Unlocking Enterprise-Level Optimization with Causal AI: A Conversation with Parabole.ai's Manesh Murali

Originally Published March 2025, on ARCweb.com by Colin Masson

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. Their 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 their solutions.

Building on this familiarity, I recently had a compelling 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 Gen AI 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. Their 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 Gen AI, 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.), their 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 they work 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 elaborated on the limitations of traditional optimization approaches where departments often optimize their functions in silos. He explained how causal theory enables the connection of "multiple domains at the same time," allowing business leaders to understand the cascading impacts of decisions made in one area (e.g., procurement) on other critical KPIs like production, maintenance, and inventory. The goal is to achieve "Enterprise level" optimization where the "net resultant is always positive," a significant improvement over scenarios where localized optimizations could negatively affect downstream processes.

Strengths and Weaknesses of Causal AI and Gen AI

We then delved into the core difference between causal AI and other AI genres, particularly GenAI and classical AI/ML. Manesh clarified that while “Gen AI? is definitely a great candidate for text heavy AI 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 crucial distinction between causal AI and classical AI/ML lies in the focus: "in case of the conventional or classical AI/ML? the focus is on the correlation between two events, but what helps with Causal AI is it not only connects establishes the relationship, but it also helps you understand what causes the other". This ability to identify causation is paramount in solving complex enterprise problems.

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 overcomes the limitations of purely data-driven classical AI/ML by ingesting "subject matter expertise in the form of Six Sigma artifacts, interviews transcripts, a lot of? unstructured information around process manuals, procedures, policies, alongside data. This combination of data and "auxiliary knowledge" builds traceability and provides a complete path of which event impacted the other event".

Delivering Qualitative and Quantitative Benefits

Manesh summarized Parabole.ai value proposition through two lenses: "one is a quantitative benefit the other is a qualitative benefit. The qualitative benefit is purely the cognitive burden - how can you reduce the cognitive burden of an operator? From the quantitative side there's a very clear differential advantage that Causal AI is able to create in terms of cost saving, in terms of? speed to market, in terms of improving the productivity at a level thats never been seen before".

Key Insights and Takeaways

Our conversation with Manesh Murali underscored the growing importance and unique capabilities of Causal AI in the industrial landscape. While Gen AI has captured much of the recent attention, Causal AI offers a powerful approach to tackling complex operational challenges by providing explainable, auditable insights rooted in both data and human expertise, thats not typically available in general-purpose Foundation Models.?

The ability to move beyond correlation to understand true causation unlocks a new level of decision optimization with significant potential for ROI and operational efficiency. As Manesh aptly put it, causal AI helps enterprises understand "what caused what", enabling them to proactively address root causes and optimize outcomes across the entire value chain.

"With Causal AI, because you've got the root cause and the inter relationships, it solves the problem of explainability and auditability.” Colin Masson

“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." Manesh Murali

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.