AI Articles and Analysis about Data management

We spoke with David Carmona, the GM of Artificial Intelligence at Microsoft about his approach to AI ROI with the enterprise clients he works with at Microsoft. The biggest takeaway from this episode comes right at the beginning. David talks about how to think about artificial intelligence ROI in the long-term and the near-term.

The financial services sector has been one of the early adopters of data science and AI technologies. That said, financial firms that have engaged in AI projects will have realized that they require a deep understanding of data management and skilled data science professionals to solve these complex problems.

Digitally-native eCommerce businesses are used to working with their customer data in order to write copy for marketing campaigns, run PPC ads, calculate customer lifetime value, and make decisions based on core metrics within CRM dashboards.

Banks are in one of the best positions for leveraging AI in the coming years because the largest banks have massive volumes of historical data on customers and transactions that can be fed into machine learning algorithms. We recently completed our Emerj AI in Banking Vendor Scorecard and Capability Map in which we explored which AI capabilities banks were taking advantage of the most and which they might be able to leverage in the future.

Large volumes of data, managed properly, are a boon for many industries, including the military. It would not be possible to mount effective military operations without knowing the when, where, and what in deploying resources. Military big data, therefore, helps defense leaders make better decisions, provided it is not “dark data.”

In this second installment of the "Pitfalls to AI Adoption in the Enterprise" series, we're going to talk about underestimating the integration needs of artificial intelligence and machine learning.

I hope that by the end of this episode of the AI in Industry podcast, you'll not only be able to hire better data scientists who will be a fit for your business problems and build better data science teams, but also pick the AI applications and use cases that you should bring into your business versus those that you shouldn't.

In the last year, interest in so-called “autoML” has risen greatly in part due to its promise of bringing artificial intelligence to businesses that have been blocked from accessing it due to its serious time, talent, and budget requirements. Although machine learning may still be widely unavailable to small businesses, medium-sized businesses may find that autoML allows them to make use of it in the coming years.