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
Three Ways to Leverage Industry Expertise for an AI Career

Three Ways to Leverage Industry Expertise for an AI Career – A Guide for Non-Technical Leaders

As artificial intelligence makes its way into more industries and workflows, more and more non-technical team members will be charged with leading AI projects. The next wave of AI catalysts will be familiar with AI at a conceptual level (read: executive AI fluency), but will mostly be expert in bridging AI's capabilities to important business workflows and objectives.

Five Non-Technical AI Business Models 950x540

Five Non-Technical AI Services Business Models

When most professionals think about “AI consulting” they tend to think about technical machine learning services, like: Building our data infrastructure, crafting and testing new algorithms, interesting AI systems into existing IT infrastructure.

Bridging Business Needs and Data Assets

Bridging Business Needs and Data Assets – Emerj AI Leader Insight

In the vast land of opportunities that AI creates, how do we select the projects that will generate ROI? Do we gain inspiration from reading AI use-cases relevant to our industries? Do we search through our own lists of existing priorities and hope the applications for AI will become clear?

Getting Past the IT Barrier to AI Adoption

Getting Past the IT Barrier to AI Adoption – Strategies of the Most Successful Vendors

AI adoption involves more than educating stakeholder groups (SMEs, IT, leadership) on the technical nuances of AI. It involves navigating human motives and incentives.

Going from Pilot to Deployment with AI

Going from Pilot to Deployment with AI – 4 Factors to Consider

While overt AI "flops" are less common than they were three years ago, the pattern of failure for AI projects is still much the same.

Near-Term Value vs. AI Transformation - Emerj’s AI Project Quadrant

Near-Term Value vs. AI Transformation – Emerj’s AI Project Quadrant

You can invest in AI maturity and future capability - or you can have "quick wins" with surface-level AI applications that have relatively short-term, narrow ROI.

Selecting Early AI Projects with Emerj's Bullseye Model

Enterprise AI Project Selection – Emerj’s “Bullseye” Model

Picking first AI projects in challenging - and leadership is right to be wary of making the wrong investment. The challenge lies in both (a) identifying the right projects, and (b) ranking and determining the right ones.

Building Your AI Product Development Roadmap - Recommendations for Startups and Enterprise Leaders

Building Your AI Product Development Roadmap – Recommendations for Startups and Enterprise Leaders (Part 3 of 3)

This article is the third in a series part in a series about AI product development.

In the first installment in this series, we covered how to develop AI product ideas with both near-term adopt-ability and long-term potential.

Ranking AI Product or Service Ideas - Determine the Best Product to Build

Ranking AI Product or Service Ideas – Determine the Best Product to Build (Part 2 of 3)

So you've decided you want to take an AI product or service to market.

Before you sell anything - you'll have to decide what kind of product or service to develop.

AI Product Development_ Winning in the Near-Term and Long-Term

Developing AI Products: Winning in the Near-Term and Long-Term (Part 1 of 3)

Whether you're a startup or an enterprise, developing AI products is challenging.

Not only do you have to wrestle with the challenges of finding a use-case that where AI can actually deliver value into an enterprise workflow, but you also have UI concerns, and - often - much higher demands to monitor algorithmic drift and other technical issues.

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