Decision Intelligence Academy

Expert AI systems often referred to as expert or knowledge-based systems, leverage codified domain knowledge and rule-based inference engines to simulate human expertise in decision support (Feigenbaum, 1977). An expert system incorporates expert knowledge that has been coded into facts, rules, heuristics and procedures through a knowledge acquisition process conducted by knowledge engineers. Expert knowledge about a task domain is stored in a knowledge base where it is possible to add new knowledge or refine the existing knowledge without recompiling the inference engine, which is a rule-based system that responds to user queries by executing a set of rules. Generally, these systems are passive decision support systems in that their outputs are typically responses to queries. To improve traceability, some of these systems are enhanced with explanation facilities to support explainability.

Expert AI systems are designed to emulate the problem-solving and decision-making capabilities of human experts within specific domains. OpenAI CEO Sam Altman predicts that artificial intelligence (AI) could surpass expert skill levels in most fields within a decade. AI could be smarter than "experts" in 10 years, OpenAI CEO says - CBS News, while recent research demonstrates that large language models already surpass human experts in predicting scientific outcomes in complex fields like neuroscience. Unlike generative AI that creates content, Expert AI systems leverage standardized knowledge bases comprising facts, rules, and behavioral patterns to analyze situations, assess risk, and provide explainable recommendations.

References:

"The art of artificial intelligence: Themes and case studies of knowledge engineering. Proceedings of the Fifth International Joint Conference on Artificial Intelligence", Feigenbaum, E. A., 1977

“AI could be smarter than "experts" in 10 years, OpenAI CEO says”, CBS News, 23 May 2023, https://www.cbsnews.com/news/ai-smarter-than-experts-in-10-years-openai-ceo/

“Large language models surpass human experts in predicting neuroscience results”, Arxiv, 28 Nov 2024, https://arxiv.org/html/2403.03230v4

“Large language models surpass human experts in predicting neuroscience results”, Nature Human Behavior, 27 November 2024, https://www.nature.com/articles/s41562-024-02046-9

Expert AI systems provide organizations with the ability to scale specialized knowledge beyond individual experts, creating sustainable competitive advantages. The systems emulate human expertise within specific domains and enable teams to make consistent, high-quality decisions even when experts are unavailable. By adding expert knowledge into structured rules and behavioral patterns, organizations can leverage scarce expertise resources and turn them into scalable assets.

Expert AI systems deliver measurable operational advantages:

• Knowledge Amplification and Distribution - Expert systems complete tasks faster than human experts with lower error rates. This makes specialized expertise accessible across teams and eliminates bottlenecks.
• Consistent and Auditable Decision-Making - Systems provide reliable, explainable recommendations free from human biases, fatigue, or emotional influence. This ensures that decision quality remains consistent.
• Operational Efficiency and Cost Reduction - Automated analysis of routine expert-level tasks frees human specialists to focus on complex edge cases and strategic challenges. This helps reduce operational costs through minimized errors and faster processing times.
• Institutional Knowledge Preservation - Organizations protect valuable expertise by using expert systems. This ensures that specialized knowledge remains available even when key personnel move or change roles.
• Scalable Expertise Across the Enterprise - Organizations can deploy expert-level decision support simultaneously across multiple locations, teams.
• Enhanced Profitability Through Better Decisions - Improved decision accuracy, reduced errors, and faster resolution of complex problems directly contribute to better business outcomes and competitive advantage.

Expert AI enables organizations to shift from managing risks as they occur to proactively mitigating them before they escalate. By embedding a subject expert directly into analytical systems, companies gain several critical capabilities:

• Proactive Risk Identification – Detect emerging threats early by continuously monitoring for behavioral patterns that signal potential risks before they materialize into costly incidents
• Consistent Assessment Standards – Apply uniform risk evaluation criteria across the enterprise while maintaining the flexibility to adapt quickly to evolving threat landscapes
• Transparent Decision Support – Understand the "why" behind every risk assessment, providing the explainability critical for regulatory compliance and management discussions.
• Scalable Risk Intelligence – Extend your organization's expertise across all operations without the costs and constraints of proportional staffing increases
• Efficient Resource Allocation – Focus analyst attention on the highest-priority risks by automatically filtering noise and surfacing what truly demands executive attention

Expert AI systems leverage your organization's most valuable asset—human expertise. Rather than learning patterns from data alone, these systems allow subject matter experts to directly encode their knowledge into executable models. The AI applies proven expertise to analyze real-world situations and ensures the insights reflect a validated understanding rather than statistical correlations alone. The result is faster, more reliable decision-making that scales your experts' knowledge across the enterprise.

Cogynt's Expert AI HCEP (Hierarchical Complex Event Processing) leverages human expertise as its knowledge base through a no-code modeling approach. Subject matter experts and risk analysts use their expert knowledge to create behavioral analytic models without writing code—simply using drag-and-drop authoring tools. This means the knowledge base is the collective expertise of analysts who understand behavioral patterns, operational risks, and priorities specific to their companies' needs. The system then applies expert-defined knowledge to analyze streaming data in real-time to produce results that support high-consequence decision making in complex environments.

Expert Systems contribute to decision intelligence by providing a structured and automated way to incorporate domain-specific knowledge and rules into the analysis and decision-making process. They can be used to standardize best practices, identify potential risk indicators or opportunities based on predefined guidelines, and offer recommendations based on expert-level reasoning. When businesses integrate these systems, they can enhance the consistency and quality of their decisions and augment human analysts' capabilities by automating the application of complex rules set-up to emulate expertise. The systems can free up analysts' time allowing them to focus on more strategic and critical aspects of decision making.

Forrester Research: Generative AI Investment:

A May 2024 Forrester survey revealed that "67% of AI decision-makers plan to increase investment in generative AI within the next year." Given Forrester's view that "it's decision intelligence that gives AI its direction," this increased investment in AI directly translates to a greater need for and adoption of Decision Intelligence.

Enterprises will increasingly deploy Expert Systems AI will augment other AI capabilities to better automate complex, rule-based decision-making processes, enhancing operational efficiencies and consistency across various business functions. Future applications will include complex risk assessment for financial services, intelligent forecasting and planning, streamlined compliance monitoring for regulatory requirements, and sophisticated supply chain optimization for inventory management. By standardizing expert knowledge and institutional expertise, companies can ensure uniform application of business rules, reduce operational costs, and preserve critical knowledge from retiring subject matter experts. These systems will enable effective and consistent decision-making capabilities representing a force multiplier within organizations.

By deploying Expert System AI, enterprises will achieve substantial improvements in profit margins, customer satisfaction, and competitive positioning. Companies will leverage these systems to scale expert-level decision-making across operations, reduce human error in critical processes, and free up highly skilled employees. Expert AI will create measurable business value through enhanced accuracy, reduced processing times, and the ability to handle exponentially larger volumes of complex decisions without increases in staffing costs.