In a global environment where uncertainty seems to be increasing every day, organizations need to sift through myriad signals to find a route toward long-term viability and growth. And they need a platform that does it responsibly.
AI can be the answer, but it isn’t the AI that you might first think of. Generative AI (genAI) has the current spotlight as a productivity tool, but it has its limits, including a propensity to hallucinate when asked to cite sources or make accurate predictions.
That’s where neurosymbolic AI (NSAI) comes in. It fuses statistical AI (pattern recognition) with symbolic AI (logic, rules and causal structures), driving powerful pattern recognition to deliver predictions and decisions that are practical, actionable and grounded in real-world outcomes. It can simulate real-time market scenarios and their potential impact by combining and analysing millions of points of both proprietary and public data, structured and unstructured, to produce a roadmap for growth.
While less known than genAI, NSAI has the potential to become the platform that sees around corners, helping leaders spot opportunities that others cannot see. These can include identifying growth opportunities for a multinational corporation, discovering ways to combine molecules for new methods to treat diseases, or uncovering hidden patterns in climate data that inform more resilient infrastructure planning. It can help governments anticipate emerging threats by synthesizing structured intelligence with unstructured field reports. In financial services, it can detect subtle anomalies in transaction networks that signal fraud or systemic risk.
NSAI also operates without the potentially catastrophic hallucinations common in other AI systems, and its decision-making process is completely transparent and auditable, so you can trace outputs back to the underlying logic and sources.
NSAI is not going to replace genAI. But there are many cases where genAI can get an organization part of the way to its goal – and then NSAI gets it over the finish line. Some of its key benefits include:
- Mathematical precision: NSAI can understand mathematical rules and logic rather than simply recognizing patterns, enabling it to handle complex mathematical tasks, including probabilistic modelling and optimization.
- Unified data logic: Its understanding of rules and logical structures allows it to integrate both structured and unstructured data.
- Causal reasoning: NSAI’s mathematical proficiency, data fluency and simulation ability enable it to understand what drives growth, not just what correlates.
- Transparency: Thanks to its ability to explain the rules and relationships it applies to reach its conclusions, NSAI can provide fully auditable decisions that are critical for regulatory, board and operational needs.
- Ethical grounding: NSAI can be provided with legal and moral frameworks with which it must comply and then explain how its outputs meetsuch guidelines, mitigating the possibility of undesirable – or unlawful – recommendations.
NSAI thrives on complexity, uncovering deep causal relationships across many dimensions, going beyond what traditional analyses can achieve. Its ability to handle intricate data structures allows it to reveal insights that other models might miss.
For example, imagine analysing product performance by correlating variables like location granularity, competitor types and varying product requirements across different customer segments. NSAI integrates all these factors, finding meaningful patterns even in highly complex, evolving scenarios. All told, NSAI enables smarter, more actionable decisions in environments where complexity is the norm.
This is more than just abstract potential. We have seen the benefits in many industries:
- Financial services: NSAI enhances underwriting, claims processing and compliance, ensuring that decisions meet regulatory standards.
- Consumer products:NSAI drives hyper-personalized experiences at a one-to-one level.
- Industrial products:NSAI helps industrial conglomerates optimize the entire value chain, such as by understanding the impact of tariffs in terms of hours or days instead of months to help inform strategic decisions.
The real-world benefits are not limited to business, though. Another area of interest is the ability of NSAI to accelerate the discovery of treatments for rare and underserved medical conditions by combining deep learning with symbolic reasoning to generate explainable predictions. Academic researchers are developing neurosymbolic prototypes to identify new uses for existing drugs, where traditional pharmaceutical investment is limited due to low commercial incentives. These models not only suggest promising drug candidates, but also provide transparent, testable explanations for their predictions. This helps demonstrate how NSAI can reduce drug development costs, shorten timelines and expand access to life-saving therapies.
NSAI isn’t some future technology. For leaders willing to seize this moment, NSAI offers a strategy, a lens, and a platform for transformation. The opportunity is nothing less than redefining how an enterprise tackles its most intractable challenges, and in doing so, shapes the world in which we live.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.