In recent years, artificial intelligence (AI) and digital platforms have triggered a seismic shift across industries, reshaping how businesses operate and compete. This shift, often referred to as the “digital business revolution,” has impacted both technology-driven companies and industries traditionally unaffected by advanced technologies. As AI evolves from predictive algorithms to decision-making systems, it is transforming productivity, efficiency, and organizational structures.
In their paper, “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms,” Hanna Halaburda, of the NYU Stern School of Business; Jeffrey Prince, of IU Bloomington Kelley School of Business; D. Daniel Sokol, of USC’s Marshall School of Business; and Feng Zhu, from Harvard Business School’s D^3 Platform Lab discuss the trajectory and general impact of AI, specifically focusing on its potential to improve prediction and decision-making, leading to changes across all organizational departments. The paper further examines the specific impacts of AI and digital platforms on pricing, healthcare, content industries, and merger policy, highlighting both the potential benefits and risks associated with these technologies.
Key Insight: AI is a General Purpose Tool and Increases the Shift in Value from Tangible to Intangible Assets
“The Business Revolution is changing the office, the store, and the market, just as the Industrial Revolution earlier changed the factory.” [1]
The Digital Business Revolution refers to the transformative impact of digital technologies, particularly AI and digital platforms, on organizations and markets. It’s characterized by the shift from tangible assets to intangible ones, like data and intellectual property, and the movement of production from within firms to external third parties. This revolution is reshaping business strategies, leading to the emergence of new business models and revolutionizing various sectors of the economy.
As a general purpose technology, the article cites research that finds that AI and machine learning are reshaping firm behavior by enhancing prediction and decision-making across various sectors. They note that while AI promises productivity growth and operational efficiency, its adoption varies widely, with larger firms and startups leading. Much AI adoption is currently accomplished through third-party software rather than customized in-house solutions.
Key Insight: Dynamic AI-Driven Pricing Increases Market Efficiency but Raises Concerns
“AI already has begun to revolutionize pricing (Asker et al., 2024), and it also may lead to an increased ability for dynamic pricing.” [2]
AI is revolutionizing pricing and may enable improved dynamic pricing strategies. This allows businesses to adjust prices in real time based on various factors, including demand, competition, and customer behavior. While this can lead to greater efficiency and profitability, it also raises concerns about potential collusion and unfair pricing practices.
Key Insight: AI Enhances Healthcare Precision but Faces Challenges in Trust and Integration
“Deep learning AI may lead to better health outcomes than traditional types of diagnosis for areas such as cancer detection or heart disease.” [3]
AI promises to transform healthcare by improving diagnosis, treatment, and overall patient outcomes. It can enhance the accuracy and efficiency of medical procedures, leading to better disease detection and management. However, the effective implementation of AI in healthcare requires building trust among medical staff and patients, ensuring data privacy, and addressing potential biases in algorithms.
Key Insight: Digital Platforms Reshape Content Distribution and Discovery, Fueling Innovation
“Platforms and digitization have reduced costs of distribution in these content industries and increased differentiation.” [4]
Digital platforms have disrupted traditional content industries, like music, film, and publishing, by reducing distribution costs, increasing product differentiation, and transforming the product discovery process. Platforms leverage crowdsourcing and curation to connect consumers with new content, but challenges include maintaining user trust, for instance, in the face of fake reviews, and addressing potential antitrust concerns related to platform recommendations.
Key Insight: Mergers and Acquisitions Drive Innovation but Risk Stifling Competition
“Merger is a potential solution for firms that do not have the internal capabilities to provide greater scale or scope in the digital business revolution.” [5]
Mergers and acquisitions are a significant aspect of the digital business revolution. Tech companies acquire startups to gain access to new technologies and talent, potentially fostering innovation. However, concerns exist regarding “killer acquisitions,” where large companies acquire promising startups only to shut them down and eliminate competition.
Why This Matters
For business leaders, the digital business revolution represents both a challenge and an opportunity. AI is no longer a futuristic concept – it is transforming industries today. Companies that can harness AI’s predictive capabilities, shift their focus to intangible assets, and optimize decision-making through automation are poised to thrive. Mergers and acquisitions in this new landscape are not just about consolidating power but driving innovation and expanding market influence. However, it is important to be aware of the risks, uncertainties, and disparities in implementing and using AI. Leaders must continue to explore ongoing study and policy decisions around AI.
References
[1] Hanna Halaburda, Jeffrey Prince, D. Daniel Sokol, and Feng Zhu, “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms,” Journal of Economics & Management Strategy 33, no. 2 (January 24, 2024), 269-275: 269.
[2] Halaburda, et al., “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms”, 271.
[3] Halaburda, et al., “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms”, 271.
[4] Halaburda, et al., “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms”, 271.
[5] Halaburda, et al., “The Business Revolution: Economy-Wide Impacts of Artificial Intelligence and Digital Platforms”, 272.
Meet the Speakers
Hanna Halaburda is an Associate Professor of Technology, Operations and Statistics at the NYU Stern School of Business. Professor Halaburda studies how technology changes economic forces and can affect business models and interactions in the marketplace. She also focuses on competition between digital platforms. She holds a PhD in Economics from Northwestern University.
Jeffrey Prince is a Professor of Business Economics at University of Indiana Bloomington’s Kelley School of Business. His areas of expertise are industrial organization, applied econometrics, strategy, and regulation. He holds a PhD from Northwestern University.
D. Daniel Sokol is a Professor of Law and Business at the USC Gould School of Law and Marshall School of Business. His research and teaching focuses on complex business issues from early stage start-ups to multinational businesses and the issues that businesses face regarding competition: antitrust, data breaches, corporate governance, digital platforms, compliance, innovation, M&A, digital transformation, and global business regulation.
Feng Zhu is a Professor of Business Administration at Harvard Business School, faculty lead in D^3’s Platform Lab, and faculty co-chair of the Harvard Business Analytics Program. Zhu is a leading expert on platform strategy, digital innovation and transformation, competitive strategy, and business model innovation.