Time To Rethink Business Models
I am in the tech business. My company offers advisory, engineering and training services for exponential technologies (with a focus on AI software and hardware). Being exposed to these fast-moving fields each day, a thought keeps popping up in my head: How do I keep my market approach relevant in the age of AI? I see business models in whole sectors shifting - what is actually happening?
Let's try to make sense of this...
The current phase of artificial intelligence development, particularly with the maturation of large language models and generative systems, is altering the foundational mechanisms of economic value creation, delivery, and capture. AI is now embedded as a core infrastructure across industries, functioning as an adaptive, scalable layer that influences everything from operational efficiency to strategic decision-making. This shift is not incremental; it is redefining the basic structures through which business is conducted and value is extracted.
1. Intelligence as Infrastructure
AI has become a foundational layer of the economy, similar to electricity or the internet. Businesses now embed advanced AI into every facet of their operations, from customer engagement to supply chain management. The most successful organizations treat AI not as a tool, but as an infrastructure—constantly learning, adapting, and scaling across products and services.
Universal Access: AI capabilities, once the domain of tech giants, are now accessible to businesses of all sizes through cloud-based platforms and AI-as-a-Service offerings.
Continuous Learning: Models improve in real time, drawing on vast data streams and user interactions to enhance performance and relevance.
Embedded Autonomy: AI agents act independently, executing complex workflows and making decisions with minimal human oversight.
2. The Collapse of Traditional Scarcity
AI's ability to generate text, images, code, and even strategic plans at scale has eroded the scarcity that once underpinned many business models. Knowledge work, creative production, and basic analysis are no longer bottlenecks. Instead, new forms of scarcity have emerged:
Unique Data Ownership: Value accrues to those who control proprietary datasets, enabling differentiated AI models and insights.
Trust and Authenticity: As synthetic content proliferates, businesses that can guarantee trust, provenance, and human connection become more valuable.
Orchestration Power: The ability to integrate, coordinate, and govern networks of AI agents and systems becomes a key economic lever.
3. Platformization and Ecosystem Dominance
The age of AI accelerates the rise of platform-based business models. These platforms aggregate users, data, and services, leveraging AI to orchestrate interactions and extract value from network effects.
Multi-Sided Markets: Platforms connect producers, consumers, and third-party developers, with AI optimizing matching, pricing, and personalization.
Data Flywheels: The more a platform is used, the better its models become, further attracting users and partners in a self-reinforcing cycle.
Open Ecosystems: Modular AI components and APIs enable rapid innovation and integration across industries.
4. From Product and Service Sales to Outcomes and Experiences
AI enables a shift from selling discrete products or billable hours to delivering outcomes, usage, and ongoing experiences.
Outcome-Based Models: Businesses charge for results—such as uptime, efficiency gains, or learning outcomes—rather than inputs or time.
Personalization at Scale: AI delivers hyper-personalized products, services, and content to millions simultaneously, creating new forms of customer loyalty and engagement.
Subscription and Access Models: Continuous value delivery replaces one-off transactions, with customers subscribing to evolving AI-powered capabilities.
5. Agentic AI and Autonomous Value Creation
AI agents are increasingly capable of acting autonomously—negotiating, transacting, and collaborating across digital and physical domains.
Enterprise Automation: AI agents manage procurement, logistics, compliance, and customer service end-to-end, reducing the need for middle management and manual intervention.
Collaborative Networks: Swarms of AI agents interact with each other and with humans, coordinating complex tasks and unlocking new forms of productivity.
Emergent Intelligence: As agents learn from their environments and each other, unexpected innovations and efficiencies arise, accelerating economic transformation.
6. Sectoral Shifts: How Core Business Models Are Evolving
Across sectors, the core business models are evolving.
In law, the traditional reliance on billable hours and project fees is giving way to AI-powered advisory services and subscription (or better: pay-per-use) models, with a likely future of autonomous legal agents and outcome-based contracts.
Marketing is moving from labor-based and retainer models to performance-based fees and AI platform subscriptions, with a trajectory toward fully automated campaign orchestration.
Retail is shifting from product sales and franchising to AI-driven personalization, data monetization, and predictive commerce.
Manufacturing is transitioning from product sales and OEM contracts to predictive maintenance and product-as-a-service models, with autonomous factories on the horizon.
Education is evolving from tuition and content sales to adaptive learning subscriptions, micro-credentials, and, potentially, AI-driven mentorship and skills marketplaces.
7. The New Sources of Competitive Advantage
Proprietary Data and Models: Owning unique datasets and fine-tuned AI models becomes the primary source of defensibility.
Human-AI Collaboration: The most resilient organizations blend human judgment, creativity, and empathy with AI’s speed and scale.
Governance and Trust: Ability to ensure ethical, transparent, and reliable AI use becomes a market differentiator.
Speed of Adaptation: Firms that rapidly experiment, learn, and redeploy AI capabilities outpace slower incumbents.
8. Looking Forward: Extrapolating the Trajectory
As AI continues to advance, the economic landscape will be shaped by:
Universal Agentic Infrastructure: AI agents will become as ubiquitous as cloud computing, handling most standard business operations autonomously.
Fluid Business Boundaries: Traditional sector lines will blur as AI-powered platforms enable cross-industry integration and new forms of collaboration.
Continuous Business Model Reinvention: Organizations will need to constantly rethink how they create and capture value, as AI capabilities and societal expectations evolve.
Societal and Ethical Imperatives: The distribution of value, the role of human labor, and the governance of AI will become central economic and political questions.
Conclusion
The age of AI is not a single technological shift but a profound reordering of economic mechanisms. Intelligence, once the rarest and most valuable resource, is becoming abundant and scalable. The future belongs to those who can orchestrate, govern, and differentiate within this new landscape—where data, trust, and adaptability define enduring success.
As for my own entrepreneurial endeavors: I have no clear picture of where all this leads, but I am convinced that speed and adaptability are crucial as is paying very close attention to the "Signals of Change" that appear in the field.
