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
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.

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

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.

The Role of thought Leadership in Marketing AI Products and Services

The Role of Thought Leadership in Marketing AI Products and Services

In this article, I'll explore some of our lessons learned in getting the value of AI products or services to stick with enterprise buyers.

Finding the Enterprise Fit for AI

Finding the Enterprise Fit for AI – Emerj AI Leader Insight

In the classic business book Good to Great, author Jim Collins talks about the different approaches for technology adoption between high-performing and average companies. Collins' research indicated that high performers tend to adopt technology as an accelerant to an existing, working strategy - while underperformers tended to adopt technology in an attempt to jumpstart a change in direction or strategy that they haven't yet undertaken.

The Range of AI Capabilities in Document Search and Discovery

The Range of AI Capabilities in Document Search and Discovery

Over the last three years of AI Opportunity Landscape research, we've examined many broad capabilities across the AI ecosystem, from computer vision to conversational interfaces to anomaly detection and beyond. Some of our earliest client research work focused on back-office automation - mostly in financial services and healthcare - and it brought us face-to-face with an array of vendors, use-cases, and opportunities for applying AI for document search and discovery.

AI Knowledge Retention in the Enterprise - Making the Most of Lessons Learned 950x540

AI Knowledge Retention in the Enterprise – Making the Most of Lessons Learned

Novice AI project leaders measure projects entirely by (unrealistic) near-term financial benchmarks.

How to Build an Enterprise AI Roadmap

How to Build an Enterprise AI Roadmap – A Four-Step Process

The firms that will gain a genuine advantage from AI deploy the technology in a way that achieves short-term ROI, alignment to a long-term vision, and conscious development of AI maturity - including skills, data infrastructure, and more.

The 7 Steps of the Data Science Lifecycle

The 7 Steps of the Data Science Lifecycle – Applying AI in Business

AI is not IT- and adopting artificial intelligence is almost nothing like adopting traditional software solutions.

The 3 Phases of Enterprise AI Deployment

The 3 Phases of Enterprise AI Deployment

Making AI work has a lot to do with "getting things right" even before a project starts, including:

Developing the Market Message for an AI Product or Service - Separating "Attraction" and "Positioning" Themes

Developing the Market Message for an AI Product or Service – Finding a “Core Message” That Works

Over the last three years, Emerj has had the privilege to work on hundreds of individual thought leadership and lead generation campaigns for AI companies around the world - via our Creative Services arm.

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