If you’re a data professional like me, you’re probably hearing “Enterprise AI” at least ten times a day. But let’s be honest, what does it really mean? And more importantly, what should we actually be doing about it?
Here’s something I’ve learned, often the hard way. Enterprise AI is fundamentally different from consumer AI because it’s built on messy, chaotic, and often siloed data. Consumer AI often benefits from data that’s public and widely available, which means it’s been seen, vetted, and scrutinized by a broad community. That doesn’t always make it clean or perfect, but it does give it a level of collective validation. Fair? Feel free to challenge that.
If you’re feeling overwhelmed, you’re not alone. I’m right there with you. This stuff is complex. Here’s how we can begin to make a dent in it:
1. Embrace (and Map) the Chaos
Start simple. Grab a whiteboard or open up a spreadsheet. Map out where your critical data lives. Who owns it? How accessible is it? You might be surprised, and probably frustrated, at what you find. Even just surfacing these patterns can create momentum for change.
I recently did this exercise myself, and it was eye opening. It revealed not just technical gaps but also cultural and organizational barriers. Even partial clarity on your data landscape can go a long way.
2. Build Trust Through Transparency
In consumer AI, trust comes easier because data sources are usually public and already vetted. In enterprises, trust has to be earned. Ever had someone question your insights because they didn’t trust your data? Same here.
One thing that helps: documenting data lineage. Making the process behind your data transparent and understandable gives others more confidence to use it. It’s not flashy work, but it pays off.
3. Unlock Those Legacy Systems (Slowly but Surely)
Legacy systems are the enterprise data leader’s perennial headache. Unlike consumer AI applications, we can’t just plug in modern AI to decades-old systems. Integration is complex, expensive, and culturally challenging.
The good news? You don’t have to fix everything overnight. Focus on integrations that can deliver small but meaningful wins. I’ve found that piloting AI in lower-risk areas can create the traction needed to make larger changes later.
Why This Work Matters
Enterprise AI isn’t about flashy tech. It’s about data clarity, trust, and thoughtful integration. These are not always glamorous problems, but they’re the ones that determine whether AI actually works in practice.
I don’t have all the answers yet and I’m figuring it out as I go. But I believe if we lean into these foundations bit by bit, we’ll move closer to meaningful impact. If you’re in the thick of it too, let’s connect and share what’s working. This is a journey, and we’re all still learning.
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