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Similaire à Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scale and Profit(20)


Artificial Intelligence Impact - What AI is (and isn't) Helping Startups Scale and Profit

  1. Where AI is (and isn’t) Helping Companies Scale and Profit Artificial Intelligence Impact
  2. Take-Aways from This Talk • How and when you might use AI to further your business goals • Ideas will be based on examples of real business use cases, and the opinions of experiences executives and investors
  3. Outline of the Talk • Background • What Investors Look for in AI Applications (1, 2, 3) • Examples of AI in Industry • Applying AI toYour Own Business Model • End
  4. Points of Note • Write down companies and use cases that are interested in to you • See if ideas or presidents of AI’s use might apply to your business now or in the future • After the “Investors Perspective” section and the “Examples” section, we’ll have some time to riff on these ideas and have you ask questions as we move • If you have questions as we move along, please feel free to just chime in
  5. Background Brief • I’m Dan Faggella, CEO/Founder • Interview top execs and researchers, Conduct unique market research in AI space, Connect AI vendors to buyers through a content network
  6. What Investors Want • artificial-intelligence-from-29-ai-founders-and-executives/
  7. What Investors Want (1) • Business Problem First,Technology Second. • If the term “artificial intelligence” gets an investor excited, they’re a poor investor, and their stake in your company may be a hinderance to raising future rounds. Smart investors want a specific business problem solved for a large addressable market. If you have no compelling case as to why AI is the best method for solving this business problem, an intelligent investor will not care.
  8. What Investors Want (2) • The Proprietary Data Plume of Defensibility. • Involves the ongoing creation of data through business operations (IE: not buying it from outside sources) • Involves adequate collection and organization of that data so that it can be put to use • Implies that this date is useful in improving operations • Implies that this data is inaccessible or near-inaccessible to your competitors
  9. What Investors Want (2) • The Proprietary Data Plume of Defensibility. • Ex:Tracking the average time and miles to get from point (a) to point (b) within a city, controlling for time of day, holidays, weather • Ex:Tracking logins, views, ratings, and stick-rate Netflix, while also including demographic info • Ex:Tracking engagement, usage, while also tracking all in-app purchases and customer lifetime value
  10. What Investors Want (2) • The Proprietary Data Plume of Defensibility. • What we need: (a) Are we tracking to the end result we are after? (b) Are we tracking the necessary contextual factors that will give us an accurate picture
  11. What Investors Want (3) • Improvement with Use / Winner-Takes-All • Can your normal operations create a positive feedback loop for more and more effective operations? Can your interactions with each user yield results and lessons that will translate to improved experience for or profitability from future users?
  12. What Investors Want (3) • Improvement with Use / Winner-Takes-All • Ex: InsideSales (collects data it can’t share, but can apply those lessons across industry) • Ex: CaseText (can’t access law data others can’t… but can determine user behavior around their product and make the most helpful, intuitive, efficient) • Ex: Google (collects data others cannot about searches / clicks on searches and ads)
  13. Examples of AI in Industry • Some businesses will need AI right away, some may use it over time to beat out competitors and get a competitive advantage.Most startups will fall into the latter category due to: • Extreme rarity of AI talent needed to build something legit • Extreme expense of AI talent needed to build something legitimate • The fact that most business problems don’t initially require AI as the only or best solution
  14. Examples of AI in Industry • In some businesses,AI is completely required in order to get the business off the ground in the first place. Businesses like these can’t exist without AI: • PatternEx (anomalie detection) • NervanaSys (core AI tech) • Nuance (translation technology, specifically… classification of sounds) • Viv (classification of sounds) • Zebra Medical Imaging (classification of images) • UpTake (pattern recognition)
  15. Examples of AI in Industry • For some businesses,AI is not totally necessary, but can aide functionality over time: • Consumer multi-sided platforms:AirBNB / UpWork / Uber (multi-sided platform matching optimization / recommendations) • Consumer social media platforms: LinkedIn / Facebook (engagement optimization) • Consumer commerce and media: NetFlix / Pandora / Amazon (recommendations based on individual / group activity) • B2B: InsideSales / PatternEx / SiftScience / UpTake (more customers using it = more learnings for other customers)
  16. Lenses of Improvement • First, don’t do “toy” applications, and don’t “tinker” with AI.Too much time / resources are required. If you don’t see any evident reasons to implement AI, and you don’t have the money, time or organization to do it… then shelf it as an idea for now. So don’t take this and go DO anything with it unless you must… otherwise you won’t be able to justify completing the project and you’ll waste investor money.
  17. = ROI Criterion
  18. = Miscon- ceptions
  19. Lenses of Improvement • Ask yourself: • Are we collecting information from individual users that could be used to improve results for all users? • Outward-facing: PatternEx • Inward-facing: Uber • Are we handling and classifying large volumes of text, audio, image, or video data - which might be low-hanging-fruit for AI automation? • Neither of these should be done until the value of their implementation validates their cost in time and resources
  20. That’s All, Folks! • Feel free to stay in touch with questions or speaking requests: • @danfaggella • •