In this post we cover the shortcomings of conventional AI/ML, how causal ML can fix these shortcomings, and how you can apply causal ML to real-world business problems.
Standard AI/ML is designed for predicting/forecasting outcomes. Causal ML can also predict outcomes, but it can also shape them by telling you the best actions to take. This is not just about insights - dashboards are dead - it's about telling you the actual real-world actions to take to achieve a desired outcome. Causal ML has a holistic understanding of your system, whereas standard AI treats all the parts as independent. Causal understands the underlying process that drives outcomes. This is a requirement for data-driven decision-making, optimization of a system, or prediction in unstable or safety critical environments.
Traditional AI/ML does well at prediction, but only if the future looks like the past. It struggles with prediction under any unseen scenarios since it is designed to recognize patterns from historical data. This is a problem in changing environments. Causal ML goes further. It understands the processes that generate the data, enabling it to piece together new distributions and discover innovative solutions that go beyond the historical data it was trained on.This has two implications. First, causal ML deals better with unforeseen situations. Second, it can optimize to go beyond human ability rather than being restricted to mimic it.
CausaDB is a cloud-based causal AI platform that lowers the barrier to adopting and deploying causal AI systems. CausaDB is designed to eliminate infrastructure overheads, provide a simple developer experience, and get causal AI from the lab to the real world. But what exactly does causal AI look like in the real world?
In today's competitive market, the ability to predict product demand accurately is crucial for retailers. Traditional AI models, often limited by their inability to understand dynamic relationships can fall short in areas like demand forecasting. By leveraging causal AI, CausaDB offers a more sophisticated approach to supply chain modelling, allowing retailers to optimise their inventory and resource allocation effectively using AI algorithms that understand cause and effect.
For instance, a retailer using CausaDB to predict the demand for a seasonal product can now account for the complex interplay between the time of year and purchasing influences. By establishing causal relationships in the model, CausaDB enables accurate predictions of product demand, considering both seasonal influences and other drivers of product demand. This approach is not only more accurate but also more adaptable to varying conditions than traditional models.
The impact of adopting such an approach on retails businesses can be significant. Accurate demand predictions can lead to a reduction in overstock or stockouts, which can translate into substantial cost savings and increased revenue. Research by ROI hunter indicated that the average retailer loses over $4m a year due to overstocking, a figure that grows even greater for large online retailers. Overstocking could be greatly reduced or even eliminated by careful application of AI, but existing approaches fail to achieve this because they lack an understanding of cause and effect. By integrating CausaDB into their supply chain management, retailers can not only enhance operational efficiency but also drive considerable financial benefits.
In the competitive retail industry, predicting customer intent and optimising discount strategies are crucial for maximising profitability. Traditional AI methods often fall short in accurately predicting customer behaviour due to their reliance on correlation rather than causation. CausaDB addresses this by using causal AI to untangle the true impact of viewing and buying behaviours on purchase decisions, leading to more effective discount strategies.
Implementing CausaDB streamlines the process of understanding complex customer interactions. Its efficient setup and training, coupled with cloud-based deployment, allow for rapid integration into existing systems. By accurately identifying which customers are more likely to respond to discounts, CausaDB helps businesses target their marketing efforts more effectively, reducing unnecessary expenditure and enhancing conversion rates.
The adoption of CausaDB can lead to significant return on investment. For instance, a 10% reduction in ineffective discounts on a $500,000 marketing budget can save $50,000 annually. Additionally, improving conversion rates by just 5% on annual revenue of $10 million can yield an extra $500,000 in sales. Integrating CausaDB into sales strategies not only improves decision-making but also drives considerable cost savings and revenue generation.
In the evolving world of personalised digital healthcare, accurately recommending lifestyle changes based on individual health data is vital. Traditional AI methods often struggle, offering generic or misaligned advice due to their lack of causal understanding. CausaDB, leveraging causal AI, navigates these complexities by accurately determining the impact of specific factors on health outcomes. An example of this would be nutritional apps predicting the impact of diet on health metrics such as a user's BMI.
Setting up and integrating CausaDB is straightforward, allowing healthcare providers and wellness apps to easily model complex causal relationships. In the example above, CausaDB identifies the nuanced effects of dietary saturated fats on weight and BMI. By training the model with existing user data, CausaDB can provide tailored dietary recommendations that are trustworthy and auditable. This enhances the precision of health advice given to users and helps software to achieve regulatory approval.
The practical impact of CausaDB in a healthcare setting is substantial. For a health app with 10,000 active users, even a 5% increase in user engagement through personalised advice can significantly enhance user retention and app value. If each user contributes an average of $10 per month, this increase translates to an additional $50,000 in monthly revenue. More importantly, by providing accurate health recommendations, CausaDB aids in moving towards healthier lifestyles, potentially reducing long-term healthcare costs and improving overall public health.
In this post we've explored what causal AI is, and how it can help real world businesses to make the most of their data and avoid the pitfalls of standard AI approaches. The use cases covered in this article only scratch the surface of what causal AI can do. At Causa we believe that as businesses see the potential of causal AI, it will become a key piece of technology in modern business workflows. We have built CausaDB to help organisations integrate causal AI into their own workflows and applications without the expense, time, and expertise required to build and manage a solution in-house. If you're interested in what CausaDB can do for you, reach out to book a demo today.