5 Pillars of AI-driven Business Models
Technologies based on Artificial Intelligence (AI) require us to think not only in tactical-operational terms but also strategically. The impacts on the business model can be transformative, improving existing processes, or may call for a radical reassessment.
Integrating AI into a business model is a multi-level process that requires consideration of:
In recent academic and managerial literature, several authors have focused on identifying the key principles underpinning so-called “AI-driven business models.” Below are some of the main contributions:
Datification
Data and information are not just assets but elements capable of generating value in various ways: both from a transactional perspective and as input for business tools’ algorithms (e.g., DSS). Business models leveraging this factor treat every interaction (e.g., user-product/service, user-system, user-user) as an opportunity to extract insights from the data collected. Examples: Amazon (Alexa), Google (Nest Hub).
Automation
AI-based tools enable the 'intelligent' automation of repetitive and data-intensive tasks, freeing up time and energy for personnel to focus on other operations. Automation also requires pre-work involving optimization and standardization, increasing the scalability potential of the processes involved.
Large-scale Customization
Tools integrating AI and statistical analysis offer opportunities for retrospective (e.g., trend analysis), real-time, and prospective or predictive analysis (e.g., scenario planning and forecasting). The data and patterns identified fuel AI-powered algorithms, enabling product and service personalization and recommendation for large audiences (e.g., Netflix, Spotify) based on the analysis of preferences and usage patterns.
Continuous Innovation (CI)
This goes beyond a mindset oriented toward learning, innovation, and continuous adaptation to endogenous/exogenous forces affecting the organization. CI involves building a cultural, informational, and technological infrastructure that treats change as a natural and ongoing dynamic, emphasizing agility as a pervasive and cross-functional capability. Some companies (e.g., Microsoft) have transformed their internal structures to facilitate CI through centralized data platforms, breaking down organizational silos and promoting team collaboration.
Risk Exploitation
This refers to the ability to view the risks associated with the use of AI-based technologies as probable events that can have positive (opportunities) or negative (threats) impacts. These risks become ‘pivots’ for reflection, spaces of creativity within (and beyond) which new ways of generating value can be identified. This is possible with thematic knowledge, awareness of implications, and reference documentation (e.g., EU Artificial Intelligence Act).
Adopting an AI-driven model goes beyond optimizing existing processes. It requires a profound transformation and rethinking of the business model, building the company around data, algorithms, and the ability to continuously adapt. This transformation is essential for companies aiming to remain competitive, as AI offers a strategic advantage that far exceeds operational efficiency.
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