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SaaStr 2017: AI–Enabled SaaS - 4 Models for ML as Competitive Advantage

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Talk from SaaStr Annual 2017

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SaaStr 2017: AI–Enabled SaaS - 4 Models for ML as Competitive Advantage

  1. 1. SARAH GUO FEBRUARY 2017 AI–Enabled SaaS: 4 Models for ML as Competitive Advantage
  2. 2. Our Mission Partner with extraordinary entrepreneurs to build enduring, industry-defining companies
  3. 3. Some people are talking about AI as the next “platform.” AI is not a “platform,” It’s an enabling technology.
  4. 4. Many “X-with-ML” startup business plans (where X is some category of software) …but not so simple.
  5. 5. How are startup SaaS companies actually making ML part of their competitive advantage?
  6. 6. 1. Tell Me Something New ++++ 2. Replacing Rules-Based Systems +++ 3. The Ironman Suit ++ 4. Replacing Humans + 4 Models (Not Equally Common Today)
  7. 7. Model #1: Tell Me Something New Improve customer experience Data: Collect surveys, reviews/social, transactions, call logs, etc. ML: NLP on customer interactions Insight + Workflow: What (concretely) makes customers happy? Loyal? Extract useful data from cheap, frequent satellite images ML: Computer vision to recognize, count, measure, track objects Find use cases: government, finance, oil & gas, etc. Improve construction efficiency Data: Collect timesheets, geo, cost codes, orders, notes, etc. ML: Computer vision to tag images, NLP on notes and orders Insight + Worflow: What impacts our productivity? Causes delays? Problem—first: Data—first:
  8. 8. Model #1: Tell Me Something New Questions to Consider… Do you have advantaged access to the data? Do you need to collect the data? What friction is involved in collection/integration? Can you operationalize the insights? Can you track the changes you’re trying to bring about? Does the executive care? What’s the ROI?
  9. 9. Model #2: Replacing Rules Replace rules-based credit models for marketplace lending with ML-powered ones Recognize and block malware based on behaviors, (not signatures) Offer a health insurance plan, drive down costs using “population health management” – predict issues and intervene early Same business model, new tech: New business model, new tech:
  10. 10. Model #2: Replacing Rules Questions to Consider… Trust and accuracy of your algorithm? Regulatory hurdles to change? Does your accuracy matter? Is the ML approach less operationally costly?
  11. 11. Model #3: The Ironman Suit Make your security operations team better/faster by first surfacing insight at scale, then predicting investigation/response actions Guess your replies (1/3 of responses on mobile!) Help business analysts and quants build machine learning models quickly and easily (how meta!)
  12. 12. Model #3: The Ironman Suit Questions to Consider… Value of user time, talent, superpowers? Are you solving a scarcity problem? Does the superpower drive buying decision? Friction to adopt a new system?
  13. 13. Model #4: Replacing Humans Human-skill operational tasks accessible by API e.g. creating training sets, content moderation “Personal Assistant” for scheduling meetings by email Improve medication adherence in clinical trials by replicating “directly observed therapy” with computer vision AI—assisted humans: Algorithm:
  14. 14. Model #4: Replacing Humans Questions to Consider… Can you provide an end-to-end experience? How is the service consumed? Is the accuracy sufficient? Will it fail gracefully? Even if your core service is efficient, is sales/success?
  15. 15. AI-enabled SaaS will do more work for us, and is a massive opportunity.
  16. 16. AI does not enable distribution, is not a “platform,” but it may be part of your differentiation.
  17. 17. STILL HAVE TO BUILD A GREAT SAAS COMPANY: THE RIGHT TEAM INTIMATE UNDERSTANDING OF CUSTOMER UNIQUE + COMPELLING VALUE PROPOSITION THE RIGHT TIMING THE LAST MILE CAPITAL-EFFICIENT GO-TO-MARKET DEFENSIBILITY
  18. 18. Sarah Guo s a r a h @ g r e y l o c k . c o m @ s a r a n o r m o u s

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