AI is advancing rapidly, but in regulated industries it often runs into a hard stop when organizations cannot keep pace with compliance, governance, and risk management requirements. In this webinar, Seth Earley was joined by Vikal Kapoor and Fieran Mason-Blakley for a candid discussion about what it really takes to scale AI safely in environments like financial services and life sciences. The panel explored why so many AI initiatives stumble, from fragmented systems to opaque decision-making, and how a strong information architecture (IA) can turn those challenges into opportunities. Rather than treating compliance as a constraint, they emphasized how regulation can serve as a catalyst for building more transparent, trustworthy, and effective AI. The conversation highlighted practical lessons from real-world use cases, showing how organizations can shift from experimentation to enterprise scale. By structuring knowledge, governing metadata, and embedding traceability into every layer of AI, enterprises can move beyond pilot projects and deliver measurable value while staying compliant. Key Themes and Takeaways 1. The critical role of information architecture AI initiatives often fail because organizations lack the underlying information architecture needed to structure, govern, and contextualize data. Without this foundation, scaling AI in regulated industries becomes risky and unsustainable. 2. Compliance as a driver, not a barrier Rather than viewing compliance as a limitation, the panel emphasized using regulatory requirements as a framework for building more resilient and transparent AI systems. This helps organizations stay ahead of evolving rules while ensuring trust. 3. Responsible scaling of AI Scaling AI in regulated environments requires balancing innovation with control. Enterprises must adopt guardrails for data usage, model transparency, and bias management while still enabling agility and growth. 4. Organizational alignment and culture Successful AI adoption requires breaking down silos and fostering collaboration between business, compliance, and technology teams. Leadership must also invest in education and change management. 5. Practical steps to move forward The panel outlined pragmatic strategies, including: Establishing clear data taxonomies and governance frameworks Embedding compliance checkpoints in the AI lifecycle Prioritizing explainability and transparency in AI models Building pilot programs that demonstrate measurable business impact before scaling

1. www.earley.com
WEBINAR
WEBINAR
NoAIWithout IA:How Regulated Enterprises Can
ScaleAI Safely and Intelligently
Media Sponsor
SETH EARLEY
CEO & FOUNDER
EARLEY INFORMATION SCIENCE

2. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Today’s Panel
Seth Earley
CEO & Founder
Earley Information Science
781-820-8080
seth@earley.com
www.earley.com
www.earley.com
https://www.linkedin.com/in/sethearley
https://www.linkedin.com/
in/sethearley
Vikal Kapoor
Co-Founder & Partner
Seven Train Ventures
v@seventrainventures.com
www.seventrainventures.com
www.seventrainventures.com
https://www.linkedin.com/in/vikal/
https://www.linkedin.co
m/in/vikal/
Fieran Mason-Blakely
CEO
Leverage Analytics
fieranmason@leverageanalytics.ca
www.leverageanalytics.ca
www.leverageanalytics.ca
https://www.linkedin.com/in/fieran-mason/
https://www.linkedin.co
m/in/fieran-mason/

3. www.earley.com
Thank you to our media partners : CMSWire and VKTR
Before We Get Started
We are recording
Link to recording & slides
will be sent by email
after the webinar
Session will be 50 minutes
plus 10 minutes for Q&A
Use the Q&A box to
submit questions
Your input is valued
Participate in the polls
during the webinar
Feedback survey
afterward (~1.5 minutes)
3

4. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
What You'll Learn
The State of AI Initiatives
Challenges of Regulated Industries
Pillars of Compliant AI Systems
How RAG Enhances AI
How Information Architecture Addresses
Challenges
Metadata Taxonomy and Traceability
Practical Steps for Getting Started

5. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Earley Information Science is a
professional services firm
focusing on architecting and
organizing data – making it more
findable, usable, and valuable.
Our proven methodologies are designed
to address product data, content assets,
customer data, and corporate
knowledge bases. We deliver scalable
solutions to the world’s leading brands,
driving measurable business results.
We Make Information More
Useable, Findable, And Valuable.
1994
Year founded.
BOSTON
Headquartered.
20+
Specialists & growing.

6. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
What You'll Learn
1. The State of AI Initiatives
2. Challenges of Regulated Industries
3. Pillars of Compliant AI Systems
4. How RAG Enhances AI
5. How Information Architecture
Addresses Challenges
6. Metadata Taxonomy and
Traceability
7. Practical Steps for Getting Started

7. The State Of AI
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
https://fortune.com/2025/06/11/ai-companies-employee-fatigue-failures
'Al fatigue' is settling in as
companies' proofs of
concept increasingly fail.
https://workcs.com/blog/why-most-enterprise
Why Most Enterprise Al Projects
Fail - and the Patterns That
Actually Work
https://www.gartner.com/en/newsroom/press-releases/2024-07-29-
gartner-predicts-30-percent-of-generative-ai-projects-will-be-
abandoned-after-proof-of-concept-by-end-of-2025
Gartner Predicts 30% of Generative
Al Projects Will Be Abandoned After
Proof of Concept By End of 2025
42% of companies abandoned most of their AI initiatives
in 2025, a significant increase from 17% in 2024.
– March 14th, 2025
AI project failure rates are
on the rise: report
https://www.ciodive.com/news/AI-project-fail-data-SPGlobal/742590/

8. The State Of AI
https://economictimes.indiatimes.com/magazines/panache/mit-study-shatters-ai-hype-95-
of-generative-ai-projects-are-failing-sparking-tech-bubble-jitters/articleshow/123428252.cms
MIT study shatters AI hype: 95% of generative AI
projects are failing, sparking tech bubble jitters

9. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Poll
1. Vision and planning stages
2. Internal Proof-of-Concept (PoC) projects
3. In production (internally)
4. In production (customer facing)
5. N/A or none of the above (let us know in chat)
Where are you in your AI journey?

10. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Scaling AI in regulated industries is complex and risky due to:
Stringent
regulatory
requirements.
Data silos and
disconnected
systems.
Inconsistent content
and governance
structures.
Challenges Of Scaling AI In Regulated Industries

11. Common reasons AI fails in financial services:
Lack of traceability and audibility
(regulatory non-compliance).
113
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Why AI Fails In Highly-Regulated Sectors
Inaccurate data processing
leading to regulatory fines.
Data privacy breaches
(e.g., GDPR violations).
AI models trained on unverified or
siloed data, resulting in biased or
hallucinated outputs.

12. Navigating Compliance in Financial Services with AI Regulatory Requirements:
SEC Regulations: Financial
institutions must maintain
secure and accurate financial
records and be able to trace AI-
generated recommendations
back to source data.
GDPR: Requires clear consent,
data protection, and the ability
to explain AI-driven decisions
involving personal data.
Requires that individuals' rights
to rectification and erasure be
supported.
MiFID II: Mandates that firms
maintain comprehensive
records of investment
recommendations and
trades for transparency (2007
origination, revamp in 2018)
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Compliance Challenges For Financial Services Firms

13. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Key Pillars Of Compliant AI Systems
Traceability and
Audibility:
AI systems must maintain
a clear history of how
decisions were made.
Example: AI models used in
credit scoring must have
documented evidence of
how each variable
influenced the decision.
Contextual Awareness
and Accuracy:
AI must generate accurate
and context-aware outputs,
avoiding hallucinations.
Example: AI tools used in
financial risk analysis must
provide the correct risk
ratings for clients based on
valid, real-time data.
Scalability Across
Regulatory Landscapes:
AI systems must handle
different compliance needs
across multiple regions and
use cases, e.g., differing
GDPR requirements in the EU
versus CCPA in California.
Example: AI systems that
deal with both retail
banking and wealth
management must ensure
different compliance
standards are met.
These are all governance related issues and processes

14. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Confidentiality, Integrity and Local vs Public Models
Confidentiality:
Data uploaded to
ChatGPT may be used for
training
(Samsung example)
Protection confidential
data, including PII, health
data, trade secrets, and
financial data, can lead to
a variety of harms to users
of LLMs.
Integrity:
LLMs are probabilistic
Models do not consistently
produce the same output,
thus yielding results of
varying integrity
When correctness is critical,
probabilistic tools can be
problematic
The outputs of the models
have been shown to be
implicitly trusted, resulting in
a reduction in critical thinking
by users.
Local Models:
Local Models are models
operated within the confines
of your organization’s control
Local Models are not shared
with users outside of your
organization
Examples include
LLaMA 3, Mistral 7B, Gemma,
Private GPT, Nomic, and
Ollama

