Agentic AI has become a powerful enterprise business tool with increasing capabilities, and it has never been easier to address business pain points by applying a large language model (LLM) to develop a plausible automated solution.
But is that solution as reliable as you need it to be, especially when the insights gleaned are used to inform critical business decisions?
LLMs are incredible resources that play a significant role in a business’s agentic AI infrastructure. But when they have to work across complex data sets, business policies, industry regulations, decision hierarchies, and multiple software systems, they do not inherently provide the enterprise-grade provenance needed to show which evidence, assumptions, constraints, and decision logic produced a recommendation. Additionally, when gathering inputs from a volume of data sources, the accuracy of their outputs can break down and potentially create hallucinations. If business decisions are made based on LLM outputs that can’t be explained or may be inaccurate, it can create significant consequences that impact product sales, revenue, compliance, or customers.
The problem is that the LLM is asked to carry too much of the enterprise reasoning without the harness and enterprise foundation around it.
A structured foundation for decision-making
While enterprise interest in agentic AI has grown substantially over the past few years, what works in concept hasn’t always proven results in the pilot phase and beyond. A common reason agentic AI pilots can fail in production is that an LLM is asked to carry too much of the enterprise reasoning without the support of an enterprise foundation. LLMs are powerful for some use cases, like demos, but enterprise decision-making will always require structure beyond a good prompt. While AI tools and platforms are becoming more commonplace and efficient at producing outputs, enterprise decisioning depends on plan versions, constraints, policies, approval logic, and financial guardrails. Additionally, enterprises will still need to rely significantly on the expertise, judgment, and reasoning capabilities of their workforces when implementing and scaling AI usage to ensure that outputs are trusted and can be applied in critical business decision-making.
LLMs will always play a critical role in building out an effective enterprise AI infrastructure, but the other critical component is the information, institutional knowledge, and context that is unique to how your business operates and makes decisions. Neurosymbolic AI strengthens enterprise AI by combining the pattern recognition and language capabilities of neural AI with an explicit symbolic representation of the enterprise, including its entities, relationships, plans, policies, constraints, decision logic, and context. Unstructured information such as emails, meeting notes, spreadsheets, contracts, and policy documents can contribute to that model, with the goal to give AI a structured enterprise foundation against which it can reason.
Neurosymbolic AI is a framework that combines the core strengths of symbolic AI, which represents explicit enterprise structure, relationships, rules, constraints, and decision logic, with neural AI, which powers capabilities such as language understanding, pattern recognition, and synthesis. This framework builds upon the domain expertise and rules of symbolic AI and the capability of neural AI to find and learn from the complex patterns and relationships found in vast amounts of data. A straightforward conceptual model for describing this architecture is 10-80-10. The first 10% is neural, as the LLM helps interpret the user’s intent and translate it into a structured enterprise question. The middle 80% is where the enterprise foundation carries the reasoning that the LLM should not be asked to improvise. The system resolves enterprise context and can invoke plans, policies, constraints, decision rights, forecasting, causal analysis, simulation, optimization, rules, and solvers to evaluate scenarios and trade-offs. The final 10% is neural again, as the LLM explains the result in clear business language and makes the answer accessible for business decision-making.
As a result, neurosymbolic AI can make sense of large volumes of data at scale and recall complex patterns, while also incorporating structured reasoning and contextual knowledge that is unique to an organization. This grounds outcomes in reality and significantly reduces the chance of hallucinations.
Understanding the business question in context
A crucial component to incorporating neurosymbolic AI into a business’s larger AI strategy is a structured enterprise and decision-model foundation that can connect data, knowledge, plans, policies, constraints, and decision context. For example, the o9 Digital Brain’s Enterprise Knowledge Graph provides a structured and continuously updated semantic foundation that organizes
[…]
Content was trimmed to protect the source. Please visit the original article for the full text.
Read the original article:
How neurosymbolic AI can transform enterprise decision-making