AWS Summit London 2024 - Cognizant Partner Spotlight - Cognitive Architectures - v1.pdf

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L O N D O N | 2 4 A P R I L 2 0 2 4

Cognitive architecture for
Generative AI: The path to MVP
Phil Basford (he/him)
P T S 2 0 6
Senior Director – Data & AI
Cognizant UK&I Consulting

Cognizant and Generative AI
Opportunities and challenges
Cognitive architectures
Methodology
Customer case study
Agenda

Generative AI could create up to
$1 trillion in value by 2032
“New Work, New World” - Cognizant & Oxford Economics, 2024.

Cognizant quick to recognize the potential of
generative AI
Ravikumar S, Cognizant CEO
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

90% of jobs could be disrupted
in some way by Gen AI
“New Work, New World” - Cognizant & Oxford Economics, 2024.

Responsible AI
Cognizant advises businesses on using AI responsibly and safely
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
The possibilities with Generative AI are numerous; here are some examples:
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
AWS provides secure access to the widest range of FMs
Open Sources
LLMS
Use cases &
capabilities
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
• FMs can be fine-tuned on your
own data without sharing your
data with everyone
• Now GA in limited Regions
• Agents, Guardrails & Model Eval
• API + Batch based
consumption
• Pricing per token
Proprietary LLMS

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

Enterprise Knowledge Navigator: Data lakes
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
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

Enterprise Knowledge Navigator: Data lakes

Methodology

How to get started
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
Build an AI strategy

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

Customer case study

Automating audio
transcription at scale
Our customer
Provider of business and administrative support
services, working with global clients in complex and
highly regulated environments, including the legal
and financial services industries.
The challenge
• Reduce the time spent transcribing audio files for
clients
• Improve transcription accuracy vs existing
automation tools

Our approach
Created an Intelligent Document Processing
pipeline
• Custom ML models developed in Amazon
SageMaker
• Generative AI (Amazon Bedrock and Claude LLM)
to convert dictation commands into formatting
instructions
Built a bespoke user interface to allow for human
review of transcriptions, ensuring accuracy and
improving model performance over time
Automating audio transcription at scale
Business outcomes
Improved experience for customers
and employees
Accuracy rate of 92.5%
Increased speed – processing 8 hours
of audio in less than 30 minutes
Hear Williams Lea share their journey:
Theatre I, 3:15pm (AIM212)

Thank you!
Please complete the session
survey in the mobile app
Phil Basford
philip.basford@cognizant.com
philipbasford
linkedin.com/in/philipbasford

AWS Summit London 2024 - Cognizant Partner Spotlight - Cognitive Architectures - v1.pdf

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