AI Competency | Essay 2
Context
In essay 11, I explored the foundational concept of business competency, drawing on C.K. Prahalad and Gary Hamel’s groundbreaking 1990 Harvard Business Review paper2. Their metaphor of the corporation as a tree serves as a vital framework for understanding how organizations maintain long-term stability. The roots, which represents core competencies, provides the essential sustenance that allows the trunk (core products) and the leaves (end products) to flourish. The defining challenge for business leaders in 2026 is avoiding the trap of surface-level AI. If a company simply bolsters its leaves, such as slapping a generative AI chatbot onto its consumer facing application, competitors will copy it within weeks, resulting in zero sustainable market differentiation. To build a true competitive moat in the AI economy (where value creation, capture, and distribution is powered by AI), organizations must also cultivate AI at the roots.
In Essay 2, my goal is twofold: first, to make identifying a firm’s core competency as intuitive as identifying its end products; and second, to use business core competency as a guide to a firm’s AI implementation strategy.
The empirical basis for this essay is my analysis spanning organizations with annual revenue from $80M to $300B+, covering diverse sectors from Energy and Healthcare to Financial Services and Logistics. I began with an initial pool of over 200 companies, distilling them into a final cohort of 90+ organizations across 28 industries.
From this research, three strategic imperatives emerged. These provide a recommended sequencing for an AI implementation strategy that generates a true, compounding competitive advantage.
Approach
Inclusion Logic: I included diversified software leaders like Adobe and Salesforce alongside non-tech enterprises to compare how varying business models anchor AI in their roots and leaves to remain competitive. To focus on how established businesses are evolving into the AI economy, I excluded AI-native startups (e.g., Cursor) and foundational AI infrastructure providers (e.g., Google, Nvidia, OpenAI).
Data Sourcing: I utilized Gemini Deep Research to aggregate and verify data from public sources, including annual earnings reports, investor presentations, and verified press releases.
Limitations: This study skews toward mid-to-large-cap firms with transparent public disclosures. Privately held firms or those without documented AI initiatives are not represented.
The remainder of this essay is broken into two parts:
I. Identifying Business Core Competency
II. Business Core Competency as a Guide to AI Implementation Strategy
I. Identifying Business Core Competency
Core competency is often invisible to the naked eye, yet it dictates consumer behavior. It is the underlying mechanism that explains why you are willing to pay a premium for a product you could get cheaper from a competitor. Core competency is the quiet advantage that registers in a consumer’s mind, creating a high switching cost even when alternatives exist. Consider these three examples:
FedEx (Routing Velocity): When a package must arrive overnight, you inherently trust FedEx. This is not because they have the best cardboard boxes (the end product), but because their core competency is unparalleled routing velocity. You are paying for the invisible, global logistical choreography that guarantees on-time delivery.
Starbucks (Habitual Loyalty): In an unfamiliar city, you choose Starbucks for a reliable place to meet or work. They don’t just sell customized beverages; their core competency is habitual loyalty. They have engineered a predictable third-place experience that makes a store in Tokyo feel as safe and familiar as one in New York.
Visa (Systemic Trust): When you swipe a card in a foreign country, you expect the transaction to clear in milliseconds without your identity being compromised. You aren’t paying for the plastic; you are paying for systemic trust. Visa’s competency is the invisible, high-speed risk engine that validates billions of transactions while simultaneously hunting for fraud.
To further build on these examples, here are the core competencies that emerged from my analysis. While firms may possess multiple competencies, these core competencies represent their primary roots driving their market position:
Core Competency -- Representative Businesses
Operational velocity & efficiency -- McDonald’s, FedEx, Walmart
Supply chain & logistics -- Walmart, Nike, PepsiCo, P&G
Autonomy & perception --John Deere, Caterpillar, Lockheed, Ford, GM, Rivian
Risk & compliance -- Bank of America, Visa, Amex, PayPal, State Farm, Met Life
Trust & safety -- Airbnb, Bumble
Personalization & loyalty -- Starbucks, Marriott, Sephora
Portfolio / brand curation -- LVMH, Berkshire
Fast data orchestration --JPMorgan, Salesforce, RELX, Thomson Reuters
Content & algorithm --ByteDance, Tencent, Spotify, Pinterest
Scientific discovery -- AbbVie, AstraZeneca, Novo Nordisk, Pfizer, Oura
Creative & design -- Adobe, Canva, Louis Vuitton (LVMH)
Human-first / artisan -- Hermès, Chanel