Extracting Value from AI: Turning Potential into Real Impact

In our previous post, we uncovered the true essence of AI, from Data Engineering and Modeling to the iterative process of Tuning. However, what happens after the AI models are built and trained? How do you actually extract value from them?

For businesses, the real benefit of AI does not come from having a working model; it comes from operationalizing that model and integrating it into the business. This is where AI starts driving real-world outcomes—whether cost savings, increased efficiency, or new revenue streams.

Let’s explore the key elements of how to turn AI into business value.

1. Operation

Once your AI model is ready, the first step is operationalizing it. This means embedding AI into day-to-day business operations. For instance, an AI model predicting customer demand needs to be seamlessly integrated into your supply chain management system to guide purchasing decisions. AI only brings value when aligned with your organization's workflows.

2. Deployment

Next is deployment—the process of launching your AI model into a live environment. This is a critical step in which the AI begins working on real data in real-time scenarios. However, deployment is not just about going live; it is about ensuring the model performs well under real-world conditions and integrates smoothly with existing systems.

3. Monitoring

Deploying AI is not a one-time event. Once in production, you have to monitor it continuously to ensure that it behaves in the way you want it to. The models naturally deteriorate with time due to changes in the business scenario or data patterns. Monitoring helps a lot in the early detection of these changes, ensuring that the model remains accurate and useful.

4. Scaling

After a successful deployment, the next challenge is scaling. This involves expanding AI’s usage across different parts of the business or scaling the model to handle larger volumes of data and more complex scenarios. For instance, if you have deployed AI for customer segmentation in one region, you might scale it to cover global markets, increasing its overall impact.

5. Optimization

Even after deployment and scaling, AI models require ongoing optimization. This involves fine-tuning the model to improve its performance, reduce computational costs, or better align with evolving business needs. Optimization ensures that AI continues to deliver the highest value possible.

6. ROI (Return on Investment)

The ultimate measure of AI success is ROI. AI models should deliver tangible business benefits—whether through cost savings, enhanced decision-making, increased revenue, or operational efficiencies. Measuring ROI helps businesses determine whether their AI investments are generating real value and provides a clear case for further AI adoption.

7. User Adoption

Finally, none of the above matters if your team is not using the AI effectively. User adoption is key to realizing value from AI. This includes training employees to understand and trust the model’s outputs, integrating it into their workflows, and fostering a culture of data-driven decision-making. Without proper adoption, even the best AI models can end up underutilized.

What is Next: Understanding AI's Constraints

While AI has the potential to drive immense value, it is important to understand that it comes with limitations and constraints. In our next post, we will explore the constraints of AI in business, including challenges like data limitations, ethical considerations, and security issues. Understanding these constraints will help you avoid common pitfalls and set more realistic expectations for your AI initiatives.

Stay tuned for a balanced view of the strengths and limitations of AI!

(Authors: Suzana, Anjoum, at InfoSet)