Source document: https://docs-lawep.s3.us-east-2.amazonaws.com/1697807985099.pdf (India)
AI for Operations By the People, For the People Devavrat Shah Andrew (1956) & Erna Viterbi Professor MIT Cofounder CTO Ikigai Labs Artificial Intelligence (AI) Term was coined in 1960s Goal is to mimic human behavior in an intelligent manner AI aims to do what humans can do, but really well [automation] Artificial “creativity” Drive, Play games, Customer support, … AI seeks to create “muscle” and “mind” of humans Muscle: actuation, mechanics, … [Robotics] Mind: learning from data, … [Machine Learning, Statistics] Traditionally using “rules” and “decision trees” machines will be capable, within twenty years, of doing any work a man can do -- Minsky 1960s The Challenge Automation within organizations Using AI and Data (+ Computation) That is By the People , for People Create productive organizations by enabling individuals Organizations are “(financial) instruments” Automation of decisions with humans in the loop with aim of improving the returns on investment (ROI) using AI and data Organizations, People Data, People in Orgs: pre Computers Data, People in Orgs: enter Computers Data, People in Orgs: enter Business Intelligence Data, People in Orgs: enter e-Commerce A Day in the Life of a Data Operator make decisions to achieve best ROI how much new inventory to order every day Data Prep Advanced Analytics & ML Visualization What-if & Scenario analysis Actions Q1.
What is the ground truth?

Q2.

What action should we take?

Q3.

What strategic changes to make?

A Day in the Life of a Data Operator make decisions to achieve best ROI how much new inventory to order every day Data Prep Advanced Analytics & ML Visualization What-if & Scenario analysis Actions Q1.

What is the ground truth?

Q2.

What action should we take?

Q3.

What strategic changes to make?

Can’t Figure Ground Truth!

Multiple Sources Schemas Don’t Match Can’t Make Good Forecasts!
Constantly Changing Trends Data is Sparse Can’t Do Scenario Analysis!

Multiple Objectives Too Many Scenarios A Day in the Life of a Data Operator make decisions to achieve best ROI how much new inventory to order every day Data Prep Advanced Analytics & ML Visualization What-if & Scenario analysis Actions Q1.

What is the ground truth?

Q2.

What action should we take?

Q3.

What strategic changes to make?
DeepMatch AI driven data prep for disparate sources DeepCast predictions w extremely limited data DeepRL Interpretable RL w upto 1019 scenario analysis Three AI Nuggets DeepMatch DeepMatch DeepMatch DeepCast DeepCast Key insights: Little data across time but, across many items enables accurateforecasts tspdb.mit.edu DeepRL DeepRL Key insights: Interpretable policies Trade-off curves Human-in-the-loop can simulate1019 scenarios Data Operations with ease of spreadsheet (no-code), scale of cloud make decisions to achieve best ROI how much new inventory to order every day Data Prep Advanced Analytics & ML Visualization What-if & Scenario analysis Actions Q1.
What is the ground truth?

Q2.

What action should we take?

Q3.

What strategic changes to make?

DeepMatch DeepCast DeepRL No-Code Operational BI Platform About Ikigai Technology Data Prep Advanced Analytics & ML Visualization What-if & Scenario analysis Actions Q1.

What is the ground truth?

Q2.

What action should we take?

Q3.

What strategic changes to make?


DeepMatch DeepCast DeepRL AI driven data prep for disparate sources time series forecasting w extremely limited data Interpretable RL w upto 1019 scenario analysis Customers Give It a Try?

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