LLMs, GenAI, AgenticAI and similar topics are now on every content on your feed. Although, the push for AI adoption present a fundamental challenge for enterprise architects who seek :
How to balance AI innovation with the enterprise requirements for governance, explainability, and reliable execution of core business requirements?
The architectural decisions made today will determine whether AI initiatives deliver long-lasting business value or introduces new risks to the business, with accumulated minor errors and derived huge losses.
In this article. we break down fundamental concepts to support a broader understanding of AI solutions, while focusing on two optimal architectural strategy for enterprise AI involves a strategic combination of Statistical AI and Symbolic AI, rather than a choice between them. And here’s how we’ll explore the topic:
- Why LLMs and other predictive models, when used in isolation, may not fully address enterprise requirements
- The distinct behaviors of probabilistic versus deterministic processing and their implications for enterprise systems
- Strategic considerations for leveraging Statistical AI versus Symbolic AI in solution design
- How to combine Symbolic AI with Statistical AI to create innovative and enterprise-ready solutions
The success of an enterprise intelligent solution is determined by its architectural design. Ultimately, it comes down to two how the underlying AI will make decisions. Enterprise AI beyond LLMs by combining Statistical and Symbolic AI
The Two Concepts You Must Know About How AI Works
Artificial Intelligence research is spread across multiple focus areas, exploring different methodologies and applications. And Given the context of the article, we can narrow down the conversation to two main branches, and break it down into fundamental behavior characteristics.
An intelligent system executes computational operations to transform inputs into meaningful outputs. Here’s the secret to understanding AI:
Symbolic AI (Deterministic): Operates based on explicit, pre-defined rules and logic. The same inputs consistently produce the same outputs, ensuring predictable and explainable behavior.
Statistical AI (Probabilistic): Learns patterns from large datasets and uses probability distributions to generate outcomes. Identical inputs might occasionally yield different results.
Predictive models opened new opportunities to create amazing new user experiences, but as we know that that is no single solution to match every possible problem, we must consider what each type of AI is designed for, and their trade-offs. With that, we can either choose the best one for each problem, or even better – combine them and amplify their unique powers.
The Power of Symbolic AI: Real-World Success
Running side by side with Statistical AI (e.g. Generative AI ), we have Symbolic AI with its deterministic reasoning approach. Symbolic AI is a well-established, proven way to enable intelligence in enterprise solutions, as demonstrated by the following real stories:
- Healthcare Billing Automation: Over 50,000 daily billing decisions automated in hours, significantly reducing manual workload previously requiring more than 100 specialists.
- Rapid Rule Changes: Business analysts update complex decision rules in mere hours (previously weeks), enabling instantaneous enhancements without costly retraining or downtime.
- Insurance Policy Processing: Nearly 20,000 rules automated, reducing policy processing from 18 minutes to under 2 minutes, substantially enhancing operational efficiency and customer satisfaction.
Architecting Intelligence: Combining Statistical and Symbolic AI
At Aletyx, we don’t see GenAI and symbolic logic as competitors—they’re complementary tools:
Generative AI
Core strengths: Pattern recognition, content generation, prediction from existing data
Symbolic AI
Core strengths: Logical inference, explicit knowledge representation, consistency
- Content generation
- Conversational AI
- Pattern recognition
- Sentiment analysis
- Exploratory analytics
- Personalization and recommendations
- Regulatory compliance
- Auditing
- Financial decision-making
- Insurance claim automation
- Healthcare policy validation
- Complex pricing structures
- Regulatory change management.
Neurosymbolic AI: Combines both for confidently predicting with the safety of rule-based guardrails
The combination of both GenAI and Symbolic AI, allows powerful applications of AI. To simplify the explanations, and given its native AI readiness, from now on, let’s use Drools as the reference implementation and technology for Symbolic AI. See some ways to approach this architectural combination:
- Separation of concerns: Analysis x Execution: GenAI analyzes complex data scenarios + Drools execution of final decisions based on explicit, auditable rules.
- Guardrail Validations: GenAI provides recommendations; Drools validates, consistently, these outcomes against pre-defined compliance and regulatory constraints.
- Confidence-based Fallback: GenAI handles common, high-confidence scenarios, and automatically escalates decisions to Drools. Drools can automate human involvement based on confidence the defined confidence thresholds.
Key Takeaways
We embrace AI, therefore, we shape experiences through technology that meets enterprise needs, with native AI feature that drives innovation. The key to delivering control, consistency, explainability and trust when using generative AI, is to maximize confidence and trust by bringing Symbolic AI to the scene.
Aletyx build of Drools is ready to connect with your favorite GenAI tool, for reliable compliance with business requirements. It allows you to deliver unique experiences infused with AI at every level.
Key takeways on Statistical and Symbolic AI:
| Statistical AI (Gen AI) | Symbolic AI (Drools) | |
|---|---|---|
| Foundation | Statistical models (probabilistic inference) | Explicit logical rules (symbolic AI) |
| Execution Predictability | Variable outcomes each run | Consistent, repeatable outcomes |
| Explainability | Opaque, requires external tools | Fully explainable and transparent logic |
| Compliance Suitability | Limited due to unpredictability | Highly suitable due to precise, rule-based logic. |
At Aletyx, we specialize in creating robust intelligent solutions, combining AI’s innovative potential with deterministic accuracy and compliance assurance.
Ready to confidently architect your intelligent future? Let’s talk!
