AGILE AND AUTOMATION CONCLAVE 2018...Agile and Automation Conclave 2018 NEXT STEPS FOR AGILE SMES Understand the exact problem or improvement area where AI capabilities can help. Act

AGILE AND AUTOMATION CONCLAVE 2018

AI in AgileWorkshop

Anubhav F GuptaRaghavendra Meharwade

Agile and Automation Conclave 2018

RAGHAVENDRA MEHARWADE

• Oxfam Trailwalker• Lead Functional Architect – Accenture

myWizard Agile• Scrum Master/Agile SME/Coach

ANUBHAV F GUPTA

• Agilist• myWizard Agile FA• CSM, SAFe Agilist, Trained Kanban

Practitioner


AGENDA

Race for AI

What is AI?

What is ML?

Why Agile with AI?

Case Study

Next Steps, Possible Challenges and Solutions

Q&A

1

2

3

4

5

6

7


- by CBINSIGHTS

RACE FOR AI

– BY CBINSIGHTS https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/

https://www.cbinsights.com/research/top-acquirers-ai-startups-ma-timeline/

Agile and Automation Conclave 2018Copyright © 2018 Accenture All rights reserved. 5

WHAT IS AI?


WHAT IS AI?

AI ALLOWS SMART MACHINES TO EXTEND HUMAN CAPABILITIES

SENSE

COMPREHEND

ACT

LEARN

Computing Vision

Audio Processing

Natural Language ProcessingKnowledge Representation

Machine Learning

Expert Systems

Virtual Agent

Identity Analytics

Cognitive Robotics

Speech Analytics

Recommendation Systems

Data Visualization

Perceive the world

Analyze and understand

Make informed decisions

Improve performance


FOUNDATION

ROBOTIC

VIRTUAL AGENTS & ANALYTICS

MACHINE LEARNING/DEEP LEARNING

Project level, ad-hoc automation

Automate repetitive and predictable tasks to eliminate manual

effort

Use of analytical tools to predict

and recommend

Self-learningsystems and

self-evolving tools

Agile Examples:• Automation of reporting

like Sprint Closure Report, Weekly Report etc.• Automation of Metrics

Calculation.

Agile Examples:• Prediction of velocity,

defects, throughput etc.• Root cause analysis using

descriptive analytics• Mitigation of risks using

predictive analytics.

Agile Examples:• Predict story points against

stories.• Predict efforts against tasks.• Insight into different aspects

using BOTs.

“INTELLIGENCE”

JUDGEMENT-DRIVEN

TRANSACTIONAL

RPA

AI

Agile Examples:• RPA based auto test

cases execution.• DevOps based CI, CD etc.

AI AUTOMATION LEVELS


WHAT IS MACHINE LEARNING?


“Machine learning is the science of getting

computers to act without being explicitly

programmed.” – Stanford

“Machine learning algorithms can figure out

how to perform important tasks by generalizing from examples.” – University of

Washington

MACHINE LEARNING


TYPES OF MACHINE LEARNINGMachine Learning

• The training set is labeled. • Classification and Regression

• The training set is unlabeled. • Clustering

• No labeled or unlabeled data set.

• Algorithm learns to act in an env. to maximize reward.

• Customer Segmentation• Weather Forecast

• Spam Mail Detection• Speech Recognition

• Self-driving Car• Chess

Supervised Unsupervised Reinforcement


COMMON MACHINE LEARNING ALGORITHMS

Reinforcement

Unsupervised

Supervised

Classification

Regression

Clustering

Support Vector

Machines

Discriminant

AnalysisNaïve Bayes

Nearest

Neighbor

Linear

Regression,

GLM

SVR, GPREnsemble

MethodsDecision Trees

Neural

Networks

K-Means, K-

Medoids Fuzzy

C-Means

HierarchicalGaussian

Mixture

Neural

Networks

Hidden Markov

Model

Temporal

Difference

Learning

Q-learning SARSA

Learning

Classifier

System

Dynamic

Treatment

Regime

ML


FEATURE ENGINEERINGFeature Engineering is also called variable engineering or attribute engineering.

