The worldwide spending on artificial intelligence is projected to reach $632 billion by 2028, prompting a fundamental reassessment of business models across industries, according to recent analysis.
The shift is driven by the increasing ubiquity of AI features and the need for companies to find sustainable ways to monetize their AI investments. Whereas investment in AI continues at a staggering pace – including substantial commitments to chip development – profitability remains elusive for many, raising questions about the long-term viability of current approaches.
Traditionally, software companies have relied on established models like SaaS (Software as a Service). However, the unique economics of AI, particularly the variable costs associated with inference and model performance, are forcing a re-evaluation. Companies are now experimenting with blended models that combine subscription fees with usage-based pricing, premium add-ons, and data services.
As of 2024, approximately 35% of businesses had already integrated AI into their operations, highlighting the strategic importance of adapting to new business models. This integration is not limited to technology companies; industries across the board are exploring how AI can redefine their value propositions and revenue streams.
The emerging AI business models can be broadly categorized around three core capabilities delivered by what is being termed the “AI factory”: predictions, pattern recognition, and automation. These capabilities are then applied to create AI-enhanced products and services.
Several distinct models are gaining traction. These include AI-enhanced products offered as a service, data-as-a-service models leveraging proprietary datasets, and AI platforms that enable other businesses to build their own AI applications. The choice of model depends on a company’s specific strengths, data assets, and target market.
Despite the potential benefits, significant challenges remain. Securing AI systems and establishing ethical governance frameworks are paramount concerns. The need for robust data pipelines, skilled AI talent, and ongoing model maintenance adds complexity and cost.
Harvard Business School research indicates that the current AI boom is fueled by massive investments, but the path to profitability is not yet clear. The focus is shifting from simply developing AI capabilities to finding sustainable ways to capture value from them.
Forbes reported in July 2025 that four emerging AI business models are reshaping the future of enterprise, but did not specify what those models were. McKinsey has noted the evolution of SaaS business models in the AI era, specifically highlighting the rise of generative AI applications and consumption-based pricing.
High Peaks, an AI development company, is actively assisting businesses in navigating this landscape, offering custom AI solutions and guidance on selecting appropriate business models. The company emphasizes the importance of understanding the diverse types of AI models, including machine learning, and their respective applications.
The debate over the profitability of AI companies continues, with some analysts questioning whether the current investment levels are sustainable in the long term. The industry awaits further clarity on how these new business models will perform as AI technologies mature and grow more widely adopted.
