Ben Fox

The struggle of tracing a single piece of bad data is real. Data lineage should be a clear paper trail, not a true crime investigation. Your data needs better tracking.

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“Bolt-on” security doesn’t work for AI. Most security programs were built around adding controls after systems exist. That breaks down fast with AI. When models touch data, workflows, users, and decisions simultaneously: You can’t secure them in isolation You can’t rely on legacy DLP or point tools And you can’t prioritize risk without full context AI security isn’t about adding another layer. We spoke to Shannon Murphy from TrendAI about understanding how assets, data, identity, and behavior connect in real time. Without context, security teams don’t just miss risk. They get overwhelmed by it. Full episode in the links below! #AISecurity #AIGovernance #CyberSecurity #SecurityLeadership #EnterpriseAI

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Palantir Foundry is often misunderstood, especially by teams seeing it for the first time. It’s not just a dashboard, and it’s not only for data scientists. And with the right approach, meaningful value doesn’t have to take months. In this carousel, we break down 3 common Foundry misconceptions we hear from new clients - and what’s actually true. Which of these have you run into in your work?

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AI is reshaping DAM. Are you ready? Most enterprise teams struggle with metadata chaos and manual workflows. But practical AI can automate tagging, normalize metadata, and surface hidden assets—without vendor lock-in or black-box promises. Imagine a DAM where AI helps your team find, govern, and use content faster, with fewer errors and less friction. Real-world results: 40% less manual tagging, 2x faster campaign launches, and measurable compliance gains. Curious how unbiased AI use cases can work for you? Explore our latest insights at The DAM Republic. Let’s make DAM smarter. Message us for a vendor-neutral perspective. 👋

Palantir Foundry powers AI-driven decisions for the world’s leading enterprises. In our demo, you’ll see how we connect global sales, logistics, and distributor data into a single Palantir Ontology — delivering real-time AI insights and smarter decisions. As a Palantir (Palantir Technologies) partner, N-iX guides enterprises through every step on their Foundry journey: from assessment to full-scale implementation. 👉 Watch the full demo and explore N-iX Palantir Foundry services: https://lnkd.in/dTQQaC75

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Scores that are difficult to understand quickly turn into scores that are difficult to trust. Most GTM teams don’t have a scoring problem, they have a confidence problem. So when a score shows up without context: 1. Reps ignore it 2. Managers override it 3. RevOps defends it 4. No one actually uses it Imagine your sales team asks, “Why is this hot?” And marketing says, “Because the model says so.” If it can’t be explained in plain language, it won’t be used in the field. Predictive AI-scores only works when teams can see the 'why': • Which signals mattered • Which buyers were involved • How engagement rolled up at the account or buying-team level If you can’t explore the data behind the score, it’s not intelligence, it’s a black box. Modern GTM teams need scoring that’s transparent, explainable and grounded in real buying behavior. At Leadspace, we believe trust is built when teams can explore their data, connect scores to buying teams, and clearly see what’s driving prioritization - enabling AI-scores to guide decisions instead of getting ignored. Clarity is what turns prediction into action. Leadspace’s Dynamic Data Intelligence solution offers that clarity. https://lnkd.in/gPDPhZ6w #AIScoring #RevOps #PredictiveScoring #DataTransparency #BuyingTeams #RevenueIntelligence #DynamicDataIntelligence

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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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