The AI Advantage

This is the beginning of the continuum of AI. These include several capabilities like dashboards and data visualizations, advanced or interactive reporting, and robotic process automation. There's a lot that can be learned and unlocked from automation and analytics — especially when you align it to your organizational decision-making needs.

At its heart, data science is math. These math models help decompose and solve business problems using common math tools, formulas and algorithms, to predict behaviors. These can be market behaviors in the form of demand forecasting to reduce risk and maximize opportunities or analyzing a company’s website and in its retail stores to have the right product in stock and offer customized promotions. It can also be the behavior of assets in a manufacturing plant, predicting equipment failures and maintenance needs to reduce service downtime and operational costs.

ML is also based on a set of math problems. A lot of the times they answer the same business questions but the big difference between data science and ML is that the data science approach is more transparent. The downside however is that it requires more handholding — supervised learning as opposed to unsupervised learning.

GenAI is what’s dominating the AI conversation right now, hitting the market in tools like ChatGPT, Bard, Midjourney and many others. What differentiates GenAI from the AI that has come before it is that it creates new content. Primarily because it’s run by specific models that are trained on incredibly massive data sets. It’s generating new content by reconstructing existing information or images that it has already seen before.

It’s exactly what you think of in sci-fi/dystopian future movies, where the AI can think and reason like humans (and in film, usually does take over the world). But you’ll notice there's an asterisk on AGI in the graphics because, luckily, this technology doesn't exist yet. So it’s one less thing you need a rapid deployment strategy for this year!