AIM102-S_Cognizant_CognizantCognitive

S P O N S O R E D B Y C O G N I Z A N T
Cognizant cognitive architecture
for generative AI: The path to MVP
Naveen Sharma
A I M 1 0 2 - S
Global Head of AI&A
Cognizant
Phil Basford Nick Morgan
Inawisdom CTO
Cognizant
CTO
Williams Lea

Speakers
Global Head of AI&A
(he/him)
Cognizant
Naveen Sharma
Inawisdom CTO
(he/him)
Cognizant
Phil Basford
CTO
(he/him)
Williams Lea
Nick Morgan

Agenda
Cognizant and generative AI
Opportunities and challenges
Cognitive architectures
Methodology
Williams Lea case study

Ravikumar S, Cognizant CEO
Cognizant quick to recognize the potential of
generative AI
Generative AI is a natural evolution of our work
across cognitive AI, enterprise applications, and
data and analytics services. We expect
Cognizant to be a principal beneficiary of the
Gen AI opportunity.
• 3K+ generative AI ideas in pipeline through Cognizant’s
Bluebolt innovation movement
• 3 dedicated generative AI innovation hubs across the
USA, UK, and India
• 200 active Cognitive and generative AI engagements at
various stages; 100s more projects using AI services
within delivery
• Early adopters of Amazon Bedrock, Amazon Titan, and
Amazon CodeWhisperer
Already equipped with the capability to deliver . . .
Industry and
operations
Large-scale business process
and technology operations
already leveraging AI
AI and data
Mature artificial
intelligence and
analytics practices
Engineering
Broad engineering capability
to implement COTS
solutions with generative
features and build custom
generative apps
“
and investing $1B to accelerate
“

Cognizant is an AI partner that can implement for
the enterprise
Enterprise-scale generative AI
Realize value
With a human-centered perspective, we
design, engineer, and evolve modern
software products that accelerate
business performance
Create models
Full-stack, modern engineering
capabilities with industry context for
front-to-back custom application
development and core modernization
Prepare platforms
We simplify, modernize, and automate
businesses to reduce overhead and
unlock the ability to accelerate value
creation through innovation
Modern architecture for
business agility
Use platforms to deliver
automation-first
Create solutions with
industry focus
Deliver to meet
business outcomes
Create solutions at
enterprise scope
Gain the advantages of
global distribution
15K+ AI specialists 10+ accelerators
15+ global delivery hubs
650+ clients 850+ data scientists
From aggregating vast points of data to building sophisticated AI models, our AI practices are deploying the future of tailored generative AI in enterprises today
Top investment areas: AI labs | Generative AI partners | Harnessing IPs | Talent upskilling Key IPs: Cognizant Neuro AI | Cognizant Neuro IT Operations

Where clients are finding generative AI useful
Generative AI can perform a range of knowledge work tasks instantaneously at a level of quality that matches or exceeds human performance
Because it is unlike traditional computation, generative AI can address completely new categories of digitization
• Summarize texts
• Synthesize information
• Extrapolate trends
• Annotate data
• Edit content
• Translate languages
• Answer queries
• Tutor subjects
• Analyze data
• Moderate content
• Recommend items
• Identify sentiment
• Predict text
• Correct mistakes
• Generate narratives
• Categorize information
• Detect patterns
• Simulate conversations
• Compose writings
• Understand context
• Inconsistency – results are inconsistent in unpredictable ways
• Overconfidence – cannot directly assess certainty
• Sycophancy – will flatter and agree with the user
• Calculation – cannot process mathematical tasks
• Memory size – can only work with very limited context
• Hallucination – invents factually incorrect answers
• Domain knowledge – trained by default on public data
• Explainability – cannot describe internal thought process
• Morality – struggles to evaluate “least worst case” topics
• Bias – can react negatively to less common scenarios
Hybrid systems produce the best results
Generative AI
Automated
knowledge work
Software engineering
Digital experience and
procedure execution
Human-in-the-loop
Collaborate and
supervise
Information infrastructure
Domain knowledge, context,
and memory
Evolutionary AI
Calculation, prediction,
and optimization
Connected devices
Sensing, visualization,
and actuation
Capabilities Limitations

Responsible AI
C O G N I Z A N T A D V I S E S B U S I N E S S E S O N U S I N G A I R E S P O N S I B L Y A N D S A F E L Y
Cognizant SMEs Cognitive architectures
Security and privacy
Ownership, copyright,
and accountability
Bias, fairness, and inclusivity
Governance and control
Ethics and regulation
Economic and social impact
Environmental and
sustainability
Explainability and
transparency

Uses of generative AI
T H E P O S S I B I L I T I E S W I T H G E N E R A T I V E A I A R E N U M E R O U S ; H E R E A R E S O M E E X A M P L E S
Enterprise Knowledge
Navigator
The ability to search a large
amount of data content
Development
Lifecycle Navigator
Help developers focus
on business logic
Customer Experience
Navigator
The ability to generate
hyperpersonalized
experiences
Generative Process
Optimizer
The automation of
routine tasks
From over 150 client engagements and ~400 unique generative application ideas, we developed 4 repeatable
offerings that we call cognitive architectures that met the needs of clients to accelerate their time to value

Foundational models on AWS
A W S P R O V I D E S S E C U R E A C C E S S T O T H E W I D E S T R A N G E O F F M S
• Generation of
unique, realistic,
and high-quality
images, art,
logos, and
designs
• LM for
conversations,
question
answering, and
workflow
automation
systems
• Multilingual
LLMs for text
generation in
Spanish, French,
German,
Portuguese,
Italian, and
Dutch
• Text
summarization,
generation,
classification,
open-ended
Q&A, and search
• Built 20 years of
experience
• Text generation
model for
business
applications and
embeddings
model for search,
clustering, or
classification in
100+ languages
• Repository of
open source LLM
and GPT models
• Most models use
transferred
learning to
refine models
• Optimized
Docker images
and framework
for distributed
training
Use cases and
capabilities
Sourced from AWS
Amazon
SageMaker
Flagship service
• A full service for
machine learning
• API or batch consumption
• Pay per min/hour pricing
• SageMaker has access to latest
hardware, including Inf2 and Trn1
• Cognizant has access to a wide
range of FMs (proprietary and
open source)
• Cognizant has worked with
AWS at becoming specialists
in distributed training,
initially using Hugging Face
Amazon
Bedrock
New service
• Managed service for
proprietary FMs
• Proprietary FMs require
EULA with FM author
• NEW: agents for LangChain
• FMs can be fine-tuned on your
own data without sharing your
data with everyone
• Now GA in limited Regions
• API-based
consumption
• Pricing per token

Amazon ECR
User Experience
Cognitive
DynamoDB
Amazon
Bedrock
Endpoint
Amazon Bedrock
Management)

Enterprise Knowledge Navigator
“Please give me the current share prices
for 10 best performing FinTech
companies in the past 5 years and
summarize their performance”
Advance search/QA
The ability to search inside private documents,
images, or websites to find related content
and then return that content
Retrieval-augmented generation
Integrations with live systems to augment the
results with up-to-date information or
perform actions that may be required
Security and privacy
Private FMs are not like internet SaaS products;
your data is not shared and is kept securely

Enterprise Knowledge Navigator
(LangChain)
FAQs

Enterprise Knowledge Navigator: Data lakes
The ability to help the business user to interact
with their data lakes and produce insights
Benefits
• Quick access of data to explore key insights or
generate new insights from the data lake; no SQL
expertise needed in writing a good SQL
• ~60%–70% productivity gain; ask question in
natural language and let generative AI (FMs) do the
rest of work in generating insights for you
Conversational interface
Providing a simple interface that allows the
business users to speak/chat in plain English
using domain-specific phrases
Code and domain understanding
Creating domain-specific code to retrieve
information contained within data products
within a data mesh
Outcome playback
Generation of reports or a playback,
containing generated graphics and text
summarizing the result

Enterprise Knowledge Navigator: Data lakes
AWS Glue
Data Catalog
Amazon RDS
Run
Run SQL on
Summarize

Methodology

How to get started
B U I L D A N A I S T R A T E G Y
Enablement Provide both business and technical enablement to teams to better understand generative AI
and the impacts it can have
Ideation Bring the business and IT together with industry SMEs from Cognizant to inspire big picture
thinking and creation of a vision for AI and concepts for use cases
Policy Construct an AI policy on the usage of AI, including what is prohibited and what is not
Scoring Down-select and prioritize concepts by scoring them in terms of business value and
complexity to deliver
Roadmap Take the scored concepts and design a roadmap that delivers the AI vision in accordance with the
AI policy; unlock incremental value with incremental investment at every turn

How to get started
A N D I M P L E M E N T I T
Essential
controls
From the AI policy, implement the essential controls needed to initially start applying
the roadmap
Discover Validate concepts on the roadmap by creating the business case, likely return on investment (ROI),
and success factors, including looking at the feasibility of AI for the concept, running an EDA, and
checking the data readiness
Prove Rapidly prototype validated concepts and prove the value they can bring a business before
further investment; use the latest foundational models, AWS, and Cognitive Pro
Embed Create a pilot embedded within a business so that success factors can be judged before
full productionization
Adoption Transform a system or business process, or evolve an operating model to allow for generative AI
to reach its potential adoption and rollout
. . . then scale it

Why Cognizant for generative AI?
Industry knowledge Governance and reasonability Deep technical knowledge
v The ability to contextualize generative
AI to an industry
v Understanding and experience of
challenges and common friction points
v Awareness of industry direction over
the next 5 to 10 years
v AI policy creation and advice
v Knowledge of regulation and
compliance
v Knowledge of ESG and the human
impacts of AI
v Experts in prompt engineering,
fine-tuning, and foundational
model customization
v The ability to leverage foundational
models from AWS and Cognizant's
Cognitive Pro for prototyping
and LLM ops
Product centricity
v The creation or evolution of user experiences
v The ability to manage product lifecycle and
launch products
Business readiness
v The ability to create solutions that embed
generative AI in business process and evolve
operating models
v The ability to advise on readiness for generative
AI, and how to evolve legacy technology

