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As Director of Research for Industrial AI at ARC Advisory Group, I've had the privilege of engaging with numerous pioneering companies at the forefront of artificial intelligence. My team and I have spent considerable time with Parabole.ai, a company that stands out for its deep understanding and evangelization of causal AI within the industrial sector. The company’s innovative approach to solving complex, multidisciplinary optimization challenges has consistently impressed me. In fact, Parabole.ai's technology was recently highlighted by Georgia Pacific during an ARC Industrial AI Leadership Summit I hosted in Houston, showcasing the real-world value and impact of its solutions.
Building on this familiarity, I recently had an engrossing conversation with Manesh Murali, Co-founder and COO of Parabole.ai, at the ARC Advisory Group's Leadership Forum 2025 in Orlando. Our discussion delved into the nuances of causal AI, its distinct advantages over other AI techniques like GenAI and classical machine learning, and the tangible benefits it's delivering to industrial enterprises.
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Watch the full interview with Manesh Murali to gain deeper insights into enterprise-level decision optimization using causal AI.
This blog post summarizes the key insights from our conversation, offering a roadmap for organizations seeking to unlock the full potential of their industrial data using causal AI.
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Key Insights from Our Conversation
Here's a summary of the key insights gleaned from our engaging discussion:
Causal AI for Enterprise-Wide Optimization: Parabole.ai positions itself as a decision optimization company leveraging causal theory to help customers tackle intricate, multidisciplinary challenges. The company’s vision is to move beyond siloed optimization to a holistic, enterprise-level approach where the impact of decisions across various KPIs and domains is understood and optimized simultaneously.
Beyond Correlation to Causation: A core differentiator of causal AI lies in its ability to not just identify correlations between events, like classical AI/ML, but to understand the underlying causal relationships. This provides a deeper level of insight, explaining why certain outcomes occur, which is crucial for effective problem-solving in complex industrial environments.
Addressing the "Black Box" Challenge: Unlike GenAI, which can often function as a "black box" with limited explainability, causal AI offers transparency and auditability. By understanding the root causes and interrelationships, subject matter experts can trust and utilize the insights generated, leading to greater adoption.
Harnessing Subject Matter Expertise: Parabole.ai emphasizes the critical role of incorporating subject matter expertise into the AI modeling process. By ingesting institutional knowledge alongside data (including Six Sigma artifacts, interviews, process manuals, etc.), the company’s approach builds more complete and traceable models that resonate with the experience of operators.
Tangible Value and ROI: Companies like Georgia Pacific are already realizing significant quantitative (cost savings, improved productivity, faster time to market) and qualitative (reduced cognitive burden on operators) benefits from deploying Parabole.ai's causal AI solutions.
Let's delve deeper into the key topics Manesh and I explored.
Solving Complex Multidisciplinary Optimization Using Causal Theory
Our conversation began with Manesh framing Parabole.ai as a "decision optimization company" whose "real vision is to help our customers solve very complex multidisciplinary optimization challenges using causal theory". He highlighted that the company works with customers across oil and gas, manufacturing, and other industrial sectors, focusing on optimizing decisions related to supply chain, asset reliability, employee health and safety, and production intelligence.
Manesh explained the drawbacks of traditional optimization methods, where departments operate in silos, optimizing their functions independently. He highlighted how causal theory connects "multiple domains at the same time," enabling business leaders to see the ripple effects of decisions in one area—such as procurement—on key performance indicators like production, maintenance, and inventory. The aim is to achieve "enterprise-level" optimization, ensuring that the overall impact remains positive, unlike localized optimizations that may inadvertently disrupt downstream processes.
Strengths and Weaknesses of Causal AI and GenAI
We then delved into the core difference between causal AI and other AI genres, particularly GenAI and classical AI/ML. Manesh clarified that while “GenAI is definitely a great candidate for text heavy UI problems to be solved," it can be "a little suboptimal" for "data intensive problems" where classical AI/ML and causal AI are more relevant. The key difference between causal AI and classical AI/ML lies in their focus: while conventional AI/ML identifies correlations between events, causal AI goes further by establishing relationships and determining the underlying causes. This ability to pinpoint causation is essential for solving complex enterprise challenges.
Causal AI Isn’t a Black Box
I brought up the "black box" nature often associated with GenAI and suggested that causal AI addresses the need for explainability and auditability. Manesh emphatically agreed, stating, "absolutely, absolutely." He further emphasized that "enterprises are run by subject matter experts, and unless you capture that subject matter expertise into your AI modeling process the model representation is never complete."
Causal AI Ingests Subject Matter Expertise
Causal AI addresses the limitations of purely data-driven classical AI/ML by incorporating subject matter expertise from sources such as Six Sigma artifacts, interview transcripts, and unstructured information like process manuals, procedures, and policies, alongside data. This integration of data and "auxiliary knowledge" enhances traceability and creates a comprehensive view of how events influence one another.
Delivering Qualitative and Quantitative Benefits
Manesh outlined Parabole.ai's value proposition from two perspectives: qualitative and quantitative benefits. The qualitative advantage lies in reducing the cognitive burden on operators, while the quantitative benefits include significant cost savings, faster time to market, and unprecedented improvements in productivity, showcasing the distinct impact of causal AI.
Key Insights and Takeaways
Our discussion with Manesh Murali highlighted the rising significance and distinct advantages of causal AI in the industrial sector. While GenAI has drawn considerable attention, causal AI stands out by delivering explainable, auditable insights that integrate both data and human expertise—capabilities not typically found in general-purpose foundation models.
The shift from correlation to true causation unlocks advanced decision optimization, offering substantial potential for ROI and operational efficiency. As Manesh put it, causal AI enables enterprises to understand "what caused what," allowing them to proactively address root causes and optimize outcomes across the value chain.
Quotes
Colin Masson: "With Causal AI, because you've got the root cause and the interrelationships, it solves the problem of explainability and auditability.”
Manesh Murali: “Our fundamental belief is that enterprises are run by subject matter experts, and unless you capture that subject matter expertise into your AI modeling process the model representation is never complete."
Engage with ARC Advisory Group
For ARC Advisory Group recommendations for navigating the AI Wars, closing the digital divide by embracing Industrial AI, assembling your Industrial-grade Data Fabric, and governing and guiding major decisions about enterprise, cloud, industrial edge, and AI software, please contact Colin Masson at [email protected] or set up a meeting with me, or my fellow Analysts at ARC Advisory Group.