This may be the most honest picture of generative AI. When AI is trained on flawed data, it does not just inherit the problem. It becomes a very efficient amplifier of it. That is the part too many people still underestimate. → bad data in → scalable inaccuracy out To me, this is one of the biggest blind spots in AI. People obsess over model quality. Far fewer ask whether the source material deserves that much amplification in the first place. Because scaling knowledge with AI also means scaling responsibility in data sourcing. Just saying. What do you think is the bigger risk right now: weak models, or bad data being amplified at machine speed? #AI #GenerativeAI #DataQuality #MachineLearning #Innovation #Technology #DigitalTrust #FutureOfWork Photo credits: Ralph
Pascal BORNET Spot on, Pascal. This "amplification loop" is precisely why we see so many Enterprise AI projects stall at the finish line. In critical infrastructure and industrial SCADA, "bad data" isn't just a content issue—it’s a safety risk. When a model amplifies noise from legacy sensors, the system doesn't just hallucinate- it freezes. To me, the biggest risk isn't just the data itself, but the lack of Forensic Certainty. Without an immutable audit trail to trace why a model made a specific decision, we are essentially scaling uncertainty at machine speed. Data integrity is the new perimeter.
Garbage in, hallucinations out. Pascal nailed the uncomfortable truth most companies still ignore. I’ve seen teams celebrate impressive AI demos only to watch them collapse in production because the underlying data was messy, outdated, or incomplete. One finance team spent weeks building a beautiful generative AI tool for reporting, until stakeholders realized half the outputs were based on incorrect legacy records. The project lost all credibility overnight. The real differentiator in 2026 isn’t just having generative AI. It’s having clean, structured, trustworthy data feeding it. Pascal BORNET is right: Data quality is now a strategic imperative, not a “nice to have.” Treat your data like the valuable asset it is, and your AI becomes reliable. Neglect it, and it becomes expensive noise. Question for the thread: What’s the biggest data quality issue holding back AI adoption in your organization right now?
Pascal BORNET - Hi Pascal, the framing itself may be where the real blind spot is. Weak models vs. bad data is a technical debate. The deeper risk is organizational: leaders deploying GenAI without ever auditing the judgment embedded in their data — the assumptions, biases, and shortcuts their teams encoded over years. Bad data is not just inaccurate. It is a record of past decisions taken under different contexts, by different people, with different incentives. AI does not just amplify the inaccuracy. It industrializes the legacy mindset. In my executive coaching work with C-suite leaders integrating AI, the pattern is consistent: companies that pause to ask “what business logic are we about to scale?” before deployment outperform those that obsess over model selection by a wide margin. The model is the engine. The data is the fuel. But the destination is set by leadership clarity — and that is the part most boards still underestimate. Thank you for the provocation. 🌵
The messier version is that bad data is not always obviously wrong. In enterprise workflows it’s often stale policy, undocumented exceptions, regional rules, or fields nobody owns anymore. The model looks confident because the data has structure, not because it has truth.
Bad data at human speed is a manageable problem. Bad data at machine speed is a systemic one. The focus on model benchmarks while ignoring source material quality is like optimizing an engine while leaving contaminated fuel in the tank. Scaling responsibility in data sourcing is not a technical challenge. It is a values and governance decision that most organizations are not having loudly enough yet.
AI slop feedback loop. Ultimately, 90% of Internet will be AI slop made from other AI slop: it's not x, it's y.
Well said. There is often an assumption that better models will solve accuracy issues, but stronger models trained on flawed signals can sometimes make the problem harder to detect, not easier.
Natural language turned bad data into something that sounds right, changing the risk profile. The next frontier is visible judgment, where every answer signals how much it should be trusted and why. There is a clear trade-off, because full transparency may reduce usage when sources are weak, yet hiding it erodes trust, so users need to see uncertainty.
Pascal, this is a crucial distinction. The "garbage in, garbage out" principle amplified by AI is a massive concern. 🤔 What frameworks or strategies are organizations implementing to ensure data quality and mitigate this "scalable inaccuracy"? #DataGovernance #AIEthics
Honestly, model quality gets blamed for a lot of data governance failures. We plugged LLMs into workflows where the reasoning was fine, but the source of truth was split across CRM notes, old PDFs, and one person’s spreadsheet. AI didn’t create the risk, it just made the ownership gap impossible to ignore.