DataTopics: All Things Data, AI & Tech

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How do you build an AI strategy that delivers value today while preparing your organization for what comes next?
In this episode of Datatopics, we sit down with Sophie Van Nevel, Director Data Products & AI at TVH, to explore how a global spare parts company approaches that challenge: with a clear strategic vision, close collaboration across the business and room to adapt as AI evolves.
Sophie explains how TVH maps AI opportunities across core business capabilities and involves department leaders in turning those opportunities into priorities. We discuss balancing visible results with the foundations needed to scale.
We also explore the human side of AI adoption, from TVH’s “Forwards with AI” program and hands-on transformation labs to the changing role of AI throughout the software development lifecycle. Transparency runs through the conversation: how do you help people experiment, share what they learn and understand the boundaries as tools and capabilities change?
A practical conversation for anyone working on enterprise AI strategy, adoption or enablement.
Enjoyed the episode? Share it with a colleague working on AI in their organization.

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Murilo and guest Vitale go through the latest data and AI news: DeepSeek's open coding harness where every capability is a plugin, running near-frontier models on your own MacBook, Claude watermarking its own text to meet the EU AI Act, the argument that the code is just the byproduct of understanding, and what Anthropic found when agents start working together.

Links and references

DeepSeek's coding harness

https://deepseek.com/harness/en/

How Claude's text watermark works (Anthropic)

https://www.anthropic.com/news/claude-text-watermark

Code is the byproduct

https://yagmin.com/blog/code-is-the-byproduct/

Anthropic on multi-agent systems

https://www.anthropic.com/research/multiagent-systems

ds4, running DeepSeek locally (antirez)

https://github.com/antirez/ds4

#datatopics #ai #datanews

Watch on YouTube: https://youtu.be/fippNXe70_M

Chapters

(0:00) Welcome and guest intro

(2:14) DeepSeek's coding harness, everything is a plugin

(9:24) Running frontier models on your own machine

(11:14) Claude watermarks its text, and the EU AI Act

(22:33) Is the code just a byproduct?

(30:18) Codex vs Claude, reverting to 4.8

(32:41) When agents work together, and collude

(42:57) Wrap-up

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Google still drives most search traffic, but the rules of discoverability are changing fast. Today we sit down with Cyril, a client data partner at Dataroots, to unpack what search engine optimization actually is, why the first results page captures the vast majority of clicks, and how small changes in content clarity can shift who finds you and when.

We get practical about classic SEO fundamentals: on-page SEO that makes services crystal clear for the right persona, off-page SEO signals like backlinks that build authority, and the technical SEO basics that quietly decide whether you rank at all. Page speed, mobile-first indexing, and user experience are not “nice to have” anymore, and we talk through how tools like Google Lighthouse can reveal what is slowing a site down and where to focus first.

Then we move into the 2026 reality of AI search and generative engine optimization (GEO). If ChatGPT or Google Gemini can answer a question instantly, traffic can drop, but trust can rise if your brand gets cited. We explain how to write content that LLMs can use: add a TLDR summary, include FAQ sections, publish structured documentation where it makes sense, and build real authority across the web through credible mentions. If you care about SEO keywords, AI citations, and turning discoverability into revenue through better funnel data, this one is for you.

Subscribe for more, share this with a teammate who owns your website, and leave a review if it helped. What part of SEO or GEO feels most confusing in your company right now?

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Trust collapses fast when a dashboard misleads or an AI agent learns from messy data. We dig into how data quality became business critical—and how to move from reactive fire drills to proactive systems—through real stories from clinical trials and large platforms where a single broken test could escalate to the C‑suite. With Stan and David, we map the shifts driving this moment: AI adoption, rising reliance on metrics, and the urgent need for shared definitions, lineage, and monitoring that let teams find root causes before customers feel the impact.

We get practical about agents that actually help. Instead of vague hype, we break down a low‑risk architecture for read‑only, metadata‑aware agents that handle repetitive, high‑leverage tasks: writing dbt documentation, proposing data tests, performing lineage‑driven root cause analysis, and auto‑drafting tickets with queries, diffs, and impact notes. We explain why integrated agents beat copy‑paste prompts, how to add guardrails that limit scope and permissions, and what human‑in‑the‑loop review should look like to build real trust without slowing the work.

Expect candid guidance on adoption and observability: two layers of visibility—agent behavior and data quality posture—help teams track costs, measure time to resolution, spot repeating incidents, and choose structural fixes. We also explore buy vs build as platforms begin embedding agent capabilities, and we share a clear starting path for any team: prioritize critical datasets, standardize KPIs and definitions, enable tests, and surface lineage so automation has the context it needs. By the end, you’ll have a blueprint to reduce firefighting, improve stakeholder confidence, and make your AI agents smarter by feeding them cleaner, governed data. If this resonates, follow the show, share with your data team, and leave a review with the one task you’d automate first.

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In our final episode of 2025, we sit down with Tim Van Erum to unpack what really stood out at Tech Expo Amsterdam.
Together we revisit the most misunderstood AI statistic of the year, exploring why the “95% of AI projects fail” headline is misleading and how hype versus reality played out across the conference. Tim shares why failure and experimentation are not setbacks but essential drivers of innovation, and we highlight Reddit’s Scaling Safety strategy as a powerful example of machine learning in action.
This candid conversation closes out the year with lessons on what AI truly delivered in 2025.