Artificial intelligence now underpins much of modern digital infrastructure, from content recommendations and fraud detection to language translation, medical diagnosis, and decision support in government and business. Despite its widespread use, a fundamental question remains contested among policymakers, technologists, investors, and regulators: should AI be treated as a product or as a service?
This distinction matters because classification shapes how AI is priced, regulated, taxed, procured, and governed, as well as who bears responsibility when systems fail. In fast-growing digital markets such as Nigeria, where adoption is accelerating alongside evolving regulation, the issue is especially significant.
This article offers a clear, evidence-based explanation of AI as a product, a service, and a hybrid of both. It explores how AI operates in practice and how major global firms deliver AI capabilities.
Defining the core concepts
To understand whether AI is a service or a product, it is first necessary to clarify what each term traditionally means.
A product is generally understood as a tangible or intangible item that is sold as a unit. Once purchased, ownership or licence rights are transferred to the buyer. Software sold on a perpetual licence, packaged hardware, and downloadable applications have historically fallen into this category.
A service, by contrast, is an ongoing activity performed for a user. The value lies not in ownership, but in continuous access, performance, maintenance, and support. Cloud computing, telecommunications, and subscription-based software exemplify this model.
Artificial intelligence does not fit neatly into either category. Unlike conventional software, AI systems learn from data, adapt over time, and often rely on constant updates, model retraining, and infrastructure support. These characteristics complicate traditional definitions and have led to a range of commercial and regulatory approaches.
AI as a product
In certain contexts, AI functions much like a product. This is most evident where AI is embedded within a discrete system that can be purchased, licensed, or deployed independently.
How AI operates as a product in practice
AI behaves like a product when delivered as a self-contained solution. This may include an algorithm licensed to a company, an AI-powered device, or an enterprise system installed within an organisation’s own infrastructure.
For example, an AI-based medical imaging system sold to a hospital may include trained models, user interfaces, and diagnostic tools delivered as a package. Once deployed, the hospital may operate the system locally, subject to licence terms, without relying on continuous external computation.
Similarly, AI embedded in physical products such as smart cameras, industrial robots, or biometric access systems is often treated as part of the product itself. The intelligence is a feature that enhances the core offering.
Use cases where the product model dominates
AI-as-product is common in sectors where control, security, or regulatory compliance requires local deployment. Financial institutions may purchase fraud-detection models that run in their own data centres. Governments may acquire AI tools for identity verification or document analysis under strict procurement rules.
Strengths and limitations of the product model
The product model offers predictability. Buyers know what they are acquiring, and vendors can define clear boundaries of responsibility. However, it also struggles with the realities of modern AI. Models can degrade without new data, biases may emerge over time, and security threats evolve. A static AI product risks becoming obsolete unless paired with ongoing updates, which begins to resemble a service relationship.
AI as a service
The dominant commercial model for AI today treats it as a service. In this form, users do not buy the intelligence itself. Instead, they access AI capabilities through platforms, APIs, or subscriptions, usually delivered via cloud infrastructure.
How AI-as-a-service works
Under this model, AI systems are hosted, maintained, and continuously improved by providers. Users interact with them on demand, often paying per use, per request, or through recurring subscriptions. The provider handles model training, infrastructure scaling, security updates, and performance optimisation.
Large technology firms have popularised this approach. OpenAI offers language and image models through application programming interfaces, allowing businesses to integrate advanced AI into their products without building models from scratch. Google delivers AI services through its cloud platform, supporting tasks such as data analysis, speech recognition, and machine learning operations. Microsoft embeds AI services across enterprise software, cloud infrastructure, and productivity tools.
Use cases that favour the service model
AI-as-a-service is particularly suited to applications requiring scale, flexibility, and rapid innovation. Customer support chatbots, recommendation systems, language translation, and predictive analytics all benefit from continuous learning and access to vast computational resources.
In Nigeria’s growing technology ecosystem, startups often rely on AI services to reduce upfront costs. Instead of investing in expensive hardware and specialised staff, they can integrate AI capabilities as needed, paying only for what they use.
Benefits and trade-offs
The service model lowers barriers to entry and accelerates innovation. It allows small organisations to access world-class AI capabilities that would otherwise be unattainable. However, it also introduces dependencies. Users rely on external providers for uptime, pricing stability, and data handling practices. Questions of accountability become more complex when AI decisions are made by systems hosted and updated outside the user’s direct control.
The hybrid reality: AI as both product and service
In practice, most real-world AI deployments combine elements of both models. AI is increasingly best understood as a layered offering in which products and services coexist.
A company may license an AI model as a product but require a service agreement for updates, monitoring, and compliance support. A physical device may include embedded AI while also connecting to cloud services for advanced processing.
This hybrid approach reflects the technical reality of AI. Intelligence is not static. It evolves through data, feedback, and iteration. Treating AI purely as a product ignores this dynamism, while treating it purely as a service overlooks cases where ownership and local control are essential.
Global perspectives on AI classification
Different jurisdictions approach the AI product-versus-service question in distinct ways, shaped by legal traditions and economic priorities.
In the European Union, regulatory frameworks increasingly focus on AI systems as socio-technical services that produce ongoing effects. Liability, transparency, and risk management are emphasised over ownership models. In the United States, market-driven approaches dominate, with companies able to define AI offerings as products, services, or platforms.
International standards bodies tend to avoid rigid classification, recognising that AI cuts across existing categories. This reflects a broader understanding that AI is an enabling technology rather than a single commodity.
Implications for the economy, jobs, and governance
How AI is classified has tangible implications.
Economically, service-based AI models tend to concentrate value in large global providers, while product-based approaches may support local integration and customisation. For Nigeria, balancing access to global AI services with the development of local capacity is a strategic concern.
In the labour market, AI services can accelerate automation, but they also create demand for new skills in data management, oversight, and system integration. Treating AI as a service underscores the importance of continuous learning and workforce adaptation.
From a governance perspective, service-based AI raises questions of accountability. When decisions are made by external systems, regulators must determine who is responsible for errors or harm. Clear frameworks are essential to protect users and maintain trust.
Expected changes for meaningful progress
For AI to deliver sustained benefits, clarity is essential. Policymakers need frameworks that recognise AI’s hybrid nature, rather than forcing it into outdated categories. Organisations need procurement and governance models that account for ongoing learning and risk. Educational institutions must align curricula with the realities of AI as both a tool and an evolving system.
In Nigeria, strengthening data protection enforcement, investing in digital infrastructure, and supporting local AI research will help ensure that AI services and products alike contribute to national development.
Closing perspective
So, is AI a service or a product? The most accurate answer is that it is both, and neither alone. Artificial intelligence is better understood as a capability that can be packaged, accessed, and governed in multiple ways depending on context.
Recognising this complexity allows users, regulators, and developers to move beyond simplistic labels. What matters most is not how AI is categorised, but how responsibly it is deployed, how transparently it operates, and how equitably its benefits are shared.
Senior Reporter/Editor
Bio: Ugochukwu is a freelance journalist and Editor at AIbase.ng, with a strong professional focus on investigative reporting. He holds a degree in Mass Communication and brings extensive experience in news gathering, reporting, and editorial writing. With over a decade of active engagement across diverse news outlets, he contributes in-depth analytical, practical, and expository articles exploring artificial intelligence and its real-world impact. His seasoned newsroom experience and well-established information networks provide AIbase.ng with credible, timely, and high-quality coverage of emerging AI developments.