Williams Lea case study

The realities of Generative AI
Turning our vision into a plan… and our plan into results
A little about us
Our journey
Our challenges
Our outcome
What we learned

For context… a little about us

We provide skilled business support services
Global Leader Our Global Presence
2,600
APAC & INDIA
1,800
EMEA
2,500
AMERICAS
c. 6,900
(employees)
Flexible delivery models spanning on-site, onshore and
offshore to meet client requirements
Truly global operating model driven by
regional hubs
Our clients are leading Global Law Firms, Financial
Services & Professional Services
Regional Offices
Onshore Production Offshore Production
Regional Headquarters
Global Headquarters
Columbus
Melbourne
Chennai
Cochin
Wheeling
New York –
Global HQ Hong Kong
Columbus
Second US onshore
center (since 2019)
C.200 employees
Wheeling
First US onshore center
(since 2005)
C.700 employees
London
Leeds
Leeds
European onshore
center (since 2009)
C.150 employees
Singapore
Tokyo
Beijing
Chennai & Cochin
Offshore delivery centers
C.1,500 employees

Our services
L E A D I N G T E C H N O L O G Y - E N A B L E D G L O B A L B U S I N E S S S U P P O R T S E R V I C E S T O F I N A N C I A L , L E G A L &
P R O F E S S I O N A L S E R V I C E S F I R M S
Support Services: Delivered through onsite, onshore, offshore and hybrid models
• Digital workflow &
Reporting Dashboard
(ENGAGE)
• Digital mailroom
• Digital solutions
(Billing optimization,
graphics database)
Technology &
Other services
• Presentation services
(High-volume PPT)
• Pitchbooks
• Creative design (marketing
materials, website, PPT/Word
Templates)
• Digital & social branding
• CRM & content
management
Presentation &
creative services
• Document processing
• Administrative support
• BIS (Research, Analytics &
Management Reporting)
• Marketing & BD support
• Finance (Billing, AP/AR &
Compliance)
• Translation services
Business
services
• Print & reprographics
• Mailroom & distribution
• Front of house & hospitality
• Reception & conference center
• Records management & data
governance
• Facility management
Office
services
Strong and proven expertise in growing a tech-enabled business, with opportunities to add further value-added services

Our journey
Educated
and excited
the business
Formed a
POC team
Failed quickly and
pivoted even faster
Quickly built internal
capabilities, brought
revenue generating
products to market
Partnered with
nimble AI providers
Started with
an AI vision
Established a nimble process
to drive decisions

Our AI vision
It begged the questions…
It was bold
Imbed AI and LLMs into
select services to improve
efficiency
Quickly understand how AI
will impact our business and
how we can create value and
new revenue streams with
minimal investment
Which services?
Which technologies?
Build, buy or partner?
It was broad

Creating and executing our plan

EDUCATE EXCITE EMPOWER
First step
• Policies and best use
• Infused AI into our
corporate lexicon
• “AIm for the STARS”
contest to crowd source
ideas at employee level
• Put real dollars on
the table
• Create a funnel process
for ideas
• Create a small and
agile cross-functional
POC team
• Active executive
sponsorship

POC team principles
Listen to your
clients
Make
decisions early
Stress test
benefits with
operations
Continually
review benefits’
case post go-live
Limit focus to
just a few
concepts
Use data/
evidence to
drive decisions Set a baseline
Listen to your
clients

POC success drivers
Celebrate
quick wins
Focus on your
expertise (clients,
sectors, use cases)
and leverage
external resources
and experts to
fill gaps
Over-
communicate
progress and
results
Fail quickly Don’t be afraid to
change direction

Challenges along the way
Accessing
technical know-
how in a rapidly
changing
environment
Speed-to-
market
Resource
availability
Client
adoption/
understanding
Commercials
Finding the
right use case

Partner criteria
Framework
for moving to a
proof of value
AI expertise Access to
highly skilled
resources
Cultural alignment

Outcome
Six new tech-enabled services
launched in 18 months
Four in development with
target launches in Q4 2023
and Q1 2024
Healthy pipeline of additional
new services and continued
enhancements to existing
services
Increased operational
efficiency
New revenue generating
services released
Enhanced innovation and
expert credentials with clients
Increased margin and
profitability

Key learnings
Make it a business priority
Create cross functional teams that
understand the client, the market, the
business process and the tech
Partner with specialists to bolster
internal capability
Empower the team to make decisions
Let data guide your decisions
Frequent check-ins and phase gates to stop
failing prototypes early
Keep clients in the market test and
feedback loop

Thank you!
Please complete the session
survey in the mobile app
Thank you!
Please complete the session
survey in the mobile app
Naveen Sharma
naveen.sharma@cognizant.com
Phil Basford Nick Morgan
philip.basford@cognizant.com
philipbasford
linkedin.com/in/philipbasford
nick.morgan@williamslea.com

AIM102-S_Cognizant_CognizantCognitive

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