It is the selection of attributes in your data (such as columns in tabular data) that are most relevant to the predictive modeling problem you are working on.


BINARY CLASSIFICATION

Agile Example:

Story is meeting the INVEST criteria or not. Requirement is ambiguous or not.Defect is duplicate or not.


MULTI-CLASS CLASSIFICATION

Agile Example:

Classification of defects in backlog by severity (critical, high, medium, low)


LINEAR REGRESSION

Agile Example:

Complexity of the story impacting the size (in story points) of the story.


MULTIPLE REGRESSION

Agile Example:

Multiple variables Impacting the size of the story.• Complexity• Team member• CRUD operation• Team composition


CLUSTERING

Agile Example:

Clustering of stories of similar type and then prediction of tasks for stories.


TEXT ANALYTICS/NLP

Text Analytics

Types – ways to frame your data

Sentiment AnalysisAnalyze opinions for tones

Topic ModelingIdentify dominant themes

Term Frequency – Inverse Document FrequencyUncover frequency of a word

Named Entity RecognitionRecognize people, organizations, places and dates

Event ExtractionDiscover relationships between people, organizations, places and dates

Agile Example:Analyzing acceptance criteria of user stories to check quality of the story or to check testability of the story.

Copyright © 2018 Accenture All rights reserved. 19

WHY

AGILE

WITH AI?


STATE OF AGILE

Challenges Experienced Adopting & Scaling Agile Top 5 Tips for Success with Scaling

1

INTERNAL AGILE COACHES

EXECUTIVE SPONSERSHIP

CONSISTENT PROCESS AND PRACTISES

IMPLEMENTATION OF A COMMON TOOL ACROSS TEAMS

AGILE CONSULTANTS OR TRAINERS

5

2

3

4

Reference : https://www.versionone.com/about/press-releases/versionone-releases-11th-annual-state-of-agile-report/

https://www.versionone.com/about/press-releases/versionone-releases-11th-annual-state-of-agile-report/


EXTENDING THE LIST OF SUCCESS FACTORS/TIPSSuccess Factors of Agile

Collaboration

Inspect & Adapt

Fail Fast

Continuous Improvement

Team Dynamics

Quality / Working S/W

Reduce Waste

AI Technologies

NLP (Virtual Agents, Chat BOT …)

Descriptive Analytics

Predictive Analytics

Prescriptive Analytics

Machine Learning / Deep Learning

RPA


CASE STUDY


ENGAGEMENT CONTEXT

• Alpha Omega Inc. recently entered into an partnership with Japanese firm to provide backend

software support for newly furnished transport dashboard displays.

• The software is part of a bigger program that involves multiple related software systems, building

the displays and installing the displays on the railway stations.

• Alpha Omega has deployed four distributed scrum teams (Tokyo and Madrid) to work

collaboratively to deliver its scope.

• Each team is consisting of nine members (4-5 in Tokyo, the rest in Madrid).

• Scrum Master and Product Owner are based out of Tokyo.

• Since last month, a few teams are going through certain challenges which have risked Alpha

Omega’s delivery commitment and the overall program.

• You have been chosen as an Agile cum automation SME to help scrum teams solving their

challenges. We have connected with the teams and figured out the challenges teams are facing.


ENGAGEMENT CONTEXT - CONTINUED

Product Owner Group Challenges:

• Most of the Product Owners are doing the job for the first time—not having enough experience

writing good stories / perform multiple rounds of review iterations with Scrum Master to

complete a story.

• Modules are tightly coupled leading dependent stories across several teams.

• Few Product Owners are not comfortable using English as communication language, converses

only in Japanese.

• Out of four, two Product Owners preferred ‘XYZ’ and the remaining two selected ‘ABC’ Agile ALM

tool to store their backlogs.

