How to Use AI in Business to Make Better Decisions

Business has entered a new era where artificial intelligence (AI) is the new necessity. But what exactly is AI and how are businesses actually leveraging AI to make better decisions?

Simply stated, AI is the application of programming machines to be aware of their environment so that they react to circumstances and stimuli the way a human would. It is a machine mimicry of human behaviors. Although the concept of AI has been around for decades, there were two important limitations for using artificial intelligence in business. One, there wasn’t enough data to make AI practical and economical for business. And two, there wasn’t enough computing power. But that changed with the emergence of mind-boggling big data and equally impressive computational power.

Today’s big data and parallel computing infrastructures have eliminated the data and computing power constraints for using AI in business. This has given data scientists unprecedented freedom to use much more sophisticated models.

Some of these models mimic the processing within the human brain, such as deep learning networks that contain layers of neurons, and recurrent neural networks. Deep learning is just one class of learning algorithms that has become popular recently because it mimics how the human brain learns and achieves some pretty amazing results in image and speech processing.

Not All AI is Created Equal

For really smart AI, data scientists use machine learning algorithms to turn data into models. This is the essence of machine learning, and it’s often referred to as training and tuning the models. The goal is to produce results that closely match those obtained by human experts. Because performance data of these AI can be collected and fed back into the machine learning engine, it can update and improve the model through usage. It is this constant stream of feedback data that allows AI to learn over time. This continuous learning loop—where AI learns from every experience—is what makes AI smart. Machine learning with feedback data will update and improve prescriptive analytics so the next prescribed decision is optimized even further to bring it closer to, or better than, what human experts would do.

This is how the AlphaGo computer program beat the world champion at the game of Go. AlphaGo didn’t learn from the world champion; it learned from average professional Go players. Once, AlphaGo became good enough, it could then start playing the game against itself—perhaps even millions of games in a day—learning all the mistakes quickly to get smarter with every single game. So even though AlphaGo wasn’t programmed with the level of intelligence or strategies that could beat the world champion, because it got better with every game, it eventually surpassed even the world champion.

Applying Revenue-Boosting Intelligence

Imagine incorporating this level of intelligence into your everyday pricing processes. Employing AI for pricing has become a reality for many organizations already. However, because AI is a disruptor, businesses choosing to leverage AI must change and adapt to the disruption it creates before they can fully realize its benefits. More importantly, there’s one crucial behavior change that must happen before the pricing industry can leverage the full potential of AI in business.

Ready to learn more? Join me for How the Pricing Industry Can Realize the Benefit of AI webinar on February 7, 2019, 12:00pm EST.   

About the Author

Michael Wu

Dr. Michael Wu is one of the world’s premier authorities on artificial intelligence (AI), machine learning (ML), data science, and behavioral economics. He’s the Chief AI Strategist at PROS (NYSE: PRO), an AI-powered SaaS provider that helps companies monetize more efficiently in the digital economy. He’s been appointed as a Senior Research Fellow at the Ecole des Ponts Business School for his work in Data Science. Prior to PROS, Michael was the Chief Scientist at Lithium for a decade, where he focuses on developing predictive and prescriptive algorithms to extract insights from social media big data. His research spans many areas, including customer experience, CRM, online influence, gamification, digital transformation, AI, etc. His R&D won him the recognition as an Influential Leader by CRM Magazine along with Mark Zuckerberg, Marc Benioff and other industry giants. Michael has served as a DOE fellow at the Los Alamos National Lab conducting research in face recognition and was awarded 4 years of full fellowship under the Computational Science Graduate Fellowship. Prior to industry, Michael received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology; and his Ph.D. from UC Berkeley’s Biophysics program, where he uses machine learning to model visual processing within the human brain. Michael believes in knowledge dissemination, and speaks internationally at universities, conferences, and enterprises. His insights have inspired many global enterprises and are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books.

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