Generative AI vs. Traditional AI: What’s Better?

Back to Basics: What is Artificial Intelligence?

Before delving into AI, let’s ensure we are aligned with our terminology. Analytics is generally categorized into four broad categories: descriptive, diagnostic, predictive, and prescriptive. Most organizations use a combination of analytic types to solve specific business problems and gain a competitive advantage. Businesses have a few basic types of questions, which have been mapped to each analytical category.

What is Generative AI?

Unlike traditional AI, which deals primarily with numeric data and occasionally small amounts of text, generative AI refers to a category of AI systems capable of generating completely novel content on their own. Traditional AI focuses on analyzing historical data and making future numeric predictions, while generative AI allows computers to produce brand-new outputs that are often indistinguishable from human-generated content.

Key Differences

Now, let’s discuss the critical differences between traditional AI and generative AI.

What Does It Do?

• Generative AI: Understands context and generates novel human-like content (e.g., text, code, music, audio, video, data, etc.)
• Traditional AI: Based on historical patterns in data, predicts outcomes for specific use cases (e.g., numeric predictions)

How is it Applied?

• Generative AI: Applies to various general use cases and applications (e.g., answer complex questions, create net-new images, audio, video)
• Traditional AI: Narrowly defined–use case specific (e.g., detect fraud, play chess, recognize an anomaly in an image)

What Data is Used to Train the Model?

• Generative AI: Data harvested from the internet
• Traditional AI: Carefully curated data for specific purposes

How is it Delivered?

• Generative AI: More human interfaces (e.g., chat interfaces via apps and web browsers
• Traditional AI: Specialized use case-specific applications (e.g., BI reports, dashboards, call center screens, etc.)

Who Can Use It?

• Generative AI: Anyone
• Traditional AI: Generally requires knowledge and specialized skills

Conclusion

As technology advances, traditional AI and generative AI will work together. Generative AI will be used to simplify and streamline human-machine interaction and to create applications using traditional AI. In other words, they will be used in tandem, rather than in isolation. Now, many of the articles I’ve read over the past few months essentially say that generative AI will help with just about everything.

For example, take a typical supply chain use case like demand forecasting. There are countless articles describing how generative AI can help with demand forecasting. Most of these articles claim that generative AI can help with data augmentation, scenario analysis, and more advanced pattern recognition. The one issue I would point out is that these can all be done today–without the assistance of generative AI. For data augmentation, do businesses think that generative AI will magically fix all of their data issues? If it’s not cataloged and is a mess, good luck. For scenario analysis and advanced pattern recognition, again, all in today’s toolkit.

What’s different with generative AI is the number of people that can help address these issues. Assuming organizations have findable, well-organized data, I can now ask the application to help create forecasts for specific periods and do scenario analysis. We can ask it to use ML algorithms for the forecast rather than statistical methods–changing it from a univariate to a multivariate model. But, when you ask these questions, it will write Python code to analyze the data and create output graphics–thus leveraging traditional AI.

Businesses planning to implement their generative AI projects should step back, take a breath, and think holistically about the business problem to be solved and work backward from that. What is the minimum viable data needed, how will your business process change, and how will it help us achieve our organizational goals? Remember, generative AI is not a panacea; it’s simply another tool in the toolbox.