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AI for enterprise, a roadmap 🗺️

On Monday, more than 300 founders, investors, builders, and journalists gathered in NYC for A.Team's first ever Generative AI Summit.

This is MISSION, a weekly newsletter for all the builders out there doing wildly inventive things with new AI tools.

A.TEAM HACKATHON

3 Steps to Building an Enterprise AI Prototype

How can any new AI product rise above the fray and make a profit? By solving a big hairy problem for an enterprise company.

A.Team held a generative AI hackathon to test this hypothesis. Each team of expert AI builders was paired with a Fortune 500 executive with an insider’s perspective on the toughest challenges facing their industry. 

Then, on Monday, nearly more than 300 founders, investors, enterprise executives, builders, and journalists gathered at the A.Team Clubhouse in NYC for the first Generative AI Summit, drinking GPTinis (made with Blank Street espresso and vodka) and watching demos as the judges picked a hackathon winner. 

You can watch how it all went down right here. 

We learned a lot—enough to compile a roadmap for creating your own generative AI enterprise prototype. Here are the three main steps:

1. Discovery: Identify the Pain Point

The first step any team took was to identify a problem within an enterprise that could be addressed with AI. The idea was simple: Solve non-artificial problems with artificial intelligence.

JAY, an intelligent claims assistant, is designed to revolutionize the way claims are processed in the auto insurance industry. It focuses on a major pain point for the insurance industry: Reducing costs while enhancing customer experience with its human-like capabilities.

By focusing on customer satisfaction and revenue generation, Jay embodies the ideal of this new era of AI tools: Solve workflow problems so that people could spend more time thinking and being creative.

2. Product Spec: Vet Potential Solutions

Once a problem could be identified, the teams entered the discussion and vetting phase. They brainstormed solutions, assessing each for feasibility, effectiveness, and potential impact.

This stage was crucial. Teams had to balance creativity with practicality. They had to ensure their solutions would not only solve the problem but also be coherent and easily demonstrated within the hackathon's two-day timeframe.

Take mAI CFO, which serves as a virtual CFO for small businesses. As Paul Sangle, a Product Manager and A.Team builder, explained, "Our platform offers small business owners the ability to get answers to complex financial questions with customized insights."

3. Implementation: Build a Prototype

Another finalist, Floorplan.ai, built a "Dream Floor Plan" tool that leverages machine learning to create floor plans based on text and simple dimensions, bypassing time-consuming and expensive architectural consultations.

Although enterprise advisors provided guidance and support, the developers led this stage, utilizing their skills to create functional prototypes within the two-day timeframe.

The hackathon took a bottom-up approach, incorporating developers early to investigate what was possible, learn new capabilities, and bring back the findings to the product manager. This allowed for a fluid, organic process, with developers and project managers collaborating to bring their AI solutions to life.
To learn more about building AI enterprise prototypes, watch the livestream replay and read the article.

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