How Business Rules Systems Empower AI-Assisted Decision Making
In today’s competitive business landscape, organisations are continuously seeking ways to enhance back-office productivity. One effective strategy is integrating Business Rules Systems (BRS) with Artificial Intelligence (AI) to fine-tune decision-making processes. This combination not only amplifies efficiency but also ensures more accurate and consistent outcomes compared to systems lacking business rules for decision-making.
Key Areas of Impact
1. Case Flow Routing with and without Regular Expressions
Business Rules Systems enable sophisticated case flow routing by utilizing rules against structured data and regular expressions to analyze unstructured data, such as email content. This allows organizations to:
- Identify Specific Customers or Intent: By parsing emails for keywords or patterns, the system can detect a customer’s identity or intent, enabling precise routing to the appropriate department or agent.
- Fine-Grained Routing: Detailed analysis ensures cases are directed to the most suitable handlers, reducing response times and improving customer satisfaction.
- Enhance the AI analysis of content when no clear confidence in AI decision: In some scenarios AI may not identify the case data confidently where Business rules are more definitive.
2. Adjusting Case Priorities Using Workflow Metadata
By examining case workflow metadata, Business Rules Systems can dynamically adjust case priorities. For example:
- Time-Based Escalation: If a case remains unresolved for more than two days, the system can automatically increase its priority to expedite resolution.
- Complexity and Value Assessment: Cases deemed highly complex or of high value can be flagged and assigned to specialized teams or even individuals with the necessary expertise.
3. Finer-Grained Assignment to Specialists
Business Rules enable the assignment of cases to specific individuals or small teams based on predefined criteria:
- Expertise Matching: Assigning cases to agents with specialized knowledge relevant to the specific case.
- Resource Optimization: Balancing workloads among agents to maximize efficiency and prevent bottlenecks.
Essential Business Objects in Case Management
Efficient case management relies on the interplay of several key business objects:
- The Case Itself: The specific issue or service request requiring attention.
- Underlying Business Object: The subject matter of the case, such as a customer account or product.
- Service/Process: The procedures and activities or workflows the case undergoes.
- Agent(s) Handling the Case: The individuals or teams responsible for resolving the case.
E.G. Multi-Step Insurance Claim
Consider a multi-step insurance claim process:
- Claim Object (Case): The insurance claim filed by the customer.
- Policy Object (Business Object): Details of the customer’s insurance policy.
- Process Object: The specific claim process, such as processing a car crash claim.
- Agent Object: The insurance adjuster or claims specialist managing the case.
Advanced systems like OPX (Operational Process Excellence) can also incorporate additional objects from existing back-end systems. This integration allows business rules to leverage thousands of attributes for even more granular decision-making data that was not available in the case service request.
AI vs. Rules Engines: Probabilistic Decisions and Deterministic Decisions
Understanding the distinction between AI-driven decisions (probabilistic decisions) and rules engine decisions (deterministic decisions) is essential for optimizing back-office operations.
Probabilistic Decisions (AI)
- Adaptability: AI systems use machine learning algorithms to make probabilistic predictions based on data patterns. They excel in handling ambiguous or complex scenarios where rigid rules may not suffice.
- Data-Driven Insights: AI can process vast amounts of data to uncover trends and insights that inform decision-making.
Deterministic Decisions (Rules Engines)
- Transparency: Rules engines apply explicit, predefined rules, offering consistency and making it far easier to understand and justify decisions.
- Control: Organizations can explicitly define decision-making processes, ensuring compliance and adherence to policies.
Academic Perspectives
Research in artificial intelligence and business process management highlights the pros and cons of both approaches:
- Pros of AI Decisions:
- Learning Capability: AI systems improve over time as they learn from new data.
- Complex Pattern Recognition: They can identify intricate patterns not immediately apparent to human analysts.
- Cons of AI Decisions:
- Lack of Transparency: AI decisions can be opaque, making it difficult to trace the reasoning behind outcomes.
- Data Quality Dependency: The effectiveness of AI is highly dependent on the quality and quantity of data available.
- Pros of Rules Engines:
- Consistency: They provide uniform decision-making based on established rules.
- Ease of Auditing: Decisions can be easily audited and explained due to their rule-based nature.
- Cons of Rules Engines:
- Inflexibility: They may not adapt well to new or unforeseen situations without manual rule updates.
- Maintenance Overhead: Keeping rules up to date with a fast changing business environment requires some ongoing effort.
Combining AI with rules-based systems can leverage the strengths of both approaches. This hybrid model allows for flexibility and adaptability while maintaining transparency and control over decision-making processes.
OPX: Bridging the Gap
Solutions like OPX (Operational Excellence) from Corporate Modelling enable organizations to harness both AI and Business Rules Systems:
- Hybrid Decision-Making: OPX integrates AI’s adaptability with the transparency of rules engines, providing a robust framework for complex decision-making processes.
- Scalability: Capable of handling numerous attributes and data points from various back-end systems, OPX enhances decision precision.
- Efficiency: The combined approach accelerates case resolution times and improves overall back-office productivity.
Conclusion
Integrating Business Rules Systems with AI in back-office operations offers significant benefits in productivity and efficiency. By leveraging both soft and hard decision-making processes, organizations can achieve more accurate, transparent, and adaptable workflows. Advanced solutions like OPX exemplify how this integration can be effectively implemented, providing a competitive edge in today’s dynamic business environment.