AI Articles and Analysis about Business intelligence and analytics

Episode Summary: In the last few months, we've had a string of fantastic interviews with investors and have gained a cross-industry picture of what's important for start-ups and emerging trends in the AI and ML space. This week's interview is no exception. Ann Miura-Ko, co-founder and partner at Floodgate, starts with an explanation of the "self-driving enterprise" concept, her functioning idea about AI investing and the future of software in general. Her high-level insights embody an interesting emphasis on the dynamic of human-machine interactions and relationships cross industries, including the constant workflows and interactions of people using software and bolstering the predictive and prescriptive analytics capabilities of that software. While forward-thinking, Miura-Ko also paints a picture of how these synergistic relationships between humans and machines are happening with companies today.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time.

Episode Summary: One of the most clear insights from our recent consensus on machine learning in marketing was that companies who have more digital touch points along the path to conversion—and more conversion in general—have an advantage when applying AI and ML technologies. In this week's episode, Scopely Co-Founder Ankur Bulsara shines a light on this dynamic and describes how gaming companies are taking advantage of digital trails and applying machine learning technologies. We don't cover much gaming on the Emerj podcast, so this interview is a bit off the beaten path. Bulsara speaks about how dialed-in and instrumented the mobile gaming environment is and how data is used to leverage higher conversions over time, as well as how Scopely's systems are set in place to ensure success of their business model. We think his insights on how gaming companies leverage higher conversions with (and without) machine learning can serve as an analogy for companies in other industries that are considering how to set in place similar, optimal digital processes over time.

In a global market that makes room for more competitors by the day, some companies are turning to AI and machine learning to try to gain an edge. Supply chain and inventory management is a domain that has missed some of the media limelight, but one where industry leaders have been hard at work developing new AI and machine learning technologies over the past decade.

Deep Learning plays a vital role in the early detection of cancer. A study published by NVIDIA showed that deep learning drops error rate for breast cancer diagnoses by 85%. This was the inspiration for Co-Founders Jeet Raut and Peter Njenga when they created AI imaging medical platform Behold.ai. Raut’s mother was told that she no longer had breast cancer, a diagnosis that turned out to be false and that could have cost her life.

Episode Summary: At Emerj, we like to look around the corner at where AI is impacting industries and how people can make better business decisions based on that information. AI and data-driven software for enterprise is an emerging topic of interest, and in this episode we get a venture capitalist's perspective on where AI will play a vital and necessary role with real results in software and industry.

When businesses make investments in new technologies, they usually do so with the intention of  creating value for customers and stakeholders and making smart long-term investments. This is not always an easy thing to do when implementing cutting-edge technologies like artificial intelligence (AI) and machine learning. Business intelligence case studies that show how these technologies have been leveraged with results are still scarce, and many companies wonder where to apply machine learning first (a question at the core of one of Emerj's most recent expert consensuses.)
Artificial intelligence and machine learning have certainly increased in capability over the past few years. Predictive analytics can help glean meaningful business insights using both sensor-based and structured data, as well as unstructured data, like unlabeled text and video, for mining customer sentiment. In the last few years, a shift toward "cognitive cloud" analytics has also increased data access, allowing for advances in real-time learning and reduced company costs. This recent shift has made an array of advanced analytics and AI-powered business intelligence services more accessible to mid-sized and small companies.
In this article, we provide five case studies that illustrate how AI and machine learning technologies are being used across industries to help drive more intelligent business decisions. While not meant to be exhaustive, the examples offer a taste for how real companies are reaping real benefits from technologies like advanced analytics and intelligent image recognition.

Episode Summary: As it turns out, survival of the fittest applies as much to algorithms as it does to amoebas, at least when we're talking about genetic algorithms. While we've explored other types of machine learning algorithms in business in past articles, genetic algorithms are newer territory. We recently interviewed Dr. Jay Perret, CTO of Aria Networks, a company that uses genetic algorithm-based technology for solving some of industry's toughest problems, from optimization of business networks to pinpointing genetic patterns that are correlated with specific diseases.