The Mostly Unstructured Podcast

AI has to run on some definition of your business. That definition either comes pre-built inside a platform you license, or you build it yourself.

In part two of “What Your Business Means to AI,” Clay and Ed discuss solutions with pre-baked industry meaning versus a vendor-agnostic layer that sits over systems you already own. They discuss buy vs build decisions with higher stakes and provide several working examples. Walk away with three questions every leader should be able to answer about business definitions.

Topics covered

Questions this episode answers

This episode is for CIOs, CDOs, and operations leaders in insurance, financial services, government, and manufacturing who are choosing an AI platform.

Don't forget to catch up with part 1 and reach out to keymarkinc.com. Subscribe for more on enterprise content intelligence and AI in regulated industries.

What does an Agent Mesh need to understand about your business before agentic AI can make a reliable decision?

In this episode of The Mostly Unstructured Podcast, Ed and Clay explain how taxonomy, ontology, and knowledge graphs give Business AI a shared understanding of documents, rules, relationships, risk, approvals, and workflow. The discussion focuses on the meaning layer beneath AI orchestration: the business-specific context that determines how information should be interpreted and used.

Learn the difference between taxonomy, which defines what something is, and ontology — what it means within your organization and how it relates to other people, data, rules, and processes. A knowledge graph connects that model to your real customers, documents, transactions, and decisions.

In this episode:

- How taxonomy, ontology, hierarchy, and knowledge graphs differ
- Why document access and business context must work together
- How a shared meaning layer supports Agent Mesh and AI orchestration
- Where agentic AI can make errors when business rules are unclear
- What executives should define before using Business AI for decisions

#AgentMesh #Ontology #Taxonomy #AgenticAI #BusinessAI

No single AI platform is going to become the "one ring to rule them all". The enterprise AI stack is fragmented by nature, but an orchestration layer lets governed, durable agents work across the systems you already have.

On the Mostly Unstructured Podcast, KeyMark CMO Clay Tuten sits down with Chris McLaughlin, Chief Revenue Officer of Vertesia, to unpack why enterprises end up running six, seven, eight AI tools at once — and how an orchestration layer, agents that can actually reach your data, and content that's genuinely readable by an LLM turn that sprawl into work that gets done with humans in charge of decision making.

Content's value is in the intelligence it brings, regardless of what system it's found in. But there is a lot of enterprise content across many, many systems.

On the Mostly Unstructured Podcast, KeyMark CMO Clay Tuten sits down with Mike Askren, VP of Product at Hyland, on how document management and ECM are becoming an intelligence layer for agentic AI, and the right size and scale problems to tackle with agents.

Topics explored:

Subscribe for more AI talk on content intelligence, IDP, and agentic AI from the team at KeyMark, or reach out if anything caught your ear.

Timestamps:
00:00 – From storage to intelligence: the ECM shift
01:58 – What "unstructured content" really means
03:01 – Mike's role at Hyland and content federation
04:11 – The content-fueled agentic enterprise
06:45 – Why 70–90% of enterprise data goes untapped
08:03 – Agentic governance and context you can trust
09:25 – Human-in-the-loop feedback and coaching agents
10:22 – The control tower: monitoring and stopping agents
12:03 – Agents as digital employees
13:45 – Advice for CIOs under pressure
15:23 – Start small: the attainable win, not the moonshot
18:39 – Where the ROI actually hides
19:47 – Practical outcomes: claims, HR, government
21:03 – First steps into the intelligence layer
24:45 – From IDP to agentic automation to new workflows
27:19 – Slow down, ask questions

Data lakes remain sources of truth, but AI accesses data beyond what lives in a lake to acquire valuable metadata and semantic understanding.
 
In this episode of the Mostly Unstructured Podcast, KeyMark CMO Clay Tuten sits down with Josh Heller of Crushable.ai, to dismantle the myth that a lake has to be the final resting place of data.
Read the companion article for info on data at rest vs data in motion, and integration tactics: https://www.keymarkinc.com/have-the-best-practices-for-data-integration-changed/
 
TOPICS COVERED IN THIS EPISODE:
• What separates a data warehouse from a data lake?
• Why the semantic layer is the most underrated shift in enterprise AI right now?
• How AI vectorizes unstructured data — and why that changes the data-readiness conversation?
• The difference between access and storage and which matters more?
• How do "conversational data queries" replace legacy BI dashboards across every level of an org?

QUESTIONS THIS EPISODE ANSWERS:
• Is a data lake still necessary for enterprise AI?
• How do you operationalize data not in a lake?
• What is a semantic layer?
• How do you know when your data foundation is good enough to move forward with AI?
• What should enterprise leaders audit before starting an AI initiative?
• Why does agentic AI make data governance more important?

WHO THIS IS FOR:
CDOs, CIOs, and operations leaders looking to understand if they should double down on data lakes, or embrace MCP connectors. And, anyone evaluating enterprise AI, intelligent automation, or agentic AI during a fundamental shift in the understanding of data access.
 
Subscribe to the Mostly Unstructured Podcast for more conversations on enterprise AI, data readiness, and intelligent automation from the team at KeyMark.

Data governance is the foundation of enterprise AI. If your data is not AI-ready, your copilots, agents, and automations can return bad answers, expose risk, and make the wrong decisions faster.

In this episode of the Mostly Unstructured Podcast, Clay and Ed break down why enterprise AI success isn't just about model performance, but starts with data readiness, traceability, audit trails, validation, policy, and clear ownership across the business.

Read our KeyMark companion article:
https://www.keymarkinc.com/managing-a...

Topics explored in this episode:
• What data governance for AI actually means
• Why many AI failures start with governance failures
• How bad data, shadow AI, and weak controls create enterprise risk
• Why traceability, monitoring, auditing, and validation matter before agents make decisions
• How bias, compliance, privacy, and trust affect enterprise AI rollouts
• What CIOs, CDOs, IT leaders, operations leaders, and compliance teams should ask before scaling AI

In this episode, Clay and Ed address key AI questions:
• What is data governance for AI?
• Why is data governance important for enterprise AI?
• What makes enterprise data AI-ready?
• Who owns AI governance in an organization?
• How do you reduce AI risk without slowing innovation?
• How do you govern agentic AI responsibly?

If you are evaluating enterprise AI, agentic AI, intelligent document processing, or AI automation, this episode directs seekers in establishing smart AI beginnings with data governance for accurate data, and AI governance for output guardrails.

Enterprise LLMs: RAG vs Fine‑Tuning, IDP & Governance

In this episode of the Mostly Unstructured podcast, Ed and Clay discuss whether it’s better to train a domain‑specific LLM or leverage foundational models like ChatGPT, Gemini and Claude. They explain the trade‑offs between fine‑tuning and retrieval‑augmented generation (RAG), and why Intelligent Document Processing (IDP) is vital for turning unstructured data into usable context.

In this discussion, we cover:

For those thrown by the excessive acronyms, let's define:
LLM = Large Language Model
RAG = Retrieval‑Augmented Generation
IDP = Intelligent Document Processing.
 
For more insights on enterprise AI for data intelligence, visit our website and read our blog on training an LLM referenced in the episode.

Website: https://www.keymarkinc.com/
Blog: https://www.keymarkinc.com/how-to-tra...