Predictive AI is transforming industries in ways no one could have predicted. Some of the changes might as well have come from the pages of cult-classic sci-fi novels.
From recommendation engines to fraud detection, predictive AI has many applications. The technology leverages machine learning algorithms and historical data to predict likely outcomes. It assesses trends and patterns in data sets to forecast future results. While it’s not perfect, it is effective.
So, predictive AI has applications in everything from retail — predicting customer behavior — to fleet maintenance software — recommending when certain maintenance should be done. It’s fairly common in manufacturing, healthcare, marketing, and finance. If it hasn’t established a stronghold in your sector yet, it’s likely only a matter of time.
One source says the worldwide predictive AI segment could reach approximately $108 billion by 2033. That’s up from $14.9 billion in 2023, according to market.us. The compound annual growth rate from 2023 to 2033, says the research firm, is 21.9%.
According to market.us, Google AI’s move to invest $100 billion in responsible AI initiatives involving cybersecurity, healthcare, and climate change underscores the sector’s growing prominence and potential.
The question for your company is: Are you ready for predictive AI? Whether you are or not, it’s here, and the odds are it will continue to advance and grow as its influence is felt globally.
If you’re new to this technology or want to learn more, here are five things to know about predictive AI.
1. How Predictive AI Works
Many observers believe that predictive AI works in mysterious ways, but it relies on data collection, processing, and machine learning to generate future outcomes.
Specifically, it depends on data gathering, data processing, model training, prediction generation, and continuous learning. All these steps are essential, but the continuous learning aspect deserves more attention. Predictive AI should, because of continuous learning, get progressively better accuracy by refining models and using fresh data.
2. Machine Learning is the Backbone of Predictive AI
Another thing you should know about predictive AI is that its backbone is machine learning. That’s not an overstatement since predictive AI depends on machine learning techniques that enable the system to find patterns and make informed guesses.
Common machine learning algorithms upon which predictive AI draws from include neural networks, regression analysis, and deep learning models.
3. Predictive Learning Has Real-World Applications
It’s clear that predictive AI has real-world applications, so it’s not the sort of technology that’s more sizzle than steak. It’s used in various sectors to give decision-makers the tools they need to make better decisions for their businesses and customers. Here are some common applications:
- Retail: Personalizing client recommendations and optimizing inventory.
- Healthcare: Predicting ailment outbreaks and patient health outcomes.
- Marketing: Forecasting consumer behavior and campaign performance.
- Finance: Finding fraudulent activity and stock market trends.
- Manufacturing: Predicting maintenance to safeguard against equipment failures.
4. Data Quality Matters
Predictive AI needs quality data to produce reliable results that companies can count on. If the information upon which predictive AI draws is wrong, outdated, or biased, that will negatively impact its predictions.
5. Bias and Ethical Considerations
Bias, as has been mentioned, can negatively impact predictive AI. One example of this is hiring. If companies use predictive AI to help find the best candidates for open positions, they should ensure the technology isn’t making recommendations based on data sets including historical biases in hiring. One way to counter the possible bias is to roll out fairness algorithms to reduce bias. Whether you’re ready or not, predictive AI is here. And it’s likely here to stay, given the impact it has had and continues to have across many industries. Your best bet is to learn how this technology can help your business and leverage these benefits to get ahead
Alexia is the author at Research Snipers covering all technology news including Google, Apple, Android, Xiaomi, Huawei, Samsung News, and More.
