The Power of Hybrid AI: Why One Technology Isn’t Enough
Why the smartest AI strategies embrace multiple technologies instead of relying on just one.
Published
March 19, 2025
The AI Hype and the Choice Dilemma
A new Artificial Intelligence (AI) breakthrough or solution makes headlines every week. While that’s good news for anyone, like yours truly, who is working in the AI field, the hype and over-excitement are also slightly soul-crushing. By ‘soul-crushing,’ I don’t mean the grand visions of AI — utopian or dystopian. I mean how AI is discussed trivially, with single technologies placed on a pedestal as if they alone can solve all corporate challenges. How mainstream media inflates AI’s capabilities proves we’re deep in the hype cycle.
I am a big fan of Gartner’s hype cycle, which illustrates how technology matures. When it comes to current AI applications — particularly LLMs — their true value for enterprises is still being figured out. Right now, they’re nearing the peak of inflated expectations.
(image source: Gartner)
I can imagine the C-suite executives feeling the pressure of the AI hype. For those without deep AI expertise, it’s hard to pinpoint what “AI” really means. Are we talking about a chatbot that mimics human conversation? Or a particular Large-Language Model (LLM) that is “out-performing” the others? Where are the details of where the model is out-performing, and even more importantly, where it is under-performing? Beneath the AI buzzwords are a collection of distinct technologies, each with its strengths and weaknesses. This article makes the case that no single AI technology can do it all – actual value comes from combining them intelligently.
The AI Landscape: Understanding the Core Technologies
Let's examine some of AI's core technologies to understand why it works best as a hybrid approach.
Large Language Models (LLMs) & Generative AI
LLMs make AI-powered chats feel intelligent. They analyze human input and generate responses that sound natural in context. However, they lack deep reasoning and explainability. These models are the latest evolution of natural language processing and deep learning, built on decades of research. LLMs fall under the broader category of generative AI, which refers to models that create text, images, and other outputs based on learned patterns.
Knowledge Graphs & Semantic AI
This technology provides structured and contextualized knowledge. It acts as a digital twin, mapping real-world entities and their relationships – especially useful for relational data. While it doesn’t generate new knowledge on its own, it provides a structured, contextualized representation of data, allowing metadata to be stored alongside it.
Symbolic AI & Rule-Based Systems
Rooted in first-order logic and set theory, Symbolic AI uses structured and deterministic reasoning and not pattern-based learning It offers explainability and control but lacks the flexibility of generative AI. Instead of generating new knowledge, it reasons within the boundaries of existing data and predefined logical rules.
The problem?
Relying on a single AI technology
A solution that relies on only one technology is problematic. For example, a friend recently asked a generative AI chat application for the best dive bars in Helsinki, Finland. The application listed a few places, but more than half had closed years ago. Upon closer inspection, the application didn’t fully grasp what a dive bar meant in the Finnish context or verify whether the listed places still existed. In the end, it simply summarized an outdated Yelp page listing someone’s opinion of the best dive bars in Helsinki.
Enterprises that wish to build solid applications must enable reasoning and generative capabilities, which cannot be achieved with a single AI technology alone. A prominent example of a hybrid approach is to build chatbots that use LLMs for conversational capabilities while utilizing knowledge graphs behind the scenes to surface verified facts.