Artificial Intelligence - Building Teams & Products

Artificial Intelligence - Building Teams & Products

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  BUILDING AI TEAMS& PRODUCTS Aarthi Srinivasan Director of Product Management Target Inc. 1
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  How many openAI/ ML jobs do we have in US today? 2
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  % of timespent 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 3 • Where can you automate by augmenting human wisdom?
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  HBR survey summaryof 3000 AI executives Reference - McKinsey & co 4 • 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.
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  How can wehire for so many roles? 5
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  Enter Citizen’s DataScience Sharon Terry – CEO of Genetic Alliance 6
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  7 Gartner’s definition ofCitizen data scientist
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  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 8
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  Sample data organizations 9
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  10 Sample Journey Solve oneproblem – 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
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  Culture-Worry less aboutcrunching with more focus on serving the model Voice of the customer Purpose driven product Product teams with missions Fun and encouragement
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  Unified experience forguests 12 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 P r o d u c t M a n a g e r s Unified experiences through channels such as E-mail, Apps, iOT, Google Home/Alexa, Website, wearables, Stores, at Home Marketing & Service Channels 9
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  Recruiting tips –Open discussion 13 • Intern Program • Brand / Culture Social awareness • University partnership • LinkedIn friend profile sharing – Lite referral • Crowd sourced contests • Fun culture