We all have mental models we use to make sense of the world, whether we realize it or not. These "logics" are the lenses through which we observe, reason, and make decisions. They’re incredibly powerful because they operate in the background, shaping our understanding of everything from personal relationships to global economies.

Think about Newtonian mechanics—the classic physics of objects and forces. It describes a world of predictable, cause-and-effect relationships. This logic is so influential that it extends far beyond science; it’s baked into how we think about business, where we see products as objects with inherent properties, like value. This is a powerful and useful model, but it’s not the only one. A newer logic, quantum mechanics, describes a world that is less about fixed properties and more about context, probability, and systemic relationships. This shift in thinking from a deterministic world to a probabilistic one offers a different way to understand how things work.

The Old Blueprint: When Value Was in the Product

For a long time, the business world has run on a logic rooted in Newtonian thinking. Often called "goods-dominant" (G-D) logic, it views economic activity as the exchange of things that have value embedded within them.

Consider the simple act of buying a car with cash. Under G-D logic, this is a straightforward transaction: an exchange of value for value. The car has value, the cash has value, and the deal is done. This model doesn't really concern itself with where the car came from or how it will be used later; it focuses on the exchange of tangible goods with fixed properties. This mindset was perfectly suited for an economy dominated by agriculture and manufacturing, but it starts to show its limitations when we consider how modern value is created, especially when thinking about what is AI and its role in business.

A New Perspective: Service-for-Service Exchange

An alternative mindset is emerging, one that better reflects our interconnected world. It’s called "service-dominant" (S-D) logic, and it proposes that all economic exchange is fundamentally about service-for-service. It’s not just about goods for money, but about actors—people and organizations—applying their skills and resources for the benefit of others. Exploring this idea is key to understanding generative AI for business.

In this view, value isn't something embedded in a product; it’s an outcome that is co-created. Let’s go back to that car purchase. From an S-D perspective, the car itself only has potential value. Its real value is realized when the buyer integrates it with other resources: their driving knowledge, access to roads, fuel, and insurance. The money the seller receives also represents potential—the right to future services from others. The transaction is just one moment in a vast, interconnected system of service exchanges and resource integration that involves millions of people. This new perspective doesn't just offer an interesting Innovation Theory; it fundamentally changes how we approach business design.

From Mental Models to Organizational Architecture

These underlying logics don’t just stay in our heads; they dictate the Organizational Architecture of our companies. Just as a building’s architecture determines how we move and interact within it, an enterprise's architecture shapes its processes and IT infrastructure. A good architecture makes simple changes easy and complex changes possible.

For decades, many companies built monolithic enterprise architectures—complex, interwoven systems that were slow and difficult to change. In the late 1990s, Service-Oriented Architecture (SOA) emerged, offering more flexibility by organizing capabilities into shared, modular services. More recently, we’ve seen the rise of digital platform companies like Uber and Airbnb. These "digital attackers" use cloud technologies and open platforms to bring resources and data together at incredible speeds, completely disrupting traditional industries. This evolution in types of AI and business models shows a clear trend towards more open and connected systems.

Building for the Future with Service Dominant Architecture

This evolution leads us to a new blueprint: Service Dominant Architecture (SDA). Grounded in S-D logic, SDA provides a set of design patterns for building and orchestrating capabilities in a way that is agile, responsive, and ready for the future.

Think of SDA like a set of Lego bricks. Open-source and cloud platforms form the baseplate, while technical and business services are the individual bricks. Each brick is designed to support the roles needed for value co-creation in a service ecosystem. This approach allows businesses to rapidly change and adopt new technologies, including generative AI tools, turning resource density into market-accelerating service innovations. For instance, a car company using SDA can easily onboard partners like telematics providers or car-sharing services, creating a comprehensive mobility ecosystem that offers constantly improving value propositions to the customer.

The Coming Era of the AI-Powered Digital Twin

So, what does this all mean for the future, especially with the rapid advancement of artificial intelligence? We are heading toward a vision we can call "X+AI," where "X" can be anything—a person, a car, a business—and it is paired with an AI-powered digital service twin.

Right now, this is still a vision. Today’s AI has made incredible progress in areas like memory, pattern recognition, and machine learning, giving us powerful generative AI examples. When you ask is ChatGPT generative AI, the answer is yes; tools from labs like Open AI are a perfect example of this current wave. However, there are still two major gaps between today's AI and true, human-like capability:

1. Social Learning: Humans learn prodigiously by watching, imitating, and interacting with others. No AI system comes close to a child's ability to learn within a complex social context.
2. Responsible Learning: Becoming an adult involves learning to be responsible—understanding how your actions help or harm others. AI systems lack this deep model of responsibility and social roles.

Investing Wisely in an AI-Driven World

As we move forward, the difference between a positive and negative future will come down to the investments we make. A future of "great expertise" and "great collaborators" is possible if we focus on strengthening trust and building systems that benefit everyone. This requires a conscious effort to upskill ourselves and our organizations.

Practitioners, educators, and scholars all have a role to play. By using AI ChatGPT and other tools, practitioners can automate routine tasks and augment their skills. Educators can prepare the next generation to use and challenge these new models, while scholars can build better, more comprehensive models of our world. The key is to move away from outdated G-D logic and embrace a service-centric mindset that is fit for the AI era.