These articles are meant to educate senior leaders, product managers, and innovation managers. Topics include use cases of AI, best practices, and strategies for the successful implementation of AI.
Rushing through AI integration can result in unintended failures. Learn 3 AI integration mistakes leaders can easily avoid to limit confusion, reduce waste, and truly reap the benefits from AI.
In this article, you’ll learn why it’s sometimes wiser to start without AI, even if your goal is to integrate AI into products, services, and workflows. You’ll also learn tips for handlings AI non-readiness issues.
Having worked with numerous clients, big and small in the integration of AI, here are 4 Business AI predictions in 2022 and beyond.
Explore 4 real-world AI in manufacturing examples. From defect detection to predictive maintenance in beverage manufacturing, silicone wafer production and automotive production.
Learn 5 strategies for generating high-quality machine learning training data. Warning: some of these approaches have been tried and tested.
Buying AI solutions requires the right strategy. In this article, we explore 3 mistakes to avoid when buying AI.
Did you know, that several years ago, NLP was heavily an academic discipline? Today, natural language processing or NLP has become critical to business applications. This can partly be attributed to the growth of big data, consisting heavily of unstructured text data. The need for intelligent techniques to make sense of all this text-heavy data …
7 Natural Language Processing Applications for Business Problems Read More »
Have you seen machine learning solutions fall flat in practice? Well, I have. Several times. I get occasional panic calls from teams about their 98% accurate models generating questionable predictions once released to actual users. Did they build a bad model? Maybe. But the real issue is that the majority of these teams skipped a step. And that step …
How to Prevent Machine Learning Models from Failing in Practice? Read More »
AI is transforming nearly every industry, and text analysis is a key area of interest. That’s because there’s been an explosion in unstructured text data—nearly 80% of data at most organizations—which is quickly becoming impractical to analyze by humans alone. We’ve already talked about some best practices for building a text classifier, but how can …
Big data describes the volumes of data that your company generates, every single day. Both structured and unstructured. Analysts at Gartner estimate that more than 80 percent of enterprise data is unstructured. Meaning, they can be text files from IT logs, emails from customer support, direct Twitter messages from customers, and employee complaints to your HR department. This type of …