Agile Network India - Bengaluru Title: AI led Enterprise Transformation by Arpit Tandon Date: 18th May 2024 Hosted by: CGI

1. © 2024 CGI Inc. 1
AI led Enterprise
Transformation
A Practical
Perspective
Arpit Tandon

2. © 2024 CGI Inc. 2
2
Graphic modified from a16z
Cloud Platforms or On-Prem
Applications
Pre-Built Models
MLOps
Users
Traditional
AI
Cloud Platforms – Compute
On-Prem – Data Storage
Closed-Source
Foundation Models
AIOps
Models/Model Hubs
Open-Source
Foundation Models
Users
Applications
Generative
AI
Post ChatGPT
AI Landscape Changed
Foundational models (LLM) outperforming traditional models

3. © 2024 CGI Inc. 3
AI Trends
changing the way
Knowledge
Workers deal with
information
3

4. © 2024 CGI Inc. 4
Search to Ask: Changing the way we interact with information
Traditional Search
Keyword based
Time-consuming manual sifting
through results that disrupts workflows
LLM powered Ask
Retrieves, synthesizes and summarizes
information from multiple sources
Contextual answers directly within the
workflow reducing research time

5. © 2024 CGI Inc. 5
In the new era of digital
marketing, the focus shifts from
displaying links to human users to
empowering AI to extract and
leverage information directly from
your websites.

6. © 2024 CGI Inc. 6
Moving from Copilots to Autonomous AI agents
6
AI tools as
Copilots or
virtual
assistants
Autonomous AI
agents
Human In The Loop Human On The Loop
AI agents as specialized versions of
Large Language Models pre-
prompted to play specific roles in
the delivery process
• Product Owner AI agent
• Developer AI agents
• QA AI agent
• DevOps AI agent
Agents generate responses and
prompt each other autonomously
without the need for human
intervention

7. © 2024 CGI Inc. 7
Reference: GPT Pilot, GitHub

8. © 2024 CGI Inc. 8
AI Projects vs
Traditional
Software
8

9. © 2024 CGI Inc. 9
AI Projects: Fundamentally Different from Traditional Software
Experimentation
AI models are non-
deterministic; requires mindset
shift to allow for iterations and
performance fluctuation
Success criteria (and testing) can
be tough to agree upon as model
performance could be
dependent on factors outside
developer’s control
Compute Intensity
AI projects have a high running
infrastructure costs unlike
traditional software
Infrastructure skills become a
core part of the AI project
rather than a support team as in
traditional software
Maintenance Heavy
AI models need continuous
maintenance to be trained on
fresh data to avoid performance
drift
Monitoring and maintenance
become even more important
than fresh development

10. © 2024 CGI Inc. 10
Path to Unlocking
Delivery
Acceleration
10

11. © 2024 CGI Inc. 11

12. © 2024 CGI Inc. 12
“
Never touched Java before. As a .Net developer, I was assigned to a critical
anti-money laundering IP solution on Java stack. Thanks to GitHub CoPilot's
code suggestions and context-aware completions, I was understanding the
existing codebase and writing Java code myself within a month. Now, I'm
contributing as productively as senior developers on the team.
- Suvarna, CGI Member Partner

13. © 2024 CGI Inc. 13
Path to unlocking productivity benefits
CGI using AI on own IP solutions to achieve the optimum
path to productivity improvements
Selection of AI tools
Bouquet of AI tools for different
phases of software delivery
Team composition
Expansion of middle talent
New way of working
Mindset change, process
change, reskilling
Measuring Productivity Improvement
Task, program, business levels
Legal and Security
Ensuring responsible AI practices, updating legal
contracts for AI assisted code generation
Intangible Benefits
More focus time for engineering talent thereby
improving innovation potential
Responsible
AI

14. © 2024 CGI Inc. 14
New “Ways of Working” to unlock productivity gains
Closer collaboration with
domain experts
Easy to generate code; more
important to clearly define
requirements and success
outcomes
Prompt Engineering
Developers learn to craft
effective prompts to guide AI
tools in generating code, tests,
and documentation.
Human Review
“Human in the loop” to ensure
code quality, security and
standards adherence to the
project goals
Effective AI Project Management requires embracing uncertainty and rapid adaptability

15. © 2024 CGI Inc. 15
Measuring Productivity Benefits by using AI tools
Task level productivity benefits
1 Reducing development time
Coding, bug fixing
2 Code quality improvement
Code complexity, duplication, adherence
to standards
3 Developer Productivity
Function points or user stories
completed
Program level improvements
• Reduction and more predictability in delivery schedules
• Decrease in technical debt
• Higher developer velocity leading to improved utilization
Business benefits
• Faster time to market for new features
• Improvement in customer satisfaction metrics
• Competitive advantage/ Business Agility

16. © 2024 CGI Inc. 16
Responsible AI
16

17. © 2024 CGI Inc. 17
Fairness and Inclusion Uphold Trust
Transparency Accountability
Guardrails for Responsible Use of AI
Data Process Technology People

18. © 2024 CGI Inc. 18
Will AI take over jobs?

19. © 2024 CGI Inc. 19
AI Won’t replace jobs- But people
who use AI well will replace
people who do not.