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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.

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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.

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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.

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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.

Bridging AI's Trust Gaps

Bridging AI’s Trust Gaps – The Role of Corporate Leaders

This is a contributed article by The Future Society, edited by Emerj and authored by Samuel Curtis, Sacha Alanoca, Nicolas Miailhe, Yolanda Lannquist, Adriana Bora. To inquire about contributed articles from outside experts, contact editorial@emerj.com.

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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.

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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:

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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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Industries Leading in AI Adoption

Industries Leading in AI Adoption – eCommerce, FinTech, Online Media

This article was a request from one of our Catalyst members. The Catalyst Advisory Program is an application-only coaching program for AI consultants and service providers. The program involves one-to-one advisory, weekly group Q-and-A with other Catalyst members, and a series of proprietary resources and frameworks to land more AI business, and deliver more value with AI projects. Learn more at emerj.com/catalyst.

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Creating an AI Transformation Vision

Creating an AI Transformation Vision – Achieving Long-Term Advantage with AI

Artificial intelligence deployments are fraught with technical and tactical elements that have to be executed well in order to see a return on investment: The data must be accessible, cross-functional AI teams have to work together, and even after an AI pilot seems promising - it often needs to be integrated into legacy systems to be deployed successfully.