How to build and scale AI Teams? Discussion with research on the organizational scaling and recruiting & retaining Machine Learning talent.

1. BUILDING AI TEAMS & PRODUCTS
Aarthi Srinivasan
Director of Product Management
Target Inc.
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2. How many open AI/ ML jobs do we have in US
today?
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3. % of time spent in activities that can be automated
Automation using Machine Learning
• Retailers use both physical automation such as robots in warehouses and algorithms to predict what users will purchase
Reference - McKinsey & co
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• Where can you automate by augmenting human wisdom?

4. HBR survey summary of 3000 AI executives
Reference - McKinsey & co
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• Don’t believe the hype: Not every business is using AI despite a 26B investment in AI
ü 20% are using at least 1 solution at scale
• Believe the hype that AI can potentially boost your top and bottom line
ü 30% of the users are achieving revenue increase
• Without support from leadership, your AI transformation might not succeed
• You don’t have to go it alone on AI — partner for capability and capacity. Machine learning is a powerful tool, but it’s
not right for everything.
• Resist the temptation to put technology teams solely in charge of AI initiatives
• Take a portfolio approach to accelerate your AI journey
ü Short-term: Focus on use cases where there are proven technology solutions today and scale
ü Medium-term: Experiment with technology that’s emerging but still relatively immature
ü Long-term: Partner to solve a high-impact use case with bleeding-edge AI technology
• Digital capabilities come before AI
ü Odds of generating profit from using AI are 50% higher for companies that have strong experience in digitization.

5. How can we hire for so many roles?
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6. Enter Citizen’s Data Science
Sharon Terry – CEO of Genetic Alliance
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7. 7
Gartner’s definition of Citizen data scientist

8. Identify data needs & create a service mentality
Data Hygienists
Clean incoming data for accuracy
E.g. Calendar days vs. working days to count # of days
Data Explorers
Sift through data to discover the data we actually need
E.g.Training data
Solution
Architects
Organize the explored data for analysis & querying
Data Scientists Model the organized data for predictive analytics
Experience
Experts
Turn the models into experiences that get results
E.g. e-mail, Interactive
Reference – HBR
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9. Sample data organizations
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10. 10
Sample Journey
Solve one problem –
Share success Metrics
Try new solutions in few
channels to showcase
results
Expand to other channels
& problem sets
Resulting web of trees
Create a product organization with agile practices & transparency with key objectives

11. Culture-Worry less about crunching with more focus on serving the model
Voice of the customer
Purpose driven product
Product teams with missions
Fun and encouragement

12. Unified experience for guests
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Customer Backbone triggers User Experience with Real-time Context
Customer Lifestage & Shopping Journey
Products Promotions Supply Demand
Prediction
scoring
Context Security
Channel
optimized
Operations
AI / ML Engine (Algorithms, Operations, Network optimizations)
Customer Experience Backend Optimizations
Infrastructure & Data warehouse Management
Segmentation A/B TestingIdentification
Privacy &
Security
Data as a
service Content & Data Mgmt
User Facing
Tech Backend
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Unified experiences through channels such as E-mail, Apps, iOT, Google Home/Alexa, Website, wearables, Stores, at Home
Marketing & Service Channels
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13. Recruiting tips – Open discussion
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• Intern Program
• Brand / Culture Social awareness
• University partnership
• LinkedIn friend profile sharing – Lite referral
• Crowd sourced contests
• Fun culture