Building AI That Actually Understands You
- Vishwanath Akuthota
- Apr 8
- 3 min read
Insights from Vishwanath Akuthota
It's Like Ordering a Custom Suit
We're hearing a lot about Artificial Intelligence (AI) transforming businesses. It promises to understand our customers better, predict trends, and make smarter decisions. But sometimes, these AI projects stumble and don't quite deliver on their potential. Why? Often, it boils down to how we handle the data that fuels these intelligent systems.
Think of it like this: imagine you want a custom-tailored suit.
The Traditional (and Often Frustrating) AI Approach:
Imagine walking into a tailor shop, and the tailor simply hands you a blank piece of paper and says, "Okay, tell me exactly how you want your suit. Every measurement, every stitch, every type of fabric, every button – you name it."
Now, you might know you want a suit for a business meeting. You might have a vague idea of the color and maybe even the overall style. But do you know the difference between a half-canvas and a full-canvas construction? Can you articulate the precise angle of the lapel you envision? Probably not!
You know what you need the suit to do (make you look professional and feel comfortable), but you lack the technical tailoring vocabulary to perfectly describe it. You might end up with a suit that doesn't quite fit right or doesn't have the features you actually needed, simply because you didn't know how to ask for them.
This is often what happens with AI projects. We expect business users – the "customers" in this scenario – to perfectly articulate their data needs in technical terms. They know their business inside and out, just like you know you need a suit for a meeting. But they often don't have the data engineering expertise to translate those business needs into precise data requirements.
The Customer-Friendly Data Engineering Approach: A Collