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Title Page
1/11/2025
Copyright Page
1/11/2025
Dedication Page
1/11/2025
About the Author
1/11/2025
About the Reviewers
1/11/2025
Acknowledgement
1/11/2025
Preface
1/11/2025
Table of Contents
1/11/2025
1. Foundations of the AI Landscape
1/11/2025
Introduction
1/11/2025
Structure
1/11/2025
Objectives
1/11/2025
World view of AI
1/11/2025
AI as the new buzzword
1/11/2025
Closer look at the current scenario
1/11/2025
Other side of the story
1/11/2025
World of managers
1/11/2025
Upcoming effects of AI on the horizon
1/11/2025
AI as an assisting hand
1/11/2025
Sample case 1: Money management
1/11/2025
Sample case 2: Healthy conversations
1/11/2025
AI as a reviewer
1/11/2025
AI in the driver's seat
1/11/2025
Ethical considerations of AI
1/11/2025
Temptation to cross that imaginary line
1/11/2025
Difficulty in spotting AI-generated content
1/11/2025
Bending the rules
1/11/2025
Responsible AI
1/11/2025
Plagiarism
1/11/2025
Conclusion
1/11/2025
2. Applications of AI in Your Domain
1/11/2025
AI tools for team productivity
1/11/2025
Business reality
1/11/2025
AI in the context of coding
1/11/2025
Various possible coding scenarios
1/11/2025
Magic of AI as a time saver
1/11/2025
AI in the context of quality assurance
1/11/2025
Generating new tests at the click of a button
1/11/2025
Quality over quantity by optimizing testing
1/11/2025
AI in the context of software architecture and design
1/11/2025
Requirements for a large code base
1/11/2025
Magic of AI to the fore
1/11/2025
AI for repetitive work
1/11/2025
Automation as a tried and tested practice
1/11/2025
Case study for why AI fits support like a glove
1/11/2025
Taking a step further with AI
1/11/2025
Domain-specific AI tools
1/11/2025
AI as a helping hand
1/11/2025
Helping hand example 1
1/11/2025
Helping hand example 2
1/11/2025
AI as the reviewer
1/11/2025
Reviewer example 1
1/11/2025
Reviewer example 2
1/11/2025
Driver's seat example 1
1/11/2025
Driver's seat example 2
1/11/2025
Exploring further possible applications of AI
1/11/2025
Necessity as the mother of invention in the AI space
1/11/2025
Learning the necessity
1/11/2025
Innovating on others' necessities
1/11/2025
Decoupling the person from the role
1/11/2025
Conventions are inventions in the space of AI
1/11/2025
Going back to being a learner
1/11/2025
Idea generation and hackathon sessions
1/11/2025
Keeping up with the happenings in the AI space
1/11/2025
Your role may be getting in your way
1/11/2025
What you can do
1/11/2025
3. Type A, the Wide-eyed Ones
1/11/2025
Identifying and defining this persona
1/11/2025
At the extreme ends of the spectrum
1/11/2025
Skeptic about authority
1/11/2025
Untouched by the real AI
1/11/2025
Safety in conformance
1/11/2025
Group mentality
1/11/2025
Learning more by having a dialogue with them
1/11/2025
Learning 'what' they know
1/11/2025
Learning the 'why' underneath
1/11/2025
Learning the 'how' as well
1/11/2025
Managing their expectations
1/11/2025
Channeling their curiosity
1/11/2025
Leveraging the 'how' we learnt earlier
1/11/2025
Balancing the extreme ends
1/11/2025
Help control the sway
1/11/2025
Educating them on tools and use cases of AI
1/11/2025
Becoming the matchmaker
1/11/2025
Driving them towards learning
1/11/2025
Redirect them to more trustworthy channels
1/11/2025
Putting those diverse heads together
1/11/2025
Debate it out
1/11/2025
Write it down
1/11/2025
Fieldwork
1/11/2025
4. Type B, the AI Enthusiasts
1/11/2025
Person with opinions
1/11/2025
Expert on most things AI
1/11/2025
True believers in AI
1/11/2025
Looking for problems to fit the AI solution
1/11/2025
Grounding their expectations
1/11/2025
Having a dialogue about goals and metrics
1/11/2025
Making it real and getting them to learn themselves
1/11/2025
Raising awareness of the problems at hand
1/11/2025
Fixing the syndrome
1/11/2025
Shiny object tendency and the problem therein
1/11/2025
Drawbacks of the solution first approach
1/11/2025
Channeling this into something constructive
1/11/2025
Morphing to be ambassadors of the AI revolution
