What is hybrid AI? How it works and its development

What is hybrid AI?

Hybrid AI combines rule-based systems with machine learning to create more adaptable and transparent AI solutions. Traditional AI relies on explicit programming, while machine learning learns from data. By integrating both approaches, hybrid AI gains the reliability and explainability of rule-based systems and the adaptability and pattern recognition capabilities of machine learning.

Hybrid AI typically blends:

• Rule-based systems: Traditional AI using explicit, predefined logic
• Machine learning techniques: Algorithms, such as neural networks, that learn from data

Bringing these methods together helps address the limitations of using either in isolation. Many organizations adopt hybrid AI to create solutions that are both interpretable and capable of adapting to new or evolving data patterns.

Where did hybrid AI come from?

Hybrid AI emerged as AI practitioners saw the limitations of single-approach systems. The idea gained traction in the early 2010s, when it became clear that purely rule-based systems often lacked adaptability, while purely data-driven models could miss critical business rules or compliance requirements.

Notable early adopters include:

• Google: Combined knowledge graphs with neural networks to improve search relevance
• IBM Watson: Integrated multiple AI methods to handle diverse, complex queries

Originally, hybrid AI referred to combining expert systems with neural networks. Today, it represents a broader philosophy: using multiple, complementary AI techniques in a coordinated way to solve specific problems.

This reflects a practical reality—most effective AI applications require multiple techniques working in tandem rather than relying on a single method.

How does hybrid AI work, and how is it typically used today?

Implementing hybrid AI involves pairing different AI technologies so each handles the parts of a problem it solves best. Machine learning is used to detect patterns and generate predictions, while rule-based components ensure outputs comply with defined policies, regulations, or business logic. Common setups combine machine learning frameworks with rule engines or custom-coded logic.

Here’s an example in relation to warehouse inventory management:

• Machine learning analyzes historical sales data, seasonal trends, and current stock levels to forecast future product demand
• Rule-based components apply storage constraints, supplier lead times, and restocking policies (like minimum order quantities, maximum warehouse capacity)
• When inventory for an item is predicted to run low, the system combines the ML forecast with rule-based rules to decide whether to place an order immediately, schedule it for a later date, or skip replenishment altogether

By combining pattern recognition with deterministic checks, hybrid AI reduces false positives while catching more fraudulent activity.

Does hybrid AI matter?

Hybrid AI addresses challenges that machine learning or rule-based systems struggle with on their own. This allows organizations to automate complex processes while maintaining both accuracy and explainability.

When hybrid AI systems explain their reasoning and incorporate explicit business knowledge, decision-making improves. This level of transparency is a competitive advantage compared to opaque “black box” models.

The approach also reflects a broader philosophy: valuing both data-driven learning and human expertise, and building systems where technology supports rather than replaces human judgment.

TL;DR

Hybrid AI combines rule-based systems with machine learning to create AI solutions that are adaptable, interpretable, and effective for complex business needs. The central principle—using complementary AI methods instead of depending on one—offers a practical blueprint for addressing multifaceted challenges.

Bridging the gap between reliability and adaptability in AI

Hybrid AI blends the transparency of rule-based systems with the flexibility of machine learning. Rule-based logic ensures consistent, explainable outcomes, while machine learning adapts to patterns in new data. This combination allows processes to be both precise and adaptable—applying strict rules where accuracy is non-negotiable, and AI-driven insight where context or complexity requires it.

At Ramp, this mirrors how we approach automation in finance: using structured workflows to improve compliance and reliability, while leveraging AI to enhance tasks like transaction categorization and expense reconciliation.

AI agent for finance automation

Ramp recently introduced its first AI agent to handle the routine, repetitive tasks that consume finance teams’ time each month. Take a $5 latte: uploading the receipt, reviewing the charge, and coding the expense in NetSuite can add up to 14 minutes and more than $20 in labor for a single transaction. Multiply that by thousands of expenses and the cost is significant.

By automating these small but frequent tasks, the AI agent frees teams to focus on higher-value work and decision-making.

Explore how Ramp’s AI agents fits into your finance processes and where it could remove the most friction. Learn more about Ramp Agents.