Causal AI

Predictive insights are not helpful for customer retention unless combined with data-driven prescriptive recommendations for where to engage, what to offer, and how to message. Retention decisionApp Overview of the end product presented to business stakeholders Overview of the causal ai workflow Summary Traditional, correlations-based, machine learning approaches often fail to make an impact on…

70% of pricing and promotion investments deliver a negative return. There is a clear need for retailers and CPGs to improve pricing and promotion resource allocation. Read how Causal AI can fill in the gaps where Traditional AI approaches often fall short. Summary Traditional, correlational-based AI approaches often fail to make an impact when allocating…

Summary; Root Cause Analysis Optimzing the overall equipment effectiveness (OEE) levels within plants is key to overcome the challenging economics of manufacturing. To do this, fast root cause analysis of faults is required (whether in the line equipment or the end product). This is a great problem for AI to address, but traditional AI approaches…

Introduction Elisa is absent-mindedly glancing through the Research Spotlights column of her favorite eco-blog when a graph grabs her attention: A study showing daily levels of ozone and daily numbers of deaths in London. The graph reveals an interesting pattern: the peaks in ozone levels coincide with drops in human deaths, right when summer comes. Does this mean that…

Generative AI (GenAI), and more specifically, large language models (LLMs), are revolutionizing business processes across various industries, promising significant advancements in efficiency, innovation, and customer engagement. However, understanding GenAI’s capabilities and limitations is essential for leveraging its full potential while avoiding common pitfalls. The Allure and Risks of GenAI GenAI offers transformative potential, enabling businesses…

Determine the actions required to achieve your desired outcome while respecting your business constraints. Beyond predicting. Act to change outcomes. Our Decision Optimization packages determine the actions required to achieve your desired outcome, while respecting your business constraints. It allows you to generate actionable recommendations to solve your business challenges: The ability to generate accurate…

Causal AI enables a complete root cause analysis of your business to provide organizations and their leaders with a clear understanding of what causes delays and recommends actions to improve on-time and in-full service levels. Find and fix root causes rapidly. As the name suggests, causal methods are perfectly placed to enable you to identify…

Unlock interventions and counterfactuals to level up your decision making. CausalNet is an advanced structural causal model (SCM) allowing you to push beyond the limitations of traditional machine learning algorithms. CausalNet gives you: Interventions and counterfactuals out of the box to immediately allow you to generate actionablerecommendations to drive real outcomes. Trusted and transparent results enabling you to…

Executive Summary For Data Leaders, deciding whether to build or buy Causal AI tooling will have a significant impact on your decision-making capabilities. As AI-driven decisions are being demanded across the enterprise for business critical use cases, minimising time-to-value is essential. With decisionOS by causaLens, you can create solutions which optimise business KPIs in days…

Capturing cause-effect relationships in enterprise data is an essential step to trusted decision making – but it’s complex and requires the best of both algorithmic and human knowledge approaches. decisionOS includes tools for seamless synthesis of both approaches allowing users to create their own personalized workflows powered by the most complete library of algorithmic causal…