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Causal AI & LLM synergies: Enterprise decision making needs more than chatbots

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Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the most crucial questions across process engineering, quality control, supply chain, procurement, complexity management, customer experience as well as centralised planning & strategy

Traditional machine learning approaches often fail to address critical business questions

Correlation, not causation

Spurious correlations lead to bad decisions


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How can AI discover cause and effect

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They struggle to answer why

And are often perceived as “black boxes”


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Explainable AI (XAI) doesn’t explain enough

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Prediction, not next best action

For example, they can predict if a machine will break down but can’t recommend the next best action to prevent the problem

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From predicting to Influencing

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Do these questions sound familiar?

Manufacturers run on causal questions

Process Engineering & Quality Control
  • What are the root causes of process inefficiencies or defects?
  • What are the best actions (interventions) to mitigate those inefficiencies and/or defects?
  • What are the best actions (interventions) to optimize throughput while keeping energy costs, defects ratio, quality, & maintenance costs under control?
Supply Chain resilience
  • What are the root causes of poor OTIF levels?
  • What are the best actions (interventions) to improve OTIF levels given my budget constraints?
Procurement & Complexity Management
  • What are the root causes of delays?
  • How do more product configurations impact our profitability and delays? What is the optimal balance between product options, profitability, & delays?
  • What is the optimal choice of vendors that balance quality & delays?
Customer Experience
  • What are the root causes of churn and/or returns? 
  • Which customer support tickets need to be prioritized?
  • What are the optimal discount levels that balance margins and customer acquisition?
Centralized planning & strategy
  • What investments will help me achieve my sales and profit targets while improving customer experience?
  • How will the competitive, market, and macro dynamics impact my PnL?
  • How will decisions in pricing, marketing or supply chain impact the rest of the business? (e.g will pricing decisions disrupt the supply chain?)

Leverage decisionOS

the first operating system for decision making powered by Causal AI, to address all those causal questions

Causal AI

To move beyond traditional ML and into a world where you can provide actionable recommendations by leveraging state of the Causal AI tools and methods.

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DecisionApp Building

Seamlessly surface recommendations to your business partners as expressive, tailored and interactive applications focused on decision-making.

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For the deployment and monitoring of decision workflow, trusting those workflows in production and measuring the causal impact of your decision-making.

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Trusted by leading organisations

Case studies

Customer Case Study: Manufacturing Optimization

A global manufacturer sees 10x ROI from enhancing their manufacturing processes by comprehending the underlying reasons behind failures in their production lines

Customer Case Study: Inventory Optimization

A leading manufacturer of IT products and equipment sees $19mn in savings from matching inventory levels to customer demand more accurately

Customer Case Study: Early Fault Detection

$50bn Global Electronics Company chooses causaLens to revolutionize its early warning system for faulty parts with Causal AI

Proven value in weeks

  • 1 Icon
    Internal meeting

    One hour

  • 2 Icon
    Scoping sessions

    Two to three hours

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    Proof of Concept

    Three to four weeks

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    Twelve months

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