---
id: "framework-four-pillars-of-ai-success"
type: "framework"
source_timestamps: ["¶6", "§ 1. Executive sponsorship", "§ 2. A network of partners", "§ 3. Cross-department communication", "§ 4. Data management"]
tags: ["strategy-framework", "enterprise-architecture"]
related: ["claim-c-level-sponsorship-necessity", "concept-ai-center-of-excellence", "claim-partnership-ecosystem-maturation", "prereq-meticulous-data-management", "concept-unstructured-data-utilization"]
steps: ["Executive sponsorship: Secure C-level (CEO/board) backing to protect projects with indirect or delayed ROI and direct resources to high-potential areas.", "\\\"A network of partners: Supplement internal capabilities (used by 90% of leaders) with external consultants", "vendors", "and cross-industry partners to accelerate development.\\\"", "\\\"Cross-department communication: Bridge IT and operations", "typically by establishing a Center of Excellence (CoE) to standardize processes", "ensure compliance", "and manage talent.\\\"", "\\\"Data management: Invest in cloud systems to meticulously record", "organize", "and secure pertinent operational data (e.g.", "machine telemetry) and leverage GenAI for unstructured data.\\\""]
speakers: ["Bruce Lawler", "Vijay D'Silva", "Vivek Arora"]
source_url: "https://hbr.org/2025/01/what-companies-succeeding-with-ai-do-differently"
source_title: "What Companies Succeeding with AI Do Differently"
sources: ["execution"]
sourceVaultSlug: "hbr-seg-execution"
originDay: 8
articleStem: "hbr-cl-89-companies-succeeding-with-ai"
sourceUrl: "https://hbr.org/2025/01/what-companies-succeeding-with-ai-do-differently"
sourceTitle: "What Companies Succeeding with AI Do Differently"
---
# The Four Pillars of Enterprise AI Success

**The Four Pillars of Enterprise AI Success** is the article's central framework, distilled from the MIT ([[entity-mit-d89]]) and McKinsey ([[entity-mckinsey-and-company]]) surveys of 100+ companies (2021 and 2023). Successful AI implementation rests on four foundational elements that separate leaders from laggards: secure top-down protection for experimental projects, leverage mature external ecosystems, break down internal silos, and meticulously manage data infrastructure.

### Pillar 1 — Executive sponsorship
Secure C-level (CEO/board) backing to protect projects with indirect or delayed ROI and direct resources to high-potential areas. More than **75% of leaders** had it. → [[claim-c-level-sponsorship-necessity]], [[action-secure-executive-sponsorship]], case: [[entity-cooper-standard]].

### Pillar 2 — A network of partners
Supplement internal capabilities (**~90% of leaders** build internally) with external **consultants, vendors, and cross-industry partners** to accelerate development. → [[claim-partnership-ecosystem-maturation]], [[concept-cross-industry-ai-analogies]], [[action-shift-partnership-strategy]], [[action-seek-cross-industry-analogies]], case: [[entity-freeport-mcmoran]].

### Pillar 3 — Cross-department communication
Bridge IT and operations, typically via an [[concept-ai-center-of-excellence|AI Center of Excellence]] (used by ~60% of leaders) to standardize processes, ensure compliance, and manage talent. → [[action-establish-coe]], [[prereq-cross-functional-talent]], case: [[entity-target]].

### Pillar 4 — Data management
Invest in cloud systems to record, organize, and secure operational data (machine telemetry), and leverage GenAI for **unstructured** data. → [[prereq-meticulous-data-management]], [[concept-unstructured-data-utilization]], [[action-deploy-genai-unstructured-data]], cases: [[entity-titan-cement]], [[entity-panasonic-energy-north-america]].

**Adjacent framing:** McKinsey's "Rewired" model (>200 transformations) expands these into six dimensions — strategy, talent, operating model, technology, data, and adoption & scaling — adding change-management nuance the four pillars leave implicit.


## Related across articles
- [[framework-shape-index]]
- [[framework-moodys-guiding-principles]]