15. C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 15
Why Al Fails Without Governance
Al Isn't Smart Without Traceable Data
• Financial markets & banking data are complex,
high-risk, regulated
• Al errors often occur due to poorly tagged
Bloomberg/BVAL data or inconsistent loan info
• Three lines of defense ensure Al decisions are
compliant, auditable, high-value
KPIs:
of Bloomberg/BVAL data points with metadata
of loan origination Al decisions traceable
of retail Al outputs validated
Business Impact:
• Reduce trading errors & mispricing risk
• Compliant, faster lending decisions
• Trustworthy Al outputs in retail banking
• Markets
• Loans
• Retail Banking

16. © 2 0 25 E a rl e y I n for m a ti o n Sc i en c e, I nc . 16
What Is Retrieval-augmented Generation (RAG)?
LLMs used to process query and present results
Process query using
LLM to understand
user intent
Generate
Publicly available
information
• Prone to hallucination
• Exposure of IP
• Missing audit trail
Retrieve
Proprietary
information
• No exposure of IP
• Corporate knowledge
• Auditability
Process response
using LLM to provide
conversational format
User query
icon
Response

17. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
GENERAL RESPONSE VS. CONTEXTUALIZED RESPONSE
RAG Overview:
General Response:
• Provides broad, often generic answers.
• Lacks awareness of specific business context or
structured data (requires greater disambiguation).
• Relies on probabilistic outputs without validation.
• AI systems need structured taxonomies, metadata, and ontologies to deliver business-ready insights.
• Organizations that fail to invest in information architecture risk hallucinations, misinformation, and inefficiency.
AI without information architecture is unpredictable
Contextualized Response:
• Draws from structured, curated enterprise knowledge.
• Aligns with organizational taxonomy, governance, and compliance.
• Ensures accuracy, relevance, and trust in AI-generated insights.
17

18. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
How RAG Can Enhance Compliant AI
Combines AI-generated outputs with
external sources of truth (e.g.,
databases, regulatory documents) for
more accurate, explainable results.
RAG Overview:
RAG in Action for Financial Services:
Example 2: RAG models can be used to
cross-reference a client’s transaction history
against regulatory compliance rules (e.g.,
anti-money laundering).
Example 1: A wealth management firm uses
RAG to pull real-time market data and
historical trade records to provide compliant
investment recommendations.

19. 17
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Information Architecture-directed Rag Solution (IAD-RAG)
• Siloed data across systems with incompatible
data formats
• Poor quality content without metadata leads
to poor retrieval
• Compliance risks due to incorrect generated
responses
• Enterprise deployment impractical due to
knowledge debt
Challenges IAD-RAG
• Reference models using common terminology enable
integration
• Content processing using LLMs and methodologies as
prompts with expert oversight
• Data and knowledge provenance, rights, usage,
ownership and cross enterprise impact encoded as
metadata
• Componentized, tagged, processed content enables
create once, publish everywhere efficiency
Content and data models aligned with domain specific ontology

20. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
EIS Approach to an IA-Directed RAG Solution
Knowledge experts test and refine VIA results to ensure they align with enterprise
system requirements, data strategies, and user needs.
Analysis
• Content Inventory
• Content Purpose
• User Needs & Goals
• Context of Use
Organizing Principles
• Classification Systems
• Hierarchies
• Taxonomy
Content Models
• Content Types
• Fields & Attributes
• Relationships Among Content Types
• Rules
Ontologies
• Entities
• Attributes
• Relationships Among Entities
20

21. © 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Summary of Process
Multimodal Content
Processing
VIA processes &
enriches content,
creating core assets
Enriched Content
CMS Integration
Structured IA
Streamlined IA Creation Process
Accurate & Comprehensive IA
IA Creation
VIA revises core
concepts based on
your feedback
Data Engineering
VIA creates the IA pieces
you need to support
enterprise applications
Data
Sources
Upload Content
• Documents
• Business Glossary
• Product Information
Validate Core Concepts
• Content Inventory
• Use Cases
• Entities
• Ontology
Tune Information
Architecture Assets
• Iteratively refine the
foundations of your
information architecture.
Finalization
• Content Types
• Schemas
• Taxonomies
• Hierarchies
• OWL-Based Ontology
• CMS Creation
21

22. C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 22
Taxonomy: Standardizing Across Markets & Banking
Taxonomy ensures AI doesn't make errors
KPIs:
• % of trades, loans, and transactions mapped to taxonomy
• % reduction in misclassified Al outputs
• Alignment score across teams & audit
Business Value:
• Accurate risk reports, loan pricing, retail recommendations
• Faster onboarding & KYC approvals
• Consistent Al application across banking products
• Misclassification = errors & compliance headaches
• Taxonomy ensures Al interprets trading, lending,
retail consistently
AI

23. C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 23
Taxonomy: Standardizing Across Markets & Banking
Taxonomy ensures ai doesn't make errors
Business Value:
• Reduces MRAs / consent
orders
• Defensible Al decisions in
trading, lending, retail
• Builds regulator
confidence
KPIs:
• % of Al models/workflows
covered by governance
• % of governance breaches
per quarter
• Time to remediate audit
issues
• MiFID II / BVAL trade validation
• CCPA /customer data in retail
banking
• SOC 2/ISO 27001 for data storage
& processes
• Traceable, auditable decisions
Embeds regulatory
rules into Al:
Metadata
Taxonomy AI decision Audit trail

24. © 2 0 25 E a rl e y I n for m a ti o n Sc i en c e, I nc . 24
Poll
How would you characterize your
organization’s understanding of Retrieval
Augmented Generation (RAG)?
1. Not understood at all
2. High level understanding but not deployed
3. Executive awareness but lacking funding or urgency
4. Executives understand the criticality of RAG and it is a
priority with funding and buy in
5. N/A or none of the above
24

25. www.earley.com
Poll
25
1. We need foundational education for executives to get their
support
2. We have executive understanding but sufficient budget has
not been allocated
3. We have support (funding and buy in) but do not have the
expertise to build RAG applications
4. The org needs help on core data management before
building RAG
5. Our legacy environment is a major hurdle to RAG
6. Something else (let us know in chat)
How would you characterize your organization’s
readiness for Retrieval Augmented Generation
(RAG)?
(choose all that apply)

26. Bloomberg
Terminal
BVAL
Loan Files Customer
Records
C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 26
Metadata: The DNA of Financial Al
Metadata Enables Compliance & Clarity
• Tracks Bloomberg/BVAL, loan steps, retail transactions
• Creates a full audit trail for Al decisions
KPIs:
of market data, BVA valuations, loan records tagged
of Al decisions with clear lineage
• Time to respond to regulators
Business Impact:
• Fewer mispriced trades
• Reduces regulatory inquiries
• Clear, auditable Al decisions

27. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Metadata, Taxonomy, And Governance For Traceability
Regulation / Standard Primary Focus Key Requirements
Data Governance
Implications
Good Data
Practices Needed
CCPA (California
Consumer
Privacy Act)
MiFID II (EU Markets in
Financial Instruments
Directive II)
ISO 27001
(Information Security
Management)
SOC 2 (System
and Organization
Controls)
Consumer privacy &
data rights
Market transparency &
investor protection
Information Security
Management
System (ISMS)
Trust Service Criteria:
Security, Availability,
Processing Integrity,
Confidentiality, Privacy
• Right to know, delete, opt-out
• Data category mapping
• Non-discrimination in service
• Risk assessment & treatment
• Access control
• Asset management
• Continuous improvement
• Security controls
• Confidentiality &
privacy safeguards
• Continuous monitoring
Identify, classify, and track
all personal data tied to CA
residents; enable data
subject request workflows
Requires detailed
transactional records,
client suitability data, and
audit trails
Identify, classify, and protect
data assets with controls
mapped to risk
Data quality, access
restrictions, and process
integrity must be
demonstrable
• Comprehensive data
inventory with lineage
• Accurate metadata tagging
• Consent management
tracking with timestamps
• Standardized reference data
• Time-stamped order and
execution logs
• Controlled vocabularies
• Archival with integrity checks
• Up-to-date data
classification schema
• Role-based access policies
• Incident logs
• Formal change control
• Data accuracy controls
• Monitoring & logging of data
changes
• Immutable audit trails
• Encryption in transit & at rest
• Best execution
• Transaction reporting
• Suitability assessments
• 5–7 years data retention

28. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Metadata, Taxonomy, And Governance For Traceability
Regulation / Standard Primary Focus Key Requirements
Data Governance
Implications
Good Data
Practices Needed
CCPA (California
Consumer
Privacy Act)
MiFID II (EU Markets in
Financial Instruments
Directive II)
ISO 27001
(Information Security
Management)
SOC 2 (System
and Organization
Controls)
Consumer privacy &
data rights
Market transparency &
investor protection
Information Security
Management
System (ISMS)
Trust Service Criteria:
Security, Availability,
Processing Integrity,
Confidentiality, Privacy
• Right to know, delete, opt-out
• Data category mapping
• Non-discrimination in service
• Risk assessment & treatment
• Access control
• Asset management
• Continuous improvement
• Security controls
• Confidentiality &
privacy safeguards
• Continuous monitoring
Identify, classify, and track
all personal data tied to CA
residents; enable data
subject request workflows
Requires detailed
transactional records,
client suitability data, and
audit trails
Identify, classify, and protect
data assets with controls
mapped to risk
Data quality, access
restrictions, and process
integrity must be
demonstrable
• Comprehensive data
inventory with lineage
• Accurate metadata tagging
• Consent management
tracking with timestamps
• Standardized reference data
• Time-stamped order and
execution logs
• Controlled vocabularies
• Archival with integrity checks
• Up-to-date data
classification schema
• Role-based access policies
• Incident logs
• Formal change control
• Data accuracy controls
• Monitoring & logging of data
changes
• Immutable audit trails
• Encryption in transit & at rest
• Best execution
• Transaction reporting
• Suitability assessments
• 5–7 years data retention

29. Metadata, Taxonomy, And Governance For
Traceability
Metadata Management:
Helps track the source,
modifications, and purpose
of data used in AI models.
Example: In banking,
metadata can track the
changes in customer
account data and provide
a transparent history for
audit purposes.
Taxonomy:
Structuring data into a
defined classification
system to improve
consistency and accuracy.
Example: Using standardized
financial product categories
in taxonomy ensures that AI
can make decisions based
on consistent definitions,
reducing the risk of errors.
Governance Frameworks:
Ensures that data is used
responsibly and in
compliance with regulations.
Example: ISO 27001
compliance requires that
firms maintain strict controls
on how AI interacts with
sensitive financial data.
17
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .

30. Copyright ©2023 Earley Information Science, Inc. All Rights Reserved.
Using Metrics & KPIs to Focus Governance
Measuring here
(business outcomes)
Measuring here
(process indicators)
Enterprise Strategy
BusinessUnit Objectives
Internal Audit Scores
Mitigation Corrective Actions
BusinessProcesses Violations Compliance Rules
Realtime
Transactions
Digital Content
Working & Measuring
here (entity
relationships, data
accessibility) Historical
Data
Customer
Entity Data
Processes enable
objectives
L
I
N
K
A
G
E
Anomaly Detection
Mitigate AIRisk
Data supports a
process
Objectives align
with strategy
CCO/CISO: “How will this reduce
risk/compliance costs?”
Regulatory Breaches
Data/Content Scorecards
Process Scorecards
Outcome Scorecards
Transactions Entities Locations
Compliance Team: “How do I know that my customer details and their
transactions are legitimate?”

31. C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 31
Driving Business Value With Traceable Al
Al + Governance = Measurable Impact
• Faster, correct trade execution
• Automated, compliant loan approvals
• Accurate retail banking Al recommendations
KPIs:
• % reduction in trade errors/misvaluations
• % decrease in loan approval cycle time
• % of retail Al outputs validated
Business Value:
• Scalable Al across markets, lending, retail banking
• Reduced operational & compliance risk
• Turns Al into a competitive advantage

32. Practical Steps For Building The Foundation
Start Small with
Governance Frameworks:
Begin by implementing
basic metadata
management, taxonomy
creation, and data
governance policies.
Example: Focus on a small,
manageable set of financial
data (e.g., customer profiles
or transaction records) and
apply governance standards.
Create Clear Data Ownership:
Assign ownership of data
and ensure that the owners
are accountable for data
quality and compliance.
Example: Assign roles like
Data Stewards who are
responsible for ensuring the
data used in AI models is
accurate and compliant.
Iterate with Feedback:
Regularly audit AI outputs for
compliance and make
necessary adjustments.
Example: In the case of non-
compliant trade
recommendations, conduct
periodic reviews to ensure all
recommendations are
backed by fully compliant,
traceable data sources.
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .

33. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Poll
How well defined is your AI governance?
1. Not defined at all
2. Initial planning stages
3. In place on a functional level
4. Cross functional/enterprise level
5. None of the above

34. Copyright ©2023 Earley Information Science, Inc. All Rights Reserved.
www.earley.com
www.earley.com
Need for Infrastructure Governance
Organizations need to control AI development
Too many experiments without guardrails lead to risk exposure
By managing components of cloud deployments, the organization can control tools, platforms,
providers, skills, data sources and costs
EIS VIA Could Architecture Generator enables controls to be put into place at multiple levels
34

35. www.earley.com
Infrastructure Governance

36. www.earley.com

37. www.earley.com

38. www.earley.com

39. Enterprise Readiness:
Lack of
ownership
Legacy
systems
Poorly
structured
source
information
Where most get stuck
17
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .
Begin by inventorying
• Data sources
• Systems
• Ownership

40. Key Takeaways
Driving measurable business value
Fewer mispriced trades, faster loan decisions, stronger regulator confidence.
Traceable Al governance turns compliance into measurable business value
Al isn't smart without traceable data
Financial markets & banking data need full lineage.
Metadata is the DNA of financial Al
Enables compliance, clarity, and audit trails.
Taxonomy prevents misclassification
Standardization reduces errors and aligns teams.
Governance is the glue
Embeds regulatory rules (MiFID II, CCPA, SOC 2, ISO 27001).
C o p yr i gh t © S eve nT r a in V e nt ur es 2 0 2 5 , A l l R i g ht s R es er ve d . 40

41. Business Impact Of
Getting It Right
• Faster, safer decision-making
• Improved AI performance
• Reduced compliance risk
17
© 2 025 E a rl e y I n for m a ti o n Sc i en c e, I nc .

42. Vikal Kapoor, FinTech Executive • www.linkedin.in/com/vikal • August 27, 2025

43. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Today’s Panel
Seth Earley
CEO & Founder
Earley Information Science
781-820-8080
seth@earley.com
www.earley.com
www.earley.com
https://www.linkedin.com/in/sethearley
https://www.linkedin.com/
in/sethearley
Vikal Kapoor
Co-Founder & Partner
Seven Train Ventures
v@seventrainventures.com
www.seventrainventures.com
www.seventrainventures.com
https://www.linkedin.com/in/vikal/
https://www.linkedin.co
m/in/vikal/
Fieran Mason-Blakely
CEO
Leverage Analytics
fieranmason@leverageanalytics.ca
www.leverageanalytics.ca
www.leverageanalytics.ca
https://www.linkedin.com/in/fieran-mason/
https://www.linkedin.co
m/in/fieran-mason/

44. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Earley Information Science is a
professional services firm
focusing on architecting and
organizing data – making it more
findable, usable, and valuable.
Our proven methodologies are designed
to address product data, content assets,
customer data, and corporate
knowledge bases. We deliver scalable
solutions to the world’s leading brands,
driving measurable business results.
We Make Information More
Useable, Findable, And Valuable.
1994
Year founded.
BOSTON
Headquartered.
20+
Specialists & growing.

45. Copyright © 2025 Earley Information Science, Inc. All Rights Reserved.
Poll
What is your biggest concern
about Knowledge and AI?
1. Lack of knowledge repository
2. Missing understanding of connection of
Knowledge Engineering to AI Success
3. Poorly structured source information
4. Lack of maturity around knowledge processes
and engineering
5. Tendency of tech org to look at this strictly from
a technology perspective
6. Lack of buy in or commitment from the business
due to burden of knowledge curation
7. Something else
(check all that apply)

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Poll
How would you characterize your RAG results?
1. We have not deployed RAG
2. Unusable (lack of trust in results, little relevance
or accuracy)
3. Unpredictable (some accurate results, but
answers vary widely)
4. Somewhat trustworthy (many answers are
correct but still contain significant errors)
5. Answers are technically correct but do not help
users complete their task
6. Mostly producing consistent, accurate and
helpful results
7. Have not characterized our results yet
8. N/A or none of the above (tell us in Q&A tab)