• The business user group has logged around 2400+ requirements in IBM Rational Req Pro which

forms the basis for prioritization.

• A good amount of these requirements have found to be duplicate over period of time.



Scrum Master Group Challenges:

• Out of four, two Scrum Masters are playing the role of admin for various tools, platforms and

environments as well as manage access control.

• The Release Management team is dependent on the Scrum Masters to collect measures and

metrics, publication of various reports (sprint planning outcome report, daily progress report,

sprint closure report, weekly status report).

• Scrum Masters are finding challenges to coach the team on ‘best-effort’ estimate.



Team Challenges:

• One member from each location has to stretch to attend each other's daily stand up for transition.

• Technology keeps changing in every sprint resulting into longer sprint planning sessions to arrive at sub tasks and estimate

hours.

• Production support staff seeks team support on intermittent basis for resolving tickets.

• Not everyone in the team has prior Agile experience.

• Team spends almost one full day to update Requirement Traceability Matrix (mandatory deliverable for every sprint).

• Good amount of time is dedicated for discussing stories/defects which found to be duplicate later.

• Team A is able to produce quality deliverables but not able to deliver the complete committed scope in a sprint.

• Team B is able to deliver what they commit to deliver in a sprint, but getting high severity defects during the sprint due to of

extra efforts to achieve the sprint goal.

• Team C spends lots of time in doing root cause analysis and reporting activities.

• Team D is facing an issue of changes in acceptance criteria during sprint execution.


EXPECTATIONS FROM AGILE SME

As an Agile SME & Automation Expert, you are expected to:

• Go through challenges

• Identify automation opportunities

• Refer to data sheets provided and identify right features

• Update automation tracker with your findings


ACTIVITY SCHEDULE

Stages Time (in Minutes) Description Set To Refer

1 10 • Explore case study and identify challenges Set 1

2 10• Identify automation opportunity for

selected challenges• Identify Level of Automation

Set 2 & Set 4

3 15 • Identify data sets and associated features/columns Set 3

4 10 • Selected participant will demo Automation Tracker in Set 5


AUTOMATION TRACKER

Sr. No. Group Challenge Use Case for Automation / ML / AI Level of Automation Data Set & Features

10 Mins 10 Mins 15 Mins

1

2

Group

Team

Scrum Master

Product Owner

Challenge Use Case for Automation / ML / AI Data Set & Feature

< Specify the challenge which you are trying to solve > < Specify the use case which you are proposing as a solution of the challenge >

< Specify table name and respective column names

required to build AI model >

Level of AutomationFoundationRoboticVirtual Agents & AnalyticsMachine Learning / Deep Learning


NEXT STEPS


NEXT STEPS FOR AGILE SMES

Understand the exact problem or improvement area where AI capabilities can help.

Act as a functional expert and visualize the use case for AI

Analyze the historical data and visualize how identified use case be developed using this data like identification of features etc.

Highlight the benefits of using AI capabilities to the management to get required support and budget.

Work closely with the AI Dev Team to get it implemented correctly

Contribute towards building Agile data library!


POSSIBLE CHALLENGES & SOLUTIONS• Lack of management support

• Insufficient budget

• Convincing buyer of value

– Highlighting the benefits of using AI capabilities

– Engagement and Involvement of management /client

– Standardization of practices, processes and metrics

• Unavailability of enough historical data with good volume and good quality

• Client confidentiality / data access

– Managing data repository at organization level for projects of different domains,

technologies etc.

• Short duration projects

• Implementation cost / effort

– Re-using AI assets developed


Q&A


FOLLOW USLinkedIn – SolutionsIQ India | Twitter – SIQIndia | Facebook – SolutionsIQ India

Thank You!

Raghavendra [email protected]

Anubhav F [email protected]

Documents

AGILE AND AUTOMATION CONCLAVE 2018...Agile and Automation Conclave 2018 NEXT STEPS FOR AGILE SMES Understand the exact problem or improvement area where AI capabilities can help. Act