First Comes Data: Why Governance is the Real Foundation of AI Success

“To be able to leverage large language models, first you have to have data that is well governed. If you cannot explain your databases in layman’s terms — what every element means and how it’s calculated — you won’t be able to leverage AI in today’s world."

‍Hannah Testani, CEO

‍See full conversation from our recent AI in Action webinar

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Artificial intelligence has become the buzzword of the decade. It’s being hailed as the key to efficiency, the end of human bottlenecks, and the future of every industry — especially logistics. But before any of that becomes true, there’s a quieter, more essential reality that every organization must face:

AI is only as smart as your data.

Before your company starts experimenting with AI, it needs something far less glamorous but infinitely more important — clean, structured, and governed data.

Dispelling the Myth of 'plug-and-play' AI

There’s a popular belief that AI can take messy data and make sense of it automatically. In practice, that’s not how it works. Large language models (LLMs) — the type of AI behind tools like ChatGPT — don’t replace the need for data structure. They amplify what’s already there.

“It’s not coming in and creating a new revolutionary product,” said Hannah. “It’s enabling you or your teams to be a lot more impactful — to do a thousand times what you could do before.”

That amplification can only happen if the system understands what the data means in the first place.

If your datasets are fragmented, inconsistent, or undocumented, even the best AI models will struggle. Or worse, they'll deliver inaccurate results with confidence.

Step one: governance.

So, what does it take to make your data AI-ready? The first step is governance: a system of rules, ownership, and accountability around data.

Hannah explained it this way:

“Step one, if you’re on your AI journey and want to take advantage of it as an organization, is to really think about what your databases look like today, and which ones it makes sense to create a data governance structure for from the beginning. Have data stewards who own and manage the data, and think through what the data means.”

That means assigning data stewards to maintain accuracy, standardizing terminology across systems, and documenting where each data field originates and how it’s calculated. If your organization can’t explain a data point to a business user, you’re not ready to explain it to an AI model.

Intelligent Audit's unique AI foundation

At Intelligent Audit, the emphasis on data governance began long before “AI in logistics” became a trend. With billions of shipments analyzed each year, our systems depend on consistent, verifiable data to power audits, analytics, and machine learning models. That's why we built out our own robust data science team. Their role, in the simplest of terms, is to make data usable. That team, led by renowned data science expert, Dr. Brian Pollack, has been at the epicenter of our innovation for years.

This firm foundation supports the AI tools now available to our customers, from InvoiceAI, which processes PDF invoices with near-perfect accuracy, to AI-Powered Anomaly Detection, which learns the behavioral patterns of shippers and carriers to detect fraud, glitches, and errors in real time.

Turning the chaos into digestable clarity

Good data governance doesn’t stop at documentation. It extends to ongoing data cleansing. Intelligent Audit uses both data science and algorithmic tools to keep its information ecosystem clean.

As Hannah shared:

“We also use some non-data-science methods for data cleansing. The Levenshtein algorithm, for example, removes vowels, cleanses the data, and puts it back in — great for normalizing names, addresses, and company identifiers.”

That combination of process and automation allows Intelligent Audit to maintain integrity across billions of data points — from carrier invoices and shipping manifests to rate tables and customs data — ensuring that AI models learn from truth, not noise.

The payoff: AI that actually works

When organizations invest in governance, something remarkable happens: AI tools begin to work as promised. Predictive models become more accurate. Invoice automation achieves higher precision than manual processing. AI-Powered Anomaly Detection goes deep into shipping data to catch subtle shifts before they become crises, all without a single pre-configured "rule."

“Once you can live in that world,” said Hannah, “you’ll be able to leverage large language models on top of it — and that’s when the magic happens.”

In other words, governance isn’t a roadblock to AI — it’s the on-ramp.

It’s what transforms AI from buzzword to business value.

TL;DR: the short list to remember

• AI success begins with disciplined data. Governance, documentation, and accountability are the real prerequisites for intelligent automation.
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• LLMs amplify, not repair. Large language models supercharge productivity, but only if the underlying data is consistent and well-defined.
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• Clean data enables innovation. Machine learning can ignite technological revolution when it’s learning from high-quality, standardized information.
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• Governance unlocks trust. With governed data, you can trust every insight AI produces.

One last thing...

Every AI breakthrough starts with a spreadsheet, a schema, or a set of data fields that someone took the time to organize and understand. It may not be the glamorous side of innovation, but it’s the one that determines whether your AI strategy thrives or fails.

At Intelligent Audit, the equation is simple: Governance + Data Quality = AI That Delivers Real Results.

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