Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

Articles by Daniel

30 articles
AI at Verizon 950×540

AI at Verizon – Two Use-Cases

Verizon is the second-largest telecommunications company by revenue and the largest by market capitalization. The company is also the largest wireless provider in the United States with a reported 143 million subscriptions. In its 2021 annual  report, the company reported revenues of $126.3 billion. Verizon is traded on the NYSE with a market cap of approximately $194.5 billion. The company employs over 118,000.

AI at Alibaba 950×540

Artificial Intelligence at Alibaba – Two Current Use-Cases

Per Alibaba’s annual report, its revenue in 2021 exceeded 717 billion yuan (approximately 109 billion U.S. dollars), while its active yearly customers reached nearly 1.3 billion people. As of March 2022, Alibaba trades on the NYSE and has an approximate market cap of $225 billion.

Customer Engagement in Financial Services - Two AI Use-Cases 950x540

Customer Engagement in Financial Services – Two AI Use-Cases

This article was originally written as part of a PDF report sponsored by Daitan, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines.

Two NLP Use-Cases in Drug Discovery and Clinical Trials

Two NLP Use-Cases in Drug Discovery and Clinical Trials

This article was originally written as part of a PDF report sponsored by expert.ai, and was written, edited and published in alignment with our transparent Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

Making AI Projects Easier to Manage

Making AI Projects Easier to Manage – and More Like IT Projects

Increasingly, technology and business leaders look to AI project managers to make the execution (and success) of their AI projects more predictable. Executives and decision makers want AI projects to mature so they are more like the software development projects that have been with us for a generation. But, any AI project manager hoping to deliver on those expectations knows that success in AI projects requires an end-to-end thinking rarely found today.

Achieving ROI with Early AI Projects@2x

Achieving ROI with Early AI Projects – Advice from Leaders at Intel, Munich Re, Oracle, and More

In the enterprise world, more and more companies are crossing the chasm to test, and then deploy, their first AI solutions. To navigate this sometimes unfamiliar territory, enterprise leaders increasingly scrutinize the AI project selection process.

The Impending AI Ecosystem for Productivity Augmentation

Ambitious AI – The Impending AI Ecosystem for Productivity Augmentation

About AI Power: AI Power is an article series focused on the long-term consequences of AI, and how power is or will be influenced by AI technologies. Some previous AI Power articles - including "The SDGs of Strong AI" and "AI Ethics at War" have been popular over the years - but I've taken a hiatus from writing AI Power articles but suspect that I'll be creating more in 2022. I'm grateful to my friends for helping to put this first piece together - I hope you enjoy it. - Daniel Faggella

AI Culture Change in the Enterprise@2x

AI Culture Change in the Enterprise – Advice from Leaders at IBM, Facebook, SAP, and More

As far back as 2018 when we surveyed over forty banking industry leaders to discover the biggest issues with AI adoption, enterprise culture was already emerging as top of mind. Since then, we have found similar frustrations around enterprise culture in every industry. More than lack of data science talent, lack of appropriate culture serves as the largest and most consistent barrier to adoption.

Finding "Beachhead" AI Use-Cases - A Key to Gaining Traction for AI Product Firms

Finding “Beachhead” AI Use-Cases – A Key to Gaining Traction for AI Product Firms

Successful AI vendors know that 90% of the value they bring to the table lies in a deep understanding of the client's context, including:

AI Delivery Partners - How Consultants Contract AI Talent Before Hiring

AI Delivery Partners – How Consultants Contract AI Talent Before Hiring

Most early stage AI consulting firms don’t have the budget to hire expensive machine learning talent. For non-technical founders who can’t do the ML engineering themselves, this means getting creative when it comes to AI project delivery.

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