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.
AI as a field, especially in the context of real-world applications, has been progressing at a rapid pace. This has been further accelerated by the onset of the COVID-19 pandemic. In fact, AI was found to be the most discussed technology in 2021. Having worked with numerous clients, big and small, in the integration of …
Explore 4 real-world AI in manufacturing examples. From defect detection to predictive maintenance.
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 …
Most text classification examples that you see on the Web or in books focus on demonstrating techniques. This will help you build a pseudo usable prototype. If you want to take your classifier to the next level and use it within a product or service workflow, then there are things you need to do from …
Text Classification: Best Practices for Real World Applications Read More »
Companies receive support inquiries from various channels. This may include emails, support tickets, tweets, chat conversations with customer support representatives (CSRs), chatbot conversations, and more. This is a lot of data that you are dealing with and it’s mostly unstructured and scattered in nature, making it that much harder to manage. All this text data …
5 Ways to Improve Productivity in Customer Support with AI Read More »