Hybrid AI systems: How a multi-model approach can help enterprises improve AI model efficiency

More organizations are adopting and developing artificial intelligence (AI) to support a wide range of business functions, from generating content to detecting fraud. While this technology is extremely powerful, relying on a single model won’t deliver optimal results in every use case. 

For instance, an organization may need AI to solve complex or specialized problems, such as evaluating diagnostic imagery in a healthcare setting or writing code for software applications. In these situations, a foundational model, like OpenAI’s ChatGPT and Google’s Gemini, might not deliver the level of accuracy and expert knowledge needed.

Multi-model or hybrid AI systems, on the other hand, can bridge the gap. Rather than relying on one large model, this approach involves developing several smaller models tailored to your organization’s needs. 

Limitations of a single model approach 

AI models, such as large language models (LLMs), can present accuracy issues because training data limits the scope of their knowledge. Even if developers train models on massive datasets with billions of parameters, the models generate output and functionality limited to that training.

For example, OpenAI’s ChatGPT performs well for generalized tasks like language translation or content writing. However, it often produces errors and hallucinations when prompted on more complex topics or problems where training data isn’t detailed enough in scope. This is why relying on a single large model for more specialized tasks can produce unreliable results. 

Beyond output accuracy, a single-model approach has other limitations, including:

• Sustainability. Developing and deploying a large model consumes significant water and energy, increasing a company’s carbon footprint and impacting sustainability initiatives. For instance, Google’s AlphaGo Zero used the equivalent of 1000 flight hours of carbon dioxide emissions over 40 days of training. 
• Computing resources. Training a large model with millions of parameters necessitates extensive computing power. Accessing graphics processing units (GPUs) is a significant bottleneck and expense for companies investing in model development. 
• Transparency. Larger models often lack visibility into how their algorithms operate and generate outputs. This “black box” tendency creates issues when reporting on how models use data, which is necessary to comply with ethical AI frameworks and data privacy laws. 

Benefits of using multiple types of AI models 

While a single, large model is powerful for generalized tasks, a multi-model approach is better suited for niche use cases. It can also address concerns about computing power, transparency, and environmental impact.

With a multi-model approach, developers train several models on smaller, more curated datasets. The concept that not all models need to know everything underscores this methodology—some models only need to master a smaller range of tasks.

Optimized hybrid AI systems address the main limitations of a single large model. The smaller, more specialized multi-model datasets enable developers to:

• Validate and cleanse data more thoroughly than is possible with massive, unwieldy models. This helps ensure more accurate, high-quality inputs. 
• Customize and fine-tune models for unique problems and subject areas, eliminating noise and irrelevant information that large models may contain. 
• Train models with greater efficiency, requiring less powerful hardware. This leads to lower energy, compute, and financial demands, and can make model development more accessible for small to midsize companies. 
• Improve model transparency, since algorithms in smaller models tend to be easier to track and adjust. 
• Simplify and strengthen security infrastructure. Fewer parameters mean fewer potential entry points for attackers, while greater model transparency and data oversight facilitate stronger risk visibility and mitigation.

AI practitioners may also use hybrid AI systems or ensemble learning, leveraging different types of models for various functions. With hybrid AI, developers select the model types best suited for each task. Combining several model types helps balance the strengths and weaknesses of each for a more accurate and reliable AI system.

For example, neural networks are capable of complex reasoning but tend to be opaque and resource-intensive to develop. Decision trees are more transparent than neural networks in their decision-making, but don’t generalize as effectively. Other approaches, like support vector machines (SVMs), are useful for categorizing information in applications like facial recognition or text classification. While some tasks may perform well with one of these model types, others will work best with a combination. 

Challenges and considerations for multi-model AI 

Despite the benefits of multi-model or hybrid AI systems, they come with challenges. Leveraging numerous smaller models can lead to high computational costs and transparency issues, negating some of the main benefits of a multi-model system. Plus, AI development is highly resource-intensive, so organizations must balance model size and the level of specialized knowledge needed to solve their unique challenges. 

When considering an AI transformation, choose an approach that fits with what you’re seeking to accomplish, and periodically re-evaluate your approach to ensure it continues to fit. Consider your AI roadmap, and how your employees or customers already use AI. Also, determine whether those tasks would benefit from smaller, more specialized models—or if a single-model approach is satisfactory. 

Optimize AI development with different types of AI models 

Recent advancements in AI have led to the development of large models built using significantly more parameters than even a few years ago. This has enabled solutions like LLMs to respond impressively to a wide range of prompts, supporting efficiency and creativity for both consumers and businesses. 

However, adopting a single model, regardless of size, is not optimal for enterprises needing AI to tackle complex tasks requiring subject matter expertise. Additionally, some AI-supported functions are better approached through a combination of AI model types, not just neural networks. Organizations may need more resources to fine-tune large models or want to streamline energy and computational demands associated with AI development.

Organizations must identify the right combination of AI models to provide sufficient output accuracy while optimizing development and inference resources for their unique requirements. For general tasks like writing marketing emails, a single-model approach may be the best option. Other use cases, like AI-powered customer support, will benefit from both generalized knowledge and smaller models equipped with product expertise. Alternatively, for highly complex functions like building autonomous vehicles or supporting healthcare decisions, a multi-model approach will be the most appropriate.

As more organizations invest in AI model integration, multi-model systems will be the key to responsible transformation. They will ensure more reliable outputs while improving model efficiency and transparency.

Training LLMs? Read more about networking techniques to improve efficiency.