemas and cross-platform verification.
- Regulatory impacts on training data markets.
- Organizational designs that accelerate capability redeployment.
### 💡 Implications
- Practice: Budget for complements before model rollouts.
- Policy: Encourage transparent licensing and provenance.
- Security: Treat AI governance as core control plane.
- Education: Train leaders on architectural, not tool, thinking.
- Funding: Tie investment tranches to governance and partner milestones.
### 🧠 Learning Styles Assessment
- Reading level estimate: Flesch-Kincaid ≈ Grade 10–11.
#### 👀 Visual learners
- Present: Clear contrasts (Kodak/Fujifilm, Kmart/Walmart).
- Improve: Architecture and value-chain power maps.
#### 🔊 Aural learners
- Present: N/A.
- Improve: 3-minute audio abstract.
#### 🗣️ Verbal learners
- Present: Definitions (complements, power shift).
- Improve: Glossary and analogies.
#### 🧩 Kinesthetic learners
- Present: N/A.
- Improve: Step-by-step reframing workshop.
#### 🧠 Logical learners
- Present: Causal chains (tech → architecture → power).
- Improve: Decision trees and KPIs.
#### 🧑🤝🧑 Social learners
- Present: Case discussions.
- Improve: Cohort peer reviews.
#### 🧘♀️ Solitary learners
- Present: Clear takeaways.
- Improve: Reflection prompts and self-check.
#### 🌿 Naturalistic learners
- Present: N/A.
- Improve: Ecosystem analogies for complements.
### 📊 Accessibility Assessment
#### Screen reader semantics
- Present: Headings.
- Improve: Alt text for figures; language attributes.
#### Keyboard navigation
- Present: N/A.
- Improve: Ensure focus states, skip links.
#### Contrast and readability
- Present: Likely adequate.
- Improve: Check 4.5:1 minimum.
#### Text resizing and reflow
- Present: Responsive web layout.
- Improve: Support reduced motion; prevent clipping.
#### Media and captions
- Present: Likely images only.
- Improve: Captions and downloadable transcript if any video.
#### Forms and interactions
- Present: N/A.
- Improve: Accessible toasts and error states.
#### Standards reference
- Map to WCAG 2.2 AA: 1.4.x, 2.4.x, 3.3.x, 2.2.2.
### 🌍 DEIB Assessment
#### Cultural breadth
- Present: Global brands; mostly Western cases.
- Improve: Add non-U.S. case studies and translations.
#### Inclusive language
- Present: Neutral, business-focused.
- Improve: Plain-language summaries.
#### Neurodiversity support
- Present: Structured sections.
- Improve: Consistent icon+text labels, chunking.
#### Representation
- Present: Corporate examples only.
- Improve: Include SMEs, creators, diverse regions.
#### Psychological safety
- Present: Respectful framing.
- Improve: Community guidelines for debate.
### ✅ Logical Validity
#### Strong reasoning
- Inductive case comparisons (Kodak vs Fujifilm; Kmart vs Walmart) support the reinvention thesis by contrasting outcomes tied to complements and architecture.
- Causal chain: tech → complements/architecture → power/margins is explicit and plausible.
#### Fallacies
- Potential survivorship bias—exemplars are winners/losers; suggest broader datasets to generalize.
#### Tone of voice
- Analytical and prescriptive: “Reframe, redeploy, assemble the complements.”
### 🛡️ DARVO Analysis
- Deny: None.
- Attack: None.
- Reverse victim/offender: None. The piece critiques strategies, not actors.
## 🎯 Conclusion
- Bottom line: Reinvention beats adoption. Use AI to redesign how value is created, who holds power, and which complements unlock durable advantage.
- Next steps: Define reframing objectives, inventory distinctive capabilities, map complements (provenance, rights, indemnity, partnerships), set “power shift” KPIs, and run pilots that test ecosystem governance—not just efficiency.
## 🧱 Refactor Appendix (append after 🎯 Conclusion)
### 📚 Literature Split Pad
- Purpose: Stage Konik CEE units that become per-source Literature notes.
- Split level: H3.
- Include verbatim quotes under 25 words and a [view highlight] link.
### [H1] Reinvention, not adoption, creates AI advantage
> [!quote] [What Executives Get Wrong About AI](https://hbr.org/2025/11/what-executives-get-wrong-about-ai) by [[John Winsor, Sangeet Paul Choudary]] via [[Harvard Business Review]]. Published 2025-11-10. Accessed 2025-11-10.
> “With AI…reinvention, not adoption, should be your goal.”
Two lines: Use for executive briefings; align KPIs to architecture shifts.
[view highlight](https://hbr.org/2025/11/what-executives-get-wrong-about-ai#H1) #xref #articleseed ID:rw_[MISSING]
### [H2] Complements determine post-AI profit pools
> [!quote] [What Executives Get Wrong About AI](https://hbr.org/2025/11/what-executives-get-wrong-about-ai)
> “Build the complements that create durable advantage.”
Two lines: Roadmap complements first (licensing, indemnity, provenance).
[view highlight](https://hbr.org/2025/11/what-executives-get-wrong-about-ai#H2) #xref ID:rw_[MISSING]
---
### 🔖 Atomic Split Pad (Zettel Candidates)
#### [Z1] AI winners shift value-chain power
- Scope: Markets with complement-dependent trust/rights.
- Pointer: Walmart, Shutterstock cases.
- Links: [[Platform-Governance]] [[AI-Complements]]
#### [Z2] Substitution mindsets erode legacy margins
- Scope: Tech that collapses scarcity.
- Pointer: Kodak digital pivot.
- Links: [[Business-Model-Innovation]] [[Scarcity-to-Abundance]]
---
### ✏️ Fleeting Queue (Extract by Selection)
- [F1] Draft “power shift” KPI examples for AI programs — context/https://hbr.org/2025/11/what-executives-get-wrong-about-ai #fleeting #inbox
- [F2] Map complements for current AI initiative (provenance, indemnity, compensation) — context/https://hbr.org/2025/11/what-executives-get-wrong-about-ai #fleeting #inbox
- [F3] Identify capability redeployments from core strengths — context/https://hbr.org/2025/11/what-executives-get-wrong-about-ai #fleeting #inbox
---
<!-- deep-read:sections end -->