AI Articles and Analysis about Enterprise resource planning

We spoke with Jonathan Ross, CEO and founder of Groq, an AI hardware company, about software defined compute. This interview is part of a series we did in collaboration with Kisaco Research for the AI Hardware Summit happening in Mountain View, California on September 17 and 18. 

We set out to create a report that would be particularly useful for executives and business leaders who are looking to get started with an AI initiative, as well as IT and management consultants who want to competently and effectively guide their clients through AI adoption.

Although there are established use-cases for AI applications in the business world, the claims that AI vendors make about returns from their software are often exaggerated. What is also not apparent amongst the AI hype is that adopting AI and machine learning is far more challenging than it might seem.

This week we speak with David Carmona, General Manager of AI at Microsoft. Carmona discusses how redefining a business process is a very different kind of AI adoption project than working on something that is horizontal.

Investment in AI by banks and financial institutions for risk-related functions such as fraud and cybersecurity, compliance, and financing and loans has grown dramatically in the last half-decade compared to customer-facing functions.

AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.

There's an entire artificial intelligence ecosystem for enterprise search. Most of this is in a purely digital world. Most vendors help with a layer of AI-enabled search that understands terms or phrases and is able to return the results or answers to questions that someone types in. But the problem is compounded when it comes to searching the physical world.

AI has numerous use cases in legal, from document search to compliance and contract abstraction. This week, we speak with Lars Mahler, Chief Science Officer for LegalSifter, about what's possible with AI for legal departments today and how AI applications for legal teams, such as natural language processing-based contract analysis, work. In addition, Mahler discusses how lawyers at companies and data scientists work together to train machine learning algorithms.