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
Emerj AI Framing Scale

Achieving Sustainable AI Adoption with the AI Framing Scale

How AI project leaders provide the best chance at sustainable success in AI adoption?

The answer is simple: Frankly communicate both near-term and long-term value, and help leadership understand the importance of seeing measurable results, and the value of building a stronger AI foundation for future projects.

Incubating AI Projects - The Crucial Phase Between Pilot and Deployment

Incubating AI Projects – The Crucial Phase Between Pilot and Deployment

Artificial intelligence projects are more like R&D than they are like traditional IT. It is experimentation as much as it is adoption, and this difference is one of many reasons that AI projects take longer to integrate, and often hit bottlenecks that prevent them from being used in production.

The B2B AI Growth Loop

The “Growth Loop” – A Deliberate Go-to-Market Approach for AI Firms

Most AI product firms are founded and grown in a similar way.

It usually goes something like this:

What Makes AI Projects Different from IT Projects

What Makes AI Projects Different from IT Projects

One of the biggest hurdles to AI adoption and integration is a lack of proper expectations about applying AI in an existing business. Executives and their teams often go into the process blind because so few companies have learned these important lessons and challenges and because even fewer have successfully adopted AI in a way that delivers ROI.

Document Search and Discovery in Banking - An Analysis of the Field

An Analysis of AI-Powered Document Search Capabilities in Banking

The financial services industry is buried in paperwork, and the NLP use-cases in banking and insurance grow every year.

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.

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