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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.

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.

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.

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.

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.

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

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.

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 [email protected].

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

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