Verify AI Model Outputs with Caution

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

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

Where do you start with your AI strategy? 🤖 A robust data strategy, is where. If you have no effective data management - you aren’t ready to use AI. Similarly, if your digital strategy is old and technology stack isn’t up to scratch, then you need to look at getting the basics right before even thinking about enterprise wide AI implementation. Pascal BORNET takes us right to the very basics of AI strategy here 👇🏾 What are your thoughts?

I worry that the average person has very little understanding of this. I remember when friends believed something simply because it appeared in a magazine article. Then because it was in their social media feed. Now it appears with better language and more confidence - and it appears in multiple places. At a time when it is critical for people to think more critically, they are instead developing the habit of thinking less.

Absolutely. This is why data quality scoring and model accuracy audits are so critical in everything we do with our SLMs… it’s also why we built our own quantitative data collection methods using the latest people science, bc current methods are inadequate, qualitative, and research proves they are less than 15% accurate.

Couldn’t agree more...👌 Data quality is the real issue, not just model performance. AI is a powerful tool in our toolbox, but not everything!

🙋♂️AI is as intelligent as we are💡 It is just quicker, for better or worse. If accurate information is available, AI will find and utilise it. The same applies to bad data. It is intelligent in terms of analytics and productivity but cannot intrinsically assess the quality of data. 🤖AI will select the most popular and accessible information. In the social media and post-truth era, this doesn’t necessarily mean reliable or good quality information…it is sadly often otherwise! 🤡Moreover, generative AI is significantly contributing to creating fake and misleading content, feeding AI itself with poor data. Our intelligence (when available😒) is what makes us distinguish between what’s good or bad based on experience and reasoning, rather than popularity, availability, or, even worse, corporate-created algorithms 😈. Unfortunately, this intelligence is used less and less every day, as we delegate reasoning to these useful but dangerous tools. I believe AI is a great tool for producing content, summarising, and searching information, but if we don’t apply our judgement and knowledge, we risk passively accepting whatever AI assumes we need. And that could be a load of rubbish which we might end up believing!💩 #ai #artificialintelligence #realintelligence #socialintelligence #people #society #socialpsychology

This is the part of AI adoption that concerns me most right now. Companies are being pitched AI everywhere: AI sales agents AI automation AI-enabled CRMs AI follow-up AI forecasting. Some of it will be useful, but AI does not fix an ungoverned revenue system. It accelerates it. If leads are not captured consistently, AI will scale inconsistency. If qualification standards are unclear, AI will move weak opportunities faster. If CRM data is incomplete, AI will produce confident but unreliable guidance. If follow-up lacks ownership, AI will automate confusion. If pricing, scope, and handoff rules are loose, AI can help the company move faster into margin problems, delivery friction, and client churn. That is the hidden risk. The issue is not just bad data. It is bad system behavior being automated. This is one of the reasons I am building ARGen. ARGen looks at how opportunities are formed, qualified, developed, converted, handed off, delivered, and expanded. The purpose is to identify where risk enters the revenue generation and growth system, how it moves, and where it becomes visible before it becomes a larger business problem. AI will matter. But companies should be careful about automating a system they have not governed. Otherwise, AI does not become a growth engine. It becomes a faster way to scale the same breakdowns.

I couldn’t agree more‼️ Even before AI, we were already seeing “experts” shaped by surface-level Google knowledge rather than deep understanding. If inaccurate or unchecked data becomes part of AI training, it doesn’t just stay wrong: it will be reinforced and redistributed at speed. That should concern anyone who values the progress we’ve made in science, research, and knowledge. The responsibility to question, validate, and curate information has never been more important.

We’re obsessing over model intelligence while largely ignoring data integrity, and that tradeoff is going to catch up with us. A more powerful model doesn’t fix flawed inputs; it amplifies them with greater confidence and scale. That’s arguably more dangerous than a weaker system. AI can produce misleading information that isn’t always easy to detect. It may confidently state false facts, making errors hard to notice, especially in unfamiliar topics. It can also give outdated answers if its data isn’t current, which is risky in fast-changing fields like technology or health. Additionally, when uncertain, AI may “fill in gaps” by generating plausible but incorrect information, including fake details or sources. The uncomfortable truth: data quality work isn’t flashy, doesn’t demo well, and rarely gets prioritized, but it’s doing most of the heavy lifting when it comes to trust. Until organizations treat data governance as a first-class AI problem and not a back-office one, we’ll keep mistaking polished outputs for reliable ones.