AI's Impact on Corporate Strategy
Artificial Intelligence Engagement, Firm Value, and Risk Transformation
This paper introduces a novel measure of AI engagement from 10-K filings, revealing its profound impact on firm performance, valuation, and risk profiles. Understanding this engagement is crucial for investors, managers, and policymakers navigating the AI-driven economy.
Executive Summary: Key Findings on AI Engagement
Our analysis, leveraging a new, standards-based AI intensity measure derived from 10-K filings, uncovers critical insights into how firms adopt and benefit from AI technologies. These findings underscore AI's role as a transformative, risk-reshaping force in the modern enterprise.
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Increase in Sales Growth (coef.)
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Increase in Tobin's Q (1-SD effect)
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Lower Cost of Equity (bps)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The research develops a novel, standards-based AI intensity measure using textual analysis of 10-K filings. This approach is superior to other proxies because 10-K filings are comprehensive, standardized, and legally mandated, ensuring disciplined and verifiable reporting. A purpose-designed dictionary, grounded in international regulatory and technical standards, including NIST AI Risk Management Framework and the EU AI Act, organizes approximately 500 AI-related terms into eight conceptual categories. Phrase-level Natural Language Processing (NLP) systematically matches these terms to 10-K filings to measure overall and category-specific AI disclosure intensity, minimizing linguistic ambiguity.
Enterprise Process Flow
Extract Full 10-K Text
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Lowercase, Remove HTML, Normalize Punctuation
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Tokenize Words & Multi-word Phrases
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Match Against AI Dictionary (500+ Terms, 8 Categories)
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Exclude Ambiguous Stand-alone Terms (e.g., 'model')
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Compute AI Intensity Score (AI Terms per 1,000 Words)
500+
AI-related terms in dictionary
AI engagement is systematically linked to improved firm performance. Higher AI intensity predicts faster sales growth, greater capital investment, and higher profitability. It also correlates with slower employment growth, consistent with task substitution. These findings validate the measure as capturing substantive AI adoption and its real economic consequences.
0.297
Coefficient on AI Intensity predicting Sales Growth
AI-Driven Workforce Adjustment
The paper notes that AI adoption is associated with significantly slower employment growth, consistent with AI technologies substituting for labor in specific tasks and facilitating organizational restructuring. This aligns with broader economic trends where AI automates routine roles, reshaping labor demand and contributing to operational efficiency.
Firms with higher AI intensity command significantly higher valuation multiples. This premium arises primarily through a lower cost of equity rather than higher expected cash flows, positioning AI as a risk-transforming technology. AI engagement improves firms' information environments, leading to higher analyst forecast accuracy and lower forecast dispersion, and reshapes exposure to technology-related systematic risk, shifting from firm-specific operational risk to more transparent technology-cycle risk.
-0.127
Coefficient on AI Intensity for Cost of Equity (PEG model)
| Measure | Before 2020 | After 2020 |
|---|---|---|
| Low AI Firms: Tech Factor Exposure | Limited/Insignificant | Negative & Significant (Underperform) |
| High AI Firms: Tech Factor Exposure (Incremental) | Limited/Insignificant | Positive & Significant (Co-move Positively) |
Estimate Your Enterprise AI ROI
Our AI Adoption ROI Calculator helps you estimate potential cost savings and efficiency gains by integrating AI into your operations. Adjust the parameters below to see the impact tailored to your organization.
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Your AI Implementation Roadmap
Embark on a structured journey to AI adoption. Our roadmap outlines the critical phases from strategic assessment to continuous optimization, ensuring a smooth and effective integration of AI into your enterprise.
Phase 1: Strategic Assessment & Data Readiness
Identify key business areas for AI integration, assess existing data infrastructure, and develop a robust data strategy. This includes data collection, cleaning, and establishing governance frameworks to ensure AI model effectiveness.
Phase 2: Pilot Program & Technology Selection
Launch targeted AI pilot projects in high-impact areas. Select appropriate AI technologies (e.g., machine learning platforms, generative AI tools) and begin initial development and testing. Focus on demonstrating tangible value early on.
Phase 3: Scaled Deployment & Integration
Scale successful pilot projects across the enterprise, integrating AI solutions into core business processes and IT systems. Establish MLOps practices for continuous monitoring, maintenance, and improvement of AI models in production.
Phase 4: Performance Monitoring & Iteration
Implement continuous performance monitoring of AI systems, track key metrics, and gather feedback. Iterate on models and strategies based on real-world performance, ensuring long-term value creation and adaptation to evolving business needs.
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