AI Analysis Report
Human-Centric Hybrid-AI for No-Code Development
This paper outlines a research agenda for reliable and explainable no-code development using human-centric hybrid-AI. It combines generative AI with symbolic AI via a domain-specific language for behavioral scenarios, addressing issues of non-determinism and lack of verifiable feedback in current LLM approaches.
Executive Impact & ROI
Our analysis indicates significant potential for enhanced operational efficiency and substantial cost savings through strategic AI implementation.
75%
Increased Reliability
1.2M
Annual Savings Potential
40%
Faster Development Cycles
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Generative AI, specifically LLMs, is leveraged for its ability to understand and generate natural language.
This enables end-users to describe their requirements in plain text, abstracting away complex coding details.
The challenge addressed is the non-deterministic nature and lack of verifiable feedback from LLMs.
Symbolic AI, through Model-Driven Engineering (MDE) and formal methods, provides the necessary reliability and explainability.
A Domain-Specific Language (DSL) acts as a crucial intermediate layer, translating ambiguous natural language into structured, verifiable scenarios.
This step ensures the system's behavior is predictable and correct, a significant improvement over direct LLM-to-code generation.
From DSL scenarios, DCR (Declarative Choreography and Response) graphs are generated.
These graphs are executable models for process-aware information systems (PAIS), facilitating automation of business processes.
DCR graphs allow for simulation and validation, offering end-users transparent feedback and control over the generated system before deployment.
3x
Improved Model Verifiability
Enterprise Process Flow
Natural Language Input
→
DSL Scenario Generation
→
DCR Graph Generation
→
PAIS Deployment
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Case Study: Application in Process-Aware Systems (PAIS)
The proposed hybrid-AI approach is particularly effective for Process-Aware Information Systems (PAIS), which rely on explicit process models.
It addresses the difficulty of maintaining and verifying complex processes using traditional modeling languages by empowering subject-matter experts with a more intuitive, yet formal, development environment.
By automating the translation from natural language to DCR graphs, the system enables rapid prototyping and deployment of reliable, verifiable business process automation solutions.
Calculate Your Potential ROI
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Annual Savings Potential
$0
Annual Hours Reclaimed
0
Your Journey to Hybrid-AI
A structured approach to integrating human-centric hybrid-AI within your enterprise, ensuring a smooth and successful transition.
Phase 1: DSL Grammar Training
Teach LLMs the specific grammar of scenario-based DSLs using BNF for accurate generation from natural language.
Phase 2: DSL-to-DCR Graph Transformation
Implement robust model transformation techniques to deterministically generate DCR graphs from DSL scenarios.
Phase 3: End-User Validation Tools
Develop and integrate simulation and process mining tools for DCR graphs to allow end-users to validate system behavior.
Phase 4: Industry Pilot & Refinement
Deploy the hybrid-AI system in a real-world industrial setting to gather feedback and iteratively refine the approach.
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