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

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Energy & Utilities

Traditional, correlational, Machine Learning approaches are often not sufficiently trusted or capable enough to address some of the biggest challenges across  Marketing, Customer Journey, Exploration & Renewables

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 customer will churn or not but can’t recommend the optimal next best action to retain the customer

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From predicting to Influencing

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Do these questions sound familiar?

Energy & Utilities run on causal questions

Marketing Optimization
  • What are the causal drivers of campaign performance? How well did it actually perform?
  • How do I attribute customers to the right channels while accounting for confounders in the data?
  • What are the next best actions (interventions) to improve campaign performance?
  • What is the incremental impact of increasing allocation on a given channel?
  • Based on my budget, what is the optimal allocation of investment across channels (e.g.: media, shopper marketing, digital, and mail)?
Customer Journey
  • What are the best actions (interventions) to reduce churn at an individual or cohort level?
  • What is the right engagement strategy that balances customer experience and efficiency of payment plans
  • What is the optimal configuration of wind turbines that optimize energy output?
  • What is the right balance between selling & storing energy from solar farms?
  • What are the root causes of inefficiencies in energy output, and what are the optimal actions (interventions) to mitigate those?
  • What are the root causes of failures of equipment?
  • What is the optimal configuration (interventions) to maximize yield while reducing failures?

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

Causal AI at TotalEnergies

Watch the talk from the Causal AI Conference 2022

Case studies

Customer Case Study: Marketing Mix Modeling

A leading Mobile App company sees a projected 15x ROI through a reduction of 5% in annual marketing spend using decisionOS to optimise marketing allocation

Customer Case Study: Client Retention

North American pension plan improved beneficiary satisfaction and increased retention by 17% using decisionOS powered by Causal AI

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

Proven value in weeks

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    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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