1/11/2025
Referencing the competitive landscape
1/11/2025
From the sidelines to the frontline
1/11/2025
Popular and pleasant voice
1/11/2025
5. Type C, the nAI-sayers
1/11/2025
Status quo first
1/11/2025
Why fix it if it is not broken
1/11/2025
Non-believer in AI or technology
1/11/2025
Getting to the bottom of their skepticism
1/11/2025
Having a dialogue to learn more
1/11/2025
Connecting and empathizing
1/11/2025
Discussing efficiency, productivity and a better way
1/11/2025
Case 1: In case problems are the centerpiece
1/11/2025
Case 2: In case of AI being the solution is the centerpiece
1/11/2025
Making them aware of the world's reality
1/11/2025
Ground under us is moving
1/11/2025
Skillset landscape is evolving rapidly
1/11/2025
Enriching with information to bridge the gap
1/11/2025
Tools and technologies
1/11/2025
Competitive landscape
1/11/2025
Memos and roadmaps
1/11/2025
Learning and internalizing the organization's vision
1/11/2025
Distilling the larger vision to team-specific level
1/11/2025
Using roadmaps to pave the road forward
1/11/2025
Handling the possible lack of structured vision
1/11/2025
Training and workshops
1/11/2025
Connecting them with the other two personas
1/11/2025
Type As
1/11/2025
Purpose and goals
1/11/2025
Few possible narratives
1/11/2025
Type Bs
1/11/2025
Co-hacking and seminars
1/11/2025
6. Plan the Transformation
1/11/2025
Taking a stock of things compiled so far
1/11/2025
Preparing narratives for leadership and team
1/11/2025
Key terms
1/11/2025
Deciding the tone and crux of your story
1/11/2025
Centered around business objectives
1/11/2025
Centered around productivity, efficiency and a better way
1/11/2025
Centered around survival
1/11/2025
AI everywhere
1/11/2025
Deciding the narrative for leadership
1/11/2025
Deciding the narrative for your team
1/11/2025
Establishing success criteria and metrics
1/11/2025
Case 1: Business objectives-based goals and mission
1/11/2025
Case 2: Productivity and efficiency-based goals and mission
1/11/2025
Deciding changes for 30-60-90-days
1/11/2025
First 30-days
1/11/2025
Next 30-days
1/11/2025
Last 30-days
1/11/2025
7. Execute the Transformation
1/11/2025
Tips for moving from planning to execution
1/11/2025
Pitching the story to leadership
1/11/2025
Pitching the story to the team
1/11/2025
Estimation of each task before starting the first 30-days
1/11/2025
Increasing confidence by incorporating a buffer
1/11/2025
Sequencing of tasks
1/11/2025
Assignment of tasks to the right team members
1/11/2025
Adjusting the estimates once more before starting
1/11/2025
Re-evaluating the trajectory for the next 30-days
1/11/2025
Revisiting the assignments for the second phase
1/11/2025
Thinking about the last 30-days
1/11/2025
Reviewing and questioning the 'how' and 'what'
1/11/2025
Defining intermittent milestones
1/11/2025
Keeping the progression of 'what' in view
1/11/2025
Keeping the 'how' going right
1/11/2025
Being A(I)-gile
1/11/2025
Reviews with leadership
1/11/2025
How of the 'how'
1/11/2025
Early customer feedback
1/11/2025
Ongoing retrospectives to fine-tune the approach
1/11/2025
On-the-spot retrospectives
1/11/2025
Recurring retrospectives
1/11/2025
Retrospective for planning
1/11/2025
8. Feedback Loop
1/11/2025
Setting up listening and feedback channels
1/11/2025
Customers
1/11/2025
Leadership
1/11/2025
Team
1/11/2025
Compete
1/11/2025
Keeping room for dialogue and changes
1/11/2025
Making use of the incoming feedback
1/11/2025
Feedback from customers
1/11/2025
Feedback from leadership
1/11/2025
Feedback from the team
1/11/2025
Feedback from compete analysis
1/11/2025
Evolution of goals and success measures
1/11/2025
Agility in planning
1/11/2025
Agility in execution
1/11/2025
Keeping an ear to the ground on advancements in AI
1/11/2025
Letting 'B' loose
1/11/2025
Prototyping stream
1/11/2025
Leveraging AI leaders in and outside the company
1/11/2025
9. Beyond the Tactical
1/11/2025
Communication and collaboration in AI-enhanced teams
1/11/2025
Importance of communication and collaboration
1/11/2025
Within the team
1/11/2025