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Nearly every week, the headlines about AI are dominated by the news of the latest model. A few days ago, Meta announced its newest model called Muse Spark \u2013 its first under its revamped AI division. According to their internal benchmarking tests, the new model is competitive with leading rivals across several tasks.\u00a0<\/p>\n
However, each new model release reveals something counterintuitive: as more models flood the market, the more they become commodities. If that\u2019s the case, then the question becomes: what is the differentiator for businesses trying to adopt and scale AI?\u00a0<\/p>\n
The answer comes down to one word \u2013 trust.\u00a0<\/p>\n
Over time, the model that sits on your desk is going to matter less than the trusted, connected intelligence that feeds into it. I think of connected intelligence as curated data drawn from multiple, organized sources. As a result, an AI model can reason across all of the data at once rather than working from a single, incomplete picture.<\/p>\n
Here\u2019s another way to think of it: AI models are the cars we\u2019re driving and they\u2019re improving every day. However, data and intelligence are the navigation system \u2013 the difference between knowing you\u2019re moving and knowing where you\u2019re going. A basic GPS running on an outdated map might get you somewhere \u2013 but will it get you there reliably and quickly?\u00a0<\/p>\n
Maybe.<\/p>\n
But \u201cmaybe\u201d isn\u2019t good enough when it comes to high-stakes decisions \u2013 especially in financial services. We\u2019re talking about some of the world\u2019s most consequential decisions that impact people\u2019s ability to get a loan, receive affordable insurance, and keep their money safe from financial criminals. These models need a source of truth to reason on \u2013 otherwise, we\u2019re not only increasing the odds of poor outcomes, we\u2019re gambling with public trust precisely at a time when trust in institutions is in worldwide decline.<\/p>\n
NVIDIA CEO Jensen Huang made this point recently when he said, \u201cStructured data is the ground truth of AI.\u201d He was identifying what the industry has been slow to acknowledge: that a powerful model requires trusted data. And not all data earns that distinction.\u00a0<\/p>\n
Data needs to be organized, normalized, and calibrated against the way the world actually works. It\u2019s painstaking work and can\u2019t be done just by scraping the web, which is why organizations that marry the best models with this kind of connected intelligence will build trust. In addition, it will also ensure that decisions based on AI can be defensible to boards, regulators, customers, and shareholders.\u00a0<\/p>\n
The consequences of getting the data foundation wrong are already showing up. According to MIT, 95% of AI pilots are failing to deliver measurable impact. That\u2019s partially because the data foundation is too weak. More powerful models don\u2019t solve this problem \u2013 if anything, they make the consequences of producing a bad output harder to detect and more costly to reverse.\u00a0<\/p>\n
Read the news and the risks of a bad output are obvious: tariffs are reshaping global trade overnight, geopolitics are redrawing supply chains, extreme weather events are defying historical models, and cyberattacks are targeting critical infrastructure. As the World Economic Forum\u2019s Global Risks 2026 report makes clear, risks continue to spiral in scale, interconnectivity, and velocity.\u00a0<\/p>\n
For banks, insurers, and asset managers, this connectedness is not theoretical \u2013 it\u2019s the difference between being reactive to risk and getting ahead of it.\u00a0 In this era of Exponential Risk, the defining challenge is not only that threats are growing in magnitude \u2013 they are also growing in connectedness. For example, an extreme weather event that damages infrastructure could impact a critical supply chain node, which has a derivative impact on economic growth and credit. For a financial services company, using generic AI coupled with fragmented data cannot get you a defensible answer on how to assess those risks. However, connected intelligence \u2013 spanning different data sets on climate, credit, and compliance \u2013 can get you closer to an answer you can trust.\u00a0<\/p>\n
As more data sources are unified, a picture of risk emerges that is fuller, more precise, and more actionable than anything a siloed approach can produce. That\u2019s why companies that unite data from third parties alongside the data they own will be the ones who make better, faster decisions \u2013 and can defend those decisions when it counts.<\/p>\n
Over the last three years, the complexity and capability of models has drastically improved. However, it\u2019s time to start focusing on perfecting the intelligence behind them. These are not decisions reserved exclusively for engineers. They are for anyone serious about unlocking the true power of AI. Every organization deploying AI at scale needs to ask its data teams the same question it asks AI vendors: is this intelligence reliable, connected, and tested against real outcomes?<\/p>\n
Because the stakes go beyond revenue and growth, they also matter for anyone who\u2019s concerned about strengthening the institutional trust markets run on. Moody\u2019s was founded over a century ago on the conviction that markets function better when everyone has access to transparent, rigorous, and independent data and analysis. That is as true today as it was then, and AI doesn\u2019t change that principle \u2013 it just raises the cost of getting it wrong.<\/p>\n
The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.<\/p>\n
#Moodys #CEO #trust #problem #models #wont #fix<\/p>\n","protected":false},"excerpt":{"rendered":"
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