Sources of Interest for “Enterprise Intelligence”

This is a list of references that at least roughly support the notions I present in my book, Enterprise Intelligence. If you’re not familiar with the book, here is a TL;DR.

I spend at least an hour every day attempting to keep up with the deluge of change in the AI/BI space. These are the ones I feel are good supplements to my book; and readily consumable (easy to understand) whenever possible.

Yes, this page reeks of confirmation bias … hahaha.

The headers for each entry below is the link to the resource (so click on the header).

How To Be Observant BETTER THAN Sherlock Holmes

Source: YouTube – Anthony Metivier
Date added to this list: August 29, 2025
Date created: July 30, 2020
Why it’s relevant: The Insight Space Graph and Tuple Correlation web I describe in Enterprise Intelligence is about enabling Sherlock Holmes style of reasoning. Sherlock Holmes talks about deducing, but the real power is in abduction. At about 10:45 the speaker mentions deduction, but that in the Holmes style, it’s a process of induction, abduction, and deduction.

Extracting Knowledge Graphs from Text with GPT 4o

Source: YouTube – Thu Vu
Date added to this list: June 1, 2025
Date created: May 28, 2025
Why it’s relevant: An Enterprise Knowledge Graph is the main object of Enterprise Intelligence. One of the primary enabling themes is that LLMs can create a pretty good draft of a knowledge graph across almost any domain. This video offers a very good example with Python and ChatGPT 4o.

Why Your Team is Probably Missing the AI Revolution

Source: YouTube – AI News & Strategy Daily – Nate B Jones
Date added to this list: April12, 2025
Date created: April 11, 2025
Why it’s relevant: Human teams should use AI as another team member, not as a tool to be 10x faster individually.

Alternative AI: Beyond ANNs, LLMs to Enhanced Graphs

Source: YouTube – Future AI Society
Date added to this list: February 21, 2025
Date created: February 20, 2025
Why it’s relevant: “Enhanced Graphs” sound much akin to the Enterprise Knowledge Graph I described in Enterprise Intelligence.

AI vs Brain: Brains do this Better

Source: YouTube – Future AI Society
Date added to this list: January 22, 2025
Date created: January 21, 2025
Why it’s relevant: If this is correct, it’s a very elegant way to describe the symbiotic relationship between LLMs and Knowledge Graphs.

AI for Drug Discovery – Joseph Loscalzo

Source: YouTube – Hariri Institute for Computing, Boston University
Date added to this list: November 26, 2024
Date created: November 21, 2023
Why it’s relevant: Joseph Loscalzo recognizes that drugs have very many effects beyond what it was originally targeted for. Some result in re-purposed drugs, some are side-effects.

The Future of Reasoning

Source: YouTube – Vsauce
Date added to this list: November 26, 2024
Date created: April 28, 2021
Why it’s relevant: Excellent explanation for theory of mind.

Does AI negate the need for taxonomy?

Source: YouTube – Ashleigh Faith
Date added to this list: September 14, 2024
Date created: October26, 2023
Why it’s relevant: Very good discussion of the symbiotic relationship of LLMs and taxonomies. LLMs can produce taxonomies, but LLMs can make mistakes.

“Humans in the Lead” ― AI is accelerating jobs, not taking them (yet) ― Localization and Translation

Source: YouTube – David Shapiro
Date added to this list: July 22, 2024
Date created: July 21, 2024
Why it’s relevant: I love the term, “Human in the Lead”, which is very much along the line of AI implemented in the Enterprise with BI as the spearhead. It’s a human-lead “mixture of human experts”–subject matter experts, MBA-like analysts, the DW/BI technical team, and extended teams of knowledge workers with highly specific, unique skillsets and experiences–spearheading analytics efforts with LLM/AI in the role of an army of precocious, tireless interns.

AI Is Not a Bubble (Why Goldman Sachs Is Wrong)

Source: YouTube – The AI Daily Brief: Artificial Intelligence News
Date added to this list: July 15, 2024
Date created: July 11, 2024
Why it’s relevant: This is a very good counterpoint to the argument that an AI Winter is coming (or the AI hype bubble is about to burst).

Microsoft CTO Kevin Scott — “The Exponentials Are Still There” but Bill Gates disagrees

Source: YouTube – David Shapiro
Date added to this list: July 15, 2024
Date created: July 14, 2024
Why it’s relevant: There is much talk right now about the AI summer yielding an AI winter. David’s argument is that at least right now, capability is slowing down, but commercial application is still greatly accelerating. Certainly, the AI capability curve will someday again point way up, but for now, I we might be rolling down into the “trough of disillusionment”.

Microsoft Fabric and Data Mesh – A Perfect Fit?

Source: YouTube – SQLBits
Date added to this list: July 5, 2024
Date created: June 23, 2024
Why it’s relevant: This is a nice, short description of data mesh and data fabric. I looked for a recent (posted only 11 days ago) video on data mesh to help “sanity check” how relevant it is today. Data mesh plays a big part in the book. The distributed “data product” approach is essential towards scaling up the reach BI to more corners of the enterprise – especially when the environment is quickly changing due to the impact of recent AI. I also depend on data mesh to take on the governance and cultural shift required for these big transformations.

6 Levels of Thinking Every Student Must Master

Source: YouTube – Justin Sung
Date added to this list: June 23, 2024
Date created: June 7, 2024
Why it’s relevant: In terms of the typical BI analyst, I think many were not encouraged to move to highest thinking level of creating. They discovered insights at the request of managers, but often didn’t participate in the strategic aspects. With AI in the mix (yeah, that guy who can do the simpler things for much less and never gets tired), it’s a critical time for BI analysts to level-up, insisting on playing predominantly in the high levels of Bloom’s Taxonomy. See my blog, Exploring the Higher Levels of Bloom’s Taxonomy

How to Save Money with Gemini Context Caching

Source: YouTube – Sam Witteveen
Date added to this list: June 21, 2024
Date created: June 20, 2024
Why it’s relevant: I just found it interesting that caching (caching of very large prompts) in LLM land is already an innovation. That’s because the vast majority of the talk about LLMs the past couple of years has been more data centers, GPUs, LLM parameters, and training data. Now that context windows are in the millions of tokens, processing a prompt can take significant time and cost. The relevance isn’t so much this new feature of Gemini-it’s more how it reminds me of the value of preserving compute in a way that’s reminiscent of how pre-aggregated OLAP is a caching mechanism that might make a system more complicated but when resources are constrained, that’s what you do.

Mundane to motivated: delegating with GAI

Source: YouTube – IBM Technology
Date added to this list: June 20, 2024
Date created: June 18, 2024
Why it’s relevant: The discussion is about how LLMs should be use in business as an assistant.

ChatGPT 4o vs Expert Analyst | Data Visualization: Who Does It Better?

Source: YouTube – Maven Analytics
Date added to this list: June 20, 2024
Date created: June 13, 2024
Why it’s relevant: This is a challenge between a BI analyst analyzing a data set in the traditional way (using PowerBI) vs. an analyst performing the same analysis with the same data using ChatGPT 4o as the BI tool.

Understanding How Vector Databases Work

Source: YouTube – The ML Tech Lead!
Date added to this list: June 20, 2024
Date created: May 1, 2024
Why it’s relevant: Vector databases play a large role in enabling the incorporation of private and/or updated data into an AI/BI system.

The Impact of Generative AI on Business Intelligence

Source: YouTube – IBM Technology
Date added to this list: June 20, 2024
Date created: May 9, 2024
Why it’s relevant: How generative AI affects three BI roles of data stewards, BI analysts, and line of business users (what I call knowledge workers in the book).

AI Progress Slowing Down

Source: YouTube – David Shapiro
Date added to this list: June 20, 2024
Date created: June 16, 2024
Why it’s relevant: David’s insight into the s-curve of this round of AI provides much food for thought towards when AGI/ASI will be achieved. It’s relevant to my book because I offer a path that considers whether or not AGI/ASI is achieved soon.

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