Making Money with Mediocre AI Models

A Guide for Business Stakeholders using ML Models

In the world of AI, it’s easy to assume that only the most accurate models can bring value to your business. However, this is far from the truth. In fact, even mediocre models can be transformed into money-making machines with the right strategies. In this article, we’ll explore three real-life examples of how we turned poor-performing models into profitable assets, and share three strategies to help you do the same.

Can a Mediocre Model Really Boost My Business?

Yes, it can! Even if your model is only slightly better than random, it can still bring value. Let’s look at an example. We once built a model to predict if a certain product was going to be sold in the next week. The model’s overall predictive performance was mediocre at best, achieving only 60% balanced accuracy. However, the model showed high predictive performance when we looked at the top 10% and bottom 10% predictions. These “tails” were already optimizing millions of dollars. So, despite its overall poor performance, the model was moved to production and generated a positive cash flow.

What if My Model is Only Slightly Better Than Random?

In the healthcare domain, we had a model with 80% ROC AUC, which is reasonable but not great. However, when we looked at the extremes (cases where the model was quite sure a patient had a disease or was healthy), the predictive performance was over 90%. This allowed us to prioritize patients in severe conditions and save resources, optimizing a lot of value despite the model’s overall mediocre performance.

Can a RecSys That Fails for Newcomers Still Be Profitable?

Absolutely! We once built a recommendation system that was good at understanding the behaviors and purchase trends of recurring customers, but terrible for newcomers. Instead of trying to improve the model for the entire population, we decided to profit from the segment of recurring customers. This approach allowed us to generate additional revenue and produce a cash flow to keep working on solving the larger case.

Do you want to further discuss this idea?

Book a meeting with Kelwin Fernandes

Meet Kelwin Learn More

How Can I Transform My Mediocre Model into a Money-Making Machine?

Here are three strategies:

Strategy 1: Look at the tails

If your model isn’t as good as you’d like, look at the top and bottom cases as we have shown in this blog post. This can help you optimize value.

Strategy 2: Retarget your model

If your model is good at predicting a certain segment, retarget your model to that segment. This can bring good performance and cash flow to fund the project’s long-term vision.

Strategy 3: Ignore technical KPIs and focus on business KPIs

Instead of focusing on accuracy, precision, etc., focus on how much money you’re producing, how many customers you’re saving, or how many errors you’re preventing. This will help you see the value your model is already bringing.

Course, Templates

Data Ignite

Learn how to move from technical aspects of your AI models into a successful business implementation.

Learn More

In conclusion, don’t be discouraged if your AI model isn’t perfect. Even mediocre models can bring significant value to your business with the right strategies. At NILG.AI, we’re here to help you navigate the world of AI and make the most of your models, no matter their performance.

Like this story?

Subscribe to Our Newsletter

Special offers, latest news and quality content in your inbox.

Signup single post

Consent(Required)
This field is for validation purposes and should be left unchanged.

Recommended Articles

Article
Transform Your Business with Intelligent Process Automation

Demystifying Intelligent Process Automation: Beyond Basic Automation Intelligent Process Automation (IPA) is so much more than just putting repetitive tasks on autopilot. Think of it as a whole new way businesses are approaching process improvement. We’re not just talking about simple rule-based automation anymore; we’re talking systems that learn and adapt as they go. That […]

Read More
Article
Overcoming Digital Transformation Challenges: Expert Tips

Beyond Anarchy: Climbing the Digital Transformation Ladder Ready to move past digital chaos and embrace data-driven decisions? This list tackles 8 key digital transformation challenges, guiding you from basic SOPs to advanced AI. We’ll cover hurdles like legacy system integration, cultural resistance, security concerns, talent shortages, and budget constraints. Learn how to define your digital […]

Read More
Article
8 Analytic Data Solutions Powering Businesses in 2025

Unlocking the Power of Data: A 2025 Perspective In 2025, data is the key to smart decisions. This listicle spotlights eight leading analytic data solutions—Tableau, Microsoft Power BI, Google BigQuery, Amazon Redshift, Snowflake, Apache Spark, SAS Analytics, and Databricks—to help your business thrive. We'll show you how these platforms transform raw data into actionable insights, […]

Read More