It’s no secret that artificial intelligence is changing the game. Whether it’s through automation that handles repetitive tasks or augmentation that gives human workers superpowers, AI is fundamentally lowering the cost of delivering better service on a massive scale. This means more people, everywhere, can get affordable access to innovative services. To really understand this shift, it's crucial to look at how different types of AI are being applied today. We’re seeing a surge in generative AI for business, with countless high-skill, high-paying jobs now focused on creating new AI use cases to drive a return on investment.

These use cases aren't just tech projects; they are data-driven service innovations. They have to be carefully woven into existing company systems and managed effectively to succeed. Looking at the world through the lens of "service" provides a clear view of how these changes are unfolding right now.

The Rise of Service Robots and Platforms

When most of us think of household robots, we picture devices without arms or legs—think Siri or Alexa, which respond to simple voice commands, or a Roomba cleaning the floors. But more advanced service robots are already appearing in businesses. Imagine walking into a large retail warehouse; instead of just a human greeter, you might encounter a robot that uses speech recognition or a touchscreen to help you find what you need. These robots are popping up in airports, hotels, and hospitals, taking on roles traditionally held by information desk staff.

Of course, their adoption faces hurdles. There are technical challenges in getting them to handle a wide range of customer requests and social barriers, like employees' concerns about job loss. This is where telerobotics comes in. By allowing a skilled person to remotely monitor and control a small fleet of robots, we can bridge the gap. An operator can step in when a robot gets stuck, and that very interaction creates valuable training data to make the AI smarter over time. This shows how automation and human augmentation are two sides of the same coin, whether it's helping shoppers in a store or using drones to find lost hikers in a national park.

This trend extends to our daily lives through the “platform society.” Most of us can’t imagine life without smartphone apps connected to online platforms. These are digital services that use technology to give us easy access to things we need while collecting user data to improve the experience—often with AI. Businesses like Uber, Airbnb, and Netflix are huge investors in AI, particularly in recommendation systems that suggest what we might want to buy, watch, or do next. The business models for many of these companies depend on keeping customers highly engaged, sometimes to a degree that raises social concerns.

An interesting development is the growth of platforms that create earning opportunities. We can think of them in two categories: labor platforms that require your time and skills (like driving for Lyft) and capital platforms that require assets, time, and skills (like selling on eBay). The application of generative AI for business here is transformative, potentially turning labor platforms into capital platforms. For example, a Lyft driver could one day own and operate a fleet of tele-robotic cars, creating a franchise-like opportunity and shifting from a pure laborer to an asset owner.

A New Way of Thinking: Service Science

To make sense of this changing world, we need a new framework. Service science offers one by viewing the world as an evolving ecosystem of people, businesses, and nations interacting in complex ways. In this view, every entity is a “responsible actor” with an identity, a reputation, and aspirations for the future. We all want to be better versions of ourselves.

These actors interact through value propositions—essentially, promises to cooperate for mutual benefit. When these interactions are successful, it’s a win-win outcome where both parties gain something, and their reputations improve. This is the essence of what service scientists call “value cocreation.” For thousands of years, the knowledge guiding these interactions was stored in our brains, passed down through generations, and later, in books. Today, that knowledge is increasingly stored in AI models, or what you might call a what is a neural network in ai. Unlike a book, an AI model can act on the information it holds, creating a dynamic new force in our social and economic ecosystems. Given the growing impact on our lives, the intersection of Artificial Intelligence and Society has become a critical area of study.

Shifting from Goods to Service-Dominant Logic

This new reality requires a new mindset, a departure from the way we’ve traditionally thought about business. For centuries, our economic thinking has been dominated by what could be called a “goods-dominant logic.” It sees the world in terms of exchanging things with inherent value—you trade cash (which has value) for a car (which has value). It’s a simple, transactional view.

An emerging alternative, “service-dominant logic,” offers a more powerful lens. It suggests that all economic exchange is fundamentally service-for-service. Money simply represents a right to future service. The car you bought has only potential value until you combine it with other resources: your driving skills, access to roads, and fuel. From this perspective, value isn't embedded in a product; it’s cocreated through a continuous, interactive process involving countless actors in a broad service ecosystem. This shift in perspective is a core concept in modern Innovation Theory and is essential for understanding what is generative ai's true potential. It moves our focus from just making things to designing entire ecosystems for mutual benefit.

The Blueprint for the Future: Service Dominant Architecture

If we’re going to operate with a new logic, we need a new blueprint. In business, this blueprint is the Organizational Architecture. For decades, companies built massive, complex, and rigid IT systems. They were powerful but slow to change. In the late 90s, Service-Oriented Architecture (SOA) introduced a more modular approach, allowing businesses to be more flexible and open.

Today, even that isn’t enough. Digital attackers like Airbnb and Uber, built on cloud technologies and open platforms, are disrupting traditional industries because they can change at lightning speed. To compete, businesses need a new blueprint: a Service Dominant Architecture (SDA). Grounded in service-dominant logic, SDA is an organizing logic for building companies and platforms that can thrive in an era of constant change. It’s a set of design patterns that enables businesses to be more agile, responsive, and collaborative.

The core idea of this Organizational Architecture is to make simple changes easy and hard changes possible. An SDA-driven company can quickly onboard partners, integrate new employee skills, and gain deeper insights from data. Think of it like Lego: a base plate of cloud technology supports a set of pre-configured service bricks that can be quickly assembled into new customer solutions. This architecture turns resource density into market-accelerating service innovations. This is one of the most powerful generative ai examples in a structural sense, reshaping how businesses can operate and cocreate value.

Ultimately, understanding what is AI and its different types of AI is just the first step. To truly succeed, businesses must adopt a new logic for how value is created and implement a new architecture that fosters agility and collaboration. This is the path to building a better future version of not just our businesses, but our entire service ecosystem.