AI Startup Data
Provide data on the US AI market, specifically how many enterprises in the US already have AI models in production, and how many applied-AI startups exist, in order to size the market for a startup with a horizontal AI platform
In, 2018 just 8% of companies had already made significant direct investments in AI. However, 20% of business executives of enterprise size companies noted they plan to implement AI enterprise-wide by the end of 2019. Venture capital funding for AI startups grew 72% between 2017 and 2018. Funding success for startups depended on where they fell AI value chain, which was broken into 6 commercial segments, AI chip and hardware makers, cloud platform and infrastructure providers, AI algorithmic and cognitive services, enterprise solution providers, and industry vertical solution providers, corporate takers of AI who are looking to increase revenues via AI investment.
AI startups that succeed most often have access to proprietary data training sets, domain knowledge, and a deep applied AI talent pool.
One source predicted that enterprises use for AI will reach $3.9 trillion by 2022, and AI is noted to potentially contribute up to $15.7 trillion globally to the economy by 2030.
Proposed next steps:
You need to be the project owner to select a next step.
Triangulate existing data on startup directories (such as Angellist and Crunchbase) to estimate how many total startups are in the AI space in the US
Provide case studies of 2-3 companies in a similar AI space that have received significant funding, and what their strategies and challenges were in seeking that funding