SARAH GUOFEBRUARY 2017

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

O u r M i s s i o n

Partner with extraordinary entrepreneursto build enduring , industry-defining

companies

Some people are ta lk ing about AI as the next “platform.”

AI is not a “platform,”I t ’ s an enabling technology .

Many “X-with-ML” startup business plans

(where X is some category of software)

…but not so simple.

How are startup SaaS companies

actually making ML part of their

competitive advantage?

1. Tell Me Something New ++++

2. Replacing Rules-Based Systems +++

3. The Ironman Suit ++

4. Replacing Humans +

4 Models (Not Equally Common Today)

Model #1: Tell Me Something New

Improve customer experienceData: Collect surveys, reviews/social, transactions, call logs, etc.ML: NLP on customer interactionsInsight + Workflow: What (concretely) makes customers happy? Loyal?

Extract useful data from cheap, frequent satellite imagesML: Computer vision to recognize, count, measure, track objectsFind use cases: government, finance, oil & gas, etc.

Improve construction efficiencyData: Collect timesheets, geo, cost codes, orders, notes, etc.ML: Computer vision to tag images, NLP on notes and ordersInsight + Worflow: What impacts our productivity? Causes delays?

Problem—first:

Data—first:

Model #1: Tell Me Something NewQuestions 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?

Model #2: Replacing Rules

Replace rules-based credit models for marketplacelending 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 andintervene early

Same business model, new tech:

New business model, new tech:

Model #2: Replacing RulesQuestions to Consider…

Trust and accuracy of your algorithm?

Regulatory hurdles to change?

Does your accuracy matter?

Is the ML approach less operationally costly?

Model #3: The Ironman Suit

Make your security operations team better/faster byfirst 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!)

Model #3: The Ironman SuitQuestions 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?

Model #4: Replacing Humans

Human-skill operational tasks accessible by APIe.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:

Model #4: Replacing HumansQuestions 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?

AI-enabled SaaS will do more work for

us, and is a massive opportunity.

AI does not enable distribution, is not a

“platform,” but it may be part of your

differentiation.

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

Sarah Guos 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