Researchers have identified four new business models for the era of agentic artificial intelligence:
- Existing+. Augment an existing business model with AI.
- Customer Proxy. Achieve customer outcomes through predefined processes executed by AI.
- Modular Creator. Use AI to assemble reusable modules (including third parties) to assist in achieving customer outcomes, with no predetermined process.
- Orchestrator. Achieve customer outcomes by using AI to assemble an ecosystem of complementary products and services, with no predetermined process.
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If your enterprise is pivoting amid a changing technology landscape, rest assured that you’re not alone. A recent research brief from the MIT Center for Information Systems Research outlined how business models are evolving to keep pace with advances in artificial intelligence, and what it takes to successfully navigate change.
The original digital business models
To understand new business models for the AI era, it helps to unpack the old ones first. In 2013, MIT CISR researchers and identified four digital business models:
- Supplier companies, which sell products through third parties, like manufacturers.
- Omnichannel companies, which have a digital and physical presence, such as retailers and banks.
- Modular Producers, which offer plug-and-play products or services, such as payment service providers.
- Ecosystem Drivers, which offer a go-to destination in a given customer domain (e.g., housing) and connect customers with providers.
These models have seen significant shifts in the past 12 years, with companies that lead or otherwise participate in a digital ecosystem becoming far more prevalent than traditional brick-and-mortar sellers. Focusing on firms’ dominant models, supplier and omnichannel business models are much less prevalent today, while companies with ecosystem driver business models have grown from 12% of businesses in 2013 to 58% of businesses in 2025. In large part, this is because these companies were the only ones of the four to exceed industry-average revenue growth.
These shifts, coupled with rapid adoption of AI in all its forms — machine learning plus agentic, generative, and robotic AI — prompted the development of a new business model framework.
4 business models for the AI era
For the update, Weill, Woerner, and colleagues and Gayan Benedict used survey data obtained from 2,378 companies between 2013 and 2025 to organize business models into four new categories. They used the example of a hypothetical financial services company to describe how the business models operate in theory.
- Existing+: These firms augment an existing business model with AI. Here, a financial services company could enhance the traditional advisory process by using AI to analyze customer information and provide personalized recommendations.
- Customer Proxy: These firms achieve customer outcomes (within guardrails) using predefined processes now supported by AI. In this case, a financial services company could set parameters to automatically manage a customer’s investment portfolio.
- Modular Creator: Much like producers of plug-and-play products, these firms use AI to assemble reusable modules (including those from third parties) into tailored service bundles. Applying this model, a financial services company could create and recommend a bundle of investment, insurance, and credit products that align with a customer’s goals.
- Orchestrator: These firms achieve customer outcomes (within guardrails) by using AI to assemble an ecosystem of complementary products and services. In this case, a financial services company could provide a fully managed wealth solution that automatically and continuously optimizes the customer’s investment portfolio.
How One New Zealand Group has evolved its business model
The ongoing transformation of telecommunications provider One New Zealand Group illustrates these business models in action. Currently, for example, the company uses AI agents to help answer customers’ frequently asked questions and assist employees in serving customers (the Existing+ model); act on requests to upgrade plans or create service tickets (Customer Proxy); and monitor power failures, forecast demand, and recommend action during weather-related service disruptions (Modular Curator).
Looking ahead, One NZ intends to bring autonomous AI agents to marketing operations (Orchestrator). Agents would be capable of creating personalized campaigns and adapting them based on how customers respond. The marketing team would set goals and guardrails for the AI agents and monitor their performance.
Companies seeking to adapt the way that One NZ has need to understand where they can create value, according to the researchers. Does your company merely assist customers, or can it represent their goals through autonomous action? Is business execution built on a structured process, or can that process be adapted, with the help of AI agents, based on a customer’s desired outcomes?
Leaders looking to understand the opportunities AI offers their company can start by identifying existing AI-enabled business models that they can scale, and the corresponding AI capabilities a company needs to build.
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This article is based on research by Peter Weill, Ina Sebastian, Stephanie Woerner, and Gayan Benedict from the MIT Center for Information Systems Research.
Peter Weill is a senior research scientist at MIT Sloan and chairman of MIT CISR. His work explores future trends, such as digital business models, IT investment portfolios, and AI maturity models, to help organizations maintain a competitive edge. Ina Sebastian is a research scientist at MIT CISR. She studies how large enterprises transform for success in the digital economy, with a focus on digital partnering, value creation, and value capture in digital models. Stephanie Woerner is a principal research scientist at MIT Sloan and the director of MIT CISR. She studies how companies use technology and data to make more effective business models, as well as how they manage associated organizational change, governance, and strategy implications. Gayan Benedict is an industry research fellow at MIT CISR and a technology partner at PwC Australia.