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Research Proposal
Prepared for Yotam O. | Delivered October 1, 2019
AI Startup Data
Goals
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
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Early Findings
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.
2019 was the
highest year of funding
on record for AI startups, with the second quarter alone seeing
$7.4 billion
of investments.
The
majority of this fundin
g went to AI startups in the transportation and healthcare spaces.
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.
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Prepared By
Jennifer P.
1284 assignments | 5.0