Making Decisions in AI: Beyond Predictions

Predictions are TRASH, Decisions are KING

Artificial Intelligence (AI) has become a prominent field, and many organizations are eager to harness its potential. However, there is a crucial aspect that often gets overlooked: the decision-making process. After more than 10 years of working in AI, I firmly believe that instead of focusing solely on predictions, we should prioritize how AI can facilitate better decision-making. In this blog post, I will explore the significance of decisions over predictions and provide insights on aligning the two effectively.


Why Are Predictions Alone Not Enough?


When we read or hear about AI in the news, predictions are often emphasized. We come across claims that AI can detect emotions, identify criminals, or even predict job promotions. While these predictions may seem intriguing, we must question their practical value. The same holds when clients approach us with requests solely for predictions. We ask them, “What will you do with those predictions?”


The Power of Decisions in AI

At NILG.AI, we firmly believe that predictions without impactful decisions are meaningless. We must shift our focus from isolated predictions to a holistic approach that aligns AI with decision-making processes. That’s why our AI Case Canvas revolves around observing opportunities, defining visions, and using predictions to support better decisions that transform businesses.

AI use case canvas

Aligning Predictions with Decisions: The Key to Success


To understand the importance of aligning predictions and decisions, let’s explore a few real-world examples where misalignment led to project failures.


Example 1: Customer Support and Demand Forecasting

In one project, a client requested a model to predict whether a customer would contact their customer support the following day. However, they intended to use this prediction to forecast demand. The initial approach involved running millions of individual predictions daily, leading to inefficiencies and high development costs. Instead, a more practical approach would have focused on forecasting overall demand, saving time and resources.


Example 2: Product Returns and Stock Management

Another project aimed to address frequent errors in fulfilling client orders. The client initially focused on predicting individual product returns. However, their primary concern was ensuring sufficient stock availability to meet customer demands. By realigning the focus to forecast demand for each product, they could optimize stock management, minimize delays, and prevent losses.


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The Importance of Alignment and Granularity

These examples highlight the significance of aligning predictions with the level of decisions being made. It is crucial to avoid pursuing predictions that are too granular or unrelated to the overarching goals of the business. By ensuring alignment and considering the appropriate level of granularity, organizations can make the most of AI technologies and achieve their desired outcomes.


Learn More About Our Methodology

If you’re interested in exploring our methodology in detail, we invite you to check out the free preview of our Data Ignite course. It provides a structured approach to understanding opportunities, defining visions, and integrating predictions to successfully make informed decisions and transform businesses.

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Data Ignite

Dive deeper into how AI use cases can be identified and developed.

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While predictions may capture attention and imagination, they alone do not drive meaningful change. To leverage the full potential of AI, we must shift our focus from predictions to decisions. By aligning predictions with the level of decisions required, organizations can unlock the true value of AI, save time and resources, and drive transformational growth. Let’s embrace the power of decisions in the age of artificial intelligence.

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