Videos

Propeller Consulting. Discusses governance, operating discipline, workforce adoption and ROI measurement as connected parts of scaling AI beyond experiments. That fits the article's maturity model because adoption is treated as changed work with owners and metrics, not as tool usage or workshop attendance.

World Wide Technology. Connects build-or-buy choices to business outcomes, workload placement, cloud economics, data sovereignty, security, infrastructure readiness and hybrid operating models. That makes it a useful strategic companion for deciding when to buy a tool, extend a platform, build a thin custom layer or own more of the deployment stack.

IBM Technology. Discusses the uneven "barbell" shape of AI coding performance, architecture ownership, agent orchestration, context limits, open-source versus proprietary tooling and why models can solve hard tasks while still failing ordinary engineering details. That supports the article's rule that tests and human review remain the shipping gate.

Tech Field Day. Shows private AI as layered infrastructure: controlled compute, isolated environments, Kubernetes, inference containers, model governance, self-service provisioning, GPU sharing and monitoring. That maps directly to the article's warning that privacy depends on deployment boundaries, logs, access and operations, not on the word "local."

CX Foundation. Breaks voice agents into the practical pipeline: speech recognition, language model, business-system APIs, text-to-speech and interruption handling. That gives the article's rollout framework a concrete technical foundation before readers choose Twilio, Retell, Vapi, LiveKit or another platform.

GOTO Conferences. Connects the EU AI Act to data quality, MLOps, documentation and post-deployment monitoring. That makes it a good companion for the article's SME governance baseline: the work starts with knowing the system, data, owner, purpose and controls, not with buying a compliance platform.

Y Combinator. Garry Tan walking through what computer use actually changes for the unautomatable long tail of software — legacy apps, internal portals, anything without an API. The framing here is exactly the article's "browser is the universal interface" argument, with a more business-realistic view of where it pays off first.

IBM Technology. Tighter focus on the two techniques teams most often confuse. Goes deeper on data freshness, source attribution, and the inference-time speed argument for fine-tuning. Worth watching if you are specifically trying to argue against an unnecessary fine-tune project.

IBM Technology. A clear whiteboard pass through all three techniques with their respective costs — retrieval latency, training compute and catastrophic forgetting, the limits of prompt-only solutions — and the combinations that actually make sense in production. The closing example of a legal AI system using all three is almost exactly the article's "when to combine" argument.

AI Engineer. Douwe Kiela led the original RAG paper at FAIR and now ships RAG into regulated enterprises. The talk is mostly about what stops working at scale — chunking strategies that don't survive 100k documents, "accuracy is table stakes, inaccuracy is the real problem," and why attribution and observability matter more than the embedding model. Good calibration before re-reading the article's eval and monitoring sections.