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🎯Enterprise AI Knowledge Leadership Session

The Governed Knowledge Layer:
The Foundation Enterprise AI Actually Needs

Most enterprises have deployed AI agents, yet many report that those agents fail or underperform in production. This session explores why and examines what it takes to build a governed knowledge layer that enables reliable, enterprise-scale AI.

15th July, 2026
10:00 AM PST
Zoom
Limited Seats
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Virtual Executive Session
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Date
15th July, 2026
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Time
10:00 AM PST
💻
Format
Virtual — Live + Q&A
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Availability
Limited seats remaining
72% of seats already claimed
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◆ Virtual Executive Session
◆ Live Architecture Demo
◆ Customer in Production
◆ Governed AI Knowledge
◆ Limited Seats
◆ Executive Q&A
◆ Governance & Risk
◆ 90-Day Proof Point
◆ Virtual Executive Session
◆ Live Architecture Demo
◆ Customer in Production
◆ Governed AI Knowledge
◆ Limited Seats
◆ Executive Q&A
◆ Governance & Risk
◆ 90-Day Proof Point
Why Attend

Strategic Clarity,
Not Another Product Pitch

01
Diagnose the Real Failure

Learn how to determine whether underperforming AI agents have a model problem, a workflow problem, or a knowledge problem. Most trace back to the third.

02
Knowledge vs. Retrieval

How a governed knowledge layer differs architecturally from enterprise search, RAG-based retrieval, and knowledge base tools. That distinction determines production success.

03
The SME Governance Loop

What human-in-the-loop validation requires operationally: who owns it, what the workflow looks like day to day, and how it scales without becoming a bottleneck.

04
Knowledge Attrition Risk

How to assess whether your existing knowledge infrastructure has the currency, consistency, and coverage to serve as trusted AI context. Or whether it carries the gaps causing agents to fail.

05
Customer in Production

Real-world perspectives from the Senior Director of Customer Experience at Yaskawa America Inc., who deployed Kiku Core across a complex manufacturing environment. What the knowledge gap looked like before, and what 90 days in actually produced.

06
90-Day Proof Point

A concrete path to deployment: what the architecture requires, what gets measured from week one, and what success looks like before any expansion conversation begins.

Featured Speakers

Voices That Lead
Enterprise AI Transformation

Session host and moderator
Sanjeeva-R-Inukollu-img.jpg
Sanjeeva R Inukollu
VP Solutions, Enterprise Architecture
CriticalRiver Inc.

Sanjeeva leads Enterprise Solutions and Architecture at CriticalRiver Inc., working with CIOs and senior technology leaders to design AI deployments that hold up in production. His focus is on the gap between AI that performs in pilots and AI that delivers at scale, and specifically on the knowledge infrastructure that determines which side of that gap an organization lands on.

Featured speaker
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Charles Karnavy
Product Lead
CriticalRiver Inc.

Charles leads product and governance at Kiku, where he designed the SME validation loop at the core of the governed knowledge architecture. His work focuses on the operational challenge of making human oversight scalable: building systems where subject-matter experts govern what AI agents know without becoming a bottleneck.

Customer speaker
Cris-Franco-img.jpg
Cris Franco
Senior Director of Customer Experience
Yaskawa America Inc.

Cris oversees support operations at Yaskawa America Inc. and has deployed Kiku Core in a complex manufacturing environment. His session covers what the knowledge gap looked like before deployment, what the SME governance loop requires from a team in practice, and what 90 days in looks like in actual numbers.

Session Agenda

60 Minutes. No Fluff.

Time Session / Topic Speaker
0 – 5 min Welcome and Housekeeping Sanjeeva R Inukollu
Host
5 – 15 min The Production Gap: Why Enterprise AI Agents Fail After Deployment
The model is not the problem. Unpacking why production failure rates have not improved despite rising AI investment — and what the correct diagnostic actually reveals.		 Sanjeeva R Inukollu
VP Solutions
15 – 28 min The Governed Knowledge Layer: Architecture, Governance Loop, and 90-Day Path
What a governed knowledge layer is, how the SME validation loop works in practice, what gets stored and what doesn’t, and what the onboarding sequence produces.		 Charles Karnavy
Product Lead
28 – 43 min In Practice: Running Kiku Core in a Manufacturing Enterprise
A candid panel conversation about what the knowledge infrastructure gap looked like, what the SME governance loop actually requires from a team, and what results look like at 90 days.		 Cris Franco, Sanjeeva R Inukollu
Customer Panel
43 – 55 min Live Q&A
Open discussion on business case, architecture, SME operations, data security, regulated environments, and knowledge attrition risk.		 All Speakers
Interactive
55 – 60 min Closing Remarks and Next Steps
Key takeaways, the ROI calculator, and the fastest way to evaluate fit for your specific environment.		 Sanjeeva R Inukollu
Host
Discussion Highlights

Key Takeaways From the Session

01 | THE PRODUCTION GAP
Why AI agents that work in pilots fail in production

An examination of how the knowledge organizations think their AI is acting on differs from the knowledge it can actually access, validate, and trust. No model upgrade resolves a retrieval problem rooted in what was never indexed.

02 | THE KNOWLEDGE LAYER
Governance as architecture, not a feature to add later

How a governed knowledge layer sits between existing systems and every AI agent that needs to act on knowledge. The closed feedback loop that captures resolution outcomes is what makes it defensible over time.

03 | SME OPERATIONS
What human validation actually requires day to day

How the SME governance loop is designed so experts make judgment calls rather than author, format, or publish content. Twenty minutes a day covers the validation load for a team of 20 agents.

04 | LIVE CUSTOMER CONVERSATION
Inside a real production deployment at Yaskawa America Inc.

An honest account of what the knowledge infrastructure gap looked like before deployment, whether the SME burden concern proved accurate in practice, and what escalation rate and handle time data look like after 90 days in production.

05 | EXECUTIVE Q&A
Live discussion on architecture, security, and ROI

Open discussion covering the most common enterprise questions: how this differs from Salesforce Agentforce and Microsoft Copilot, what data is actually stored, how regulated industries are handled, and what the realistic implementation timeline requires from IT.

Learning Objectives

What You Will Walk Away With

✓

A diagnostic framework for determining whether underperforming AI agents have a model problem, a workflow problem, or a knowledge problem. Apply it to your current environment before spending more on either of the first two.

✓

A readiness assessment for evaluating whether your existing knowledge infrastructure has the currency, consistency, and coverage required to serve as trusted AI context. Or whether it carries the decay and contradiction that cause agents to hallucinate or escalate unnecessarily.

✓

An architectural understanding of what a governed knowledge layer requires to implement: what the architecture looks like, what the SME governance loop involves operationally, and how it fits above your existing stack without requiring migration or rip-and-replace.

✓

A concrete 90-day proof-of-value path for deploying a governed knowledge layer in a customer support environment, including what gets measured from week one, what success looks like, and what expansion to other departments requires.

✓

Honest customer data from a manufacturing enterprise running the platform in production: escalation rate, average handle time, and SME query load. Enough to assess fit before any vendor conversation.

WHO THIS MATTERS TO

For Leaders Accountable for AI Performance

If you are accountable for operational performance, governance, and long-term IT strategy, these insights are especially relevant to your role.

👔 VP / SVP Customer Success
⚙️ Head of IT / ITSM
🎫 IT Operations Leaders
🏗️ Enterprise Architects
🚀 CIO / CTO
🛠️ Senior Support Engineers
Registration

Reserve Your Seat for July 15

60 minutes. No product walkthrough. A working session on the specific architecture and governance decisions that determine whether AI agents succeed in production.

✓Live session with customer Q&A
✓Replay available after the event
✓Webinar brief sent to all registrants
✓Access to the ROI calculator at kikulive.com
✓Focused discussion, no sales pitch

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    The Governed Knowledge Layer.
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    The Index You Build in Support Is the One Every Department Eventually Runs On

    Support is where ROI is fastest, the governance loop is easiest to demonstrate, and the proof point is most measurable. Join us July 15 to see what building it actually looks like.

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