Artificial intelligence is making enormous progress in enhancing efficiency in manufacturing contexts, leading to higher performance, thanks to recent innovations in cloud computing and ample data storage and analysis. AI also delivers crucial data to managers, allowing them to make better business decisions. Machine automation generates higher-quality products more quickly and effectively while also giving crucial data to assist managers in making better business decisions. However, there are still certain obstacles to overcome. Many businesses are wary about disclosing actual production and process information.

Wine quality is judged by an external group of fancy wine people who will determine your wine's future. If you get an Excellent quality rating, it's good for business. It's like Yelp reviews, mostly out of your hands but can make or break your business. So there is clearly a business benefit in trying to produce a wine that will get a good rating. While that seems self-evident – make good wine and people will like it – there is enough subjectivity in wine preferences that it makes this hard to do. I don't believe the producers of wine that are rated as a poor quality set out with that intent.

Have you just completed a boot camp or graduated with your degree in data science, computer science, machine learning, etc., so you're armed and ready to sling some code and build one of these models? Consider for a moment a different perspective, that of someone far up your leadership chain, the corporate executive. You may feel that they don't understand what you do. Because for most of them, these lists of what is trending in AI/ML and data science make them feel beaten downplaying buzz word bingo on a constantly changing board. Just when they were ramping up on machine learning, suddenly everyone is referring to AI, and they can't sort out exactly how the two are related, let alone what to do about it.

Have you just completed a boot camp or graduated with your degree in data science, computer science, machine learning, etc., so you're armed and ready to sling some code and build one of these models? Consider for a moment a different perspective, that of someone far up your leadership chain, the corporate executive. You may feel that they don't understand what you do. Because for most of them, these lists of what is trending in AI/ML and data science make them feel beaten downplaying buzz word bingo on a constantly changing board. Just when they were ramping up on machine learning, suddenly everyone is referring to AI, and they can't sort out exactly how the two are related, let alone what to do about it.

If only Arnold Schwarzenegger had toned down his terrifying Terminator, we might all be more willing to embrace the concept of Artificial Intelligence – because in real life, AI has plenty to offer your business and its bottom line. The applications are vast, and so are the potential savings. Brett Wiskar presented earlier this week at FoodPro 2017 about the extraordinary opportunities afforded by even just beginning to incorporate new technologies like AI. It's a question of where businesses see the opportunity to have value added by it," says Mr Wiskar. Some of the best ways to introduce AI into your operation relate to software modelling, using algorithms. "What you do is put in place a system that says'I'm going to measure these metrics about a carcass and I'm going to determine how to cut it up', or'I'm going to measure these metrics about my energy consumption and I'm going to determine how best to use my energy going forward', or'I'm going to use it to determine how best to order my raw product'materials'.

The future of business, it is argued, is digital. At the core of this digital transformation is the ability to harness data in enabling better business decisions. Typically, organizations have teams of experts who work on existing data sets to apply diverse analytic tools and techniques to make sense of the data. The more statistically advanced among these teams work on typical'data science' problems. Data science problems are where you need to apply sophisticated algorithms on large data sets to derive business relevant insights.

The future of business, it is argued, is digital. At the core of this digital transformation is the ability to harness data in enabling better business decisions. Typically, organizations have teams of experts who work on existing data sets to apply diverse analytic tools and techniques to make sense of the data. The more statistically advanced among these teams work on typical'data science' problems. Data science problems are where you need to apply sophisticated algorithms on large data sets to derive business relevant insights.