Impact of AI,ML, Cognitive Computing on the future of Jobs

1. Siri, Cortana, Alexa,
Erica, Eva ……
It’s getting crowded here!!
Cognitive Computing, AI/ML and the Future of Jobs
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Padmaja Surendranath
Data Solutions Architect, Career Coach & Thought Leader
pady.suren@gmail.com

2. The Future of our Jobs
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3. McKinsey Report Dec 2017
Research conducted to answer the questions
➢ Will there be enough work in the future to maintain full employment, and if so
what will that work be?
➢ Which occupations will thrive, and which ones will wither?
➢ What are the potential implications for skills and wages as machines perform
some or the tasks that humans now do?
•https://www.mckinsey.com/featured-insights/future-of-organizations-and-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#part4
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4. Good News!!!
•https://www.mckinsey.com/featured-insights/future-of-organizations-and-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#part4 4

5. Not-so-good News !!
•https://www.mckinsey.com/featured-insights/future-of-organizations-and-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#part4 5

6. McKinsey Report Recommends
•https://www.mckinsey.com/featured-insights/future-of-organizations-and-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages#part4 6

7. Things in flux, How to Manage?
Cultivate
➢ Foresight
➢ Take Stock: Pay close attention to industry developments
➢ Anticipate implications for possible futures
➢ Agility
➢ Sense, adjust and adapt within short time frame
➢ Resilience
➢ Bounce back quickly
Critical Capacities for navigating in turbulent times by Art Murray and Flynn Bucy, pg.3, Vol 27 Issue 3 May/June 2018, KMWorld 7

8. Taking Stock
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9. Right from the start…..
……….IT has always been about data
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10. How to Collect ?
How to Store?
How to Describe?
How to Use to understand
- the business
- operations
- life around me
Data…..
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11. Data…..
How to Collect ?
How to Store?
How to Describe?
How to Use to understand
- the business
- operations
- life around me
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12. We have all been on one or more of the Data LifeCycle stages
Good News is that: These stages are not going to change
What will change is HOW we go about these stages
The Data LifeCycle
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13. How to Collect ?
Then: Data Entry applications. Now: Mobile Apps
How to Store?
Then: DB Servers Now: Object Storage on cloud
How to Describe?
Then: Lists, Reports Now: Interactive Viz
How to Use to understand?
Then: Dashboards Now: Analytic Applications
For example…..
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14. Anticipating Possible Futures
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15. For each stage in the Data LifeCycle
❏ What is the innovation now?
❏ What opportunities for improvement exist?
❏ Is this something that I am interested in improving?
❏ What skills/competencies do I need?
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16. Repeat the exercise for each of the following
➢ Analytic LifeCycle
○ Prep Data, Explore Data, Create Model, Validate Model, Run Model
➢ DevOps Activities
○ Build, Release
➢ QA/Test Activities
➢ History of transformations applied to the dataset along the pipeline
➢ Data Quality along the pipeline
➢ Data Privacy and Security along the pipeline
➢ Ethics of Data Usage, Processing and Delivery
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17. Possible opportunities for improvement
➢ Analytic LifeCycle
○ Prep Data, Explore Data, Create Model, Validate Model, Run Model
○ How will data availability across environment impact the model? Will Data Prep stage be
similar to ETL or will it be different? What challenges might arise? Can we export a model?
➢ DevOps Activities
○ Build, Release
○ Will there be anything different in these steps for an Analytic LifeCycle ?
➢ QA/Test Activities
➢ Data Quality along the pipeline
➢ Data Privacy and Security along the pipeline
➢ Ethics of Data Usage, Processing and Delivery
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