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A brief history of artificial intelligence for businessJack Crawford, Founder and CEO

Arthur L. Samuel, 1959

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Back then, AI was a forest of trees

citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.368.2254&rep=rep1&type=pdf

In the 1970s interest in AI renewed

Artificial intelligence attracted moneylike bears to honey

4Image: zooportraits.com

VC Bear

and the money dried up

Then, the bears got burned

5Image: The Huffington Post

After a long AI Winter, the winds of Δ ignited interest again

6Image: The BBC, “AI: 15 key moments in the story of artificial intelligence”

The first money saving business AI was an “expert system” built at DEC in the 1980s

In the 1990s, other businesses began to to employ “expert systems”

7Illustration: learnlearn.co.uk

EXPERT SYSTEM

It was the age of …

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Rules

Rules to put people (us) into buckets

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MarketSegmentation

Then Jeff Berry had a bright idea

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“Each customer shouldbe seen as aSEGMENT OF ONE

Image: LoyaltyOne

11Image: Getty Images, Cristian Baitg

Which ushered in the age of the …

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Individual

The problem was that at that time …

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The only way to predictindividual behavior and act on it in a timely manner involved using rules

Image: Pegasystems, “Next Best Action,” youtu.be/HeL-Y1kSoDg

RULES

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Maybe, just maybe …

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We don’t need rules

Two years ago at the 29th AAAI

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aaai.org/ocs/index.php/AAAI/AAAI15/paper/viewFile/9444/9488

Here’s a true storyThe problem

1.Our client had 49% market share YOY

2.It dropped to 46% in the past year

3.Customer switch was the culprit

The goal

Find out which customers were likely to switch

That is, identify thesegment of 1

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Steps that we took1. Ask the CMO if we could try2. Listen to her laugh and say “Sure. Good luck with that.”3. Call a meeting with the data guardians, citing approval from their chief

marketing officer4. Wait to receive the data we requested (1 month)5. Load and clean up the data (1 hour)6. Run the model on 70% of the data (1 day) 7. Verify prediction with the remaining data (1 day)8. Spend time improving results so they don’t think it was easy (1 month)9. Give them the test results (92% accuracy over the past 3 years)

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So what did we hope they would do with this miraculous information?1. Create strategies to identify interventions for customers with high

propensity to switch to their competitor 2. Establish “A/B” testing (or other measures) to validate the

effectiveness of these strategies in practice3. Put these interventions into the “field”4. Realize the benefits through recovery of their market share

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This particular story had a surprising twist, and led to our AI startupThey ”shelved” the idea

Why?Other market factors might have caused the switch

1. Slow pace of product improvement2. Reduced advertising budgets3. Sales force effectiveness gaps

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And that “made sense” in 2015?

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At the time, many businesses couldn’t see the power of machine learning

for predicting the buying behavior of an individual consumer

It seems that many innovations take forever to be adopted by business – 2009

22machinelearning.org/archive/icml2009/papers/218.pdf

NVIDIA GTX 280

You can make AI valuable for business – opportunitiesSales and marketingNext best actionSalesrep coachingCustomer switchSupply chainSupplier commitment predictionConversational bots

Customer carePropensity to callConversational AIPersonalizationResearch and developmentKnowledge retentionSuccess factor identification 23

Beyond predictive AI, much more opportunity lies ahead

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The next leap in AI is

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Conversation

MyPolly.ai™

Join our conversational AI beta at:

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postscript:

Let’s remember the pioneers who began the journey that brought us here today

The reunion of some early AI researchers

28Image: The New York Times, December 7, 2009

Copyright © 2017, Datalog, Inc., all rights reserved. All trademarks property of their respective holders

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Thank you

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949 • 374 • 1120Jack@datalog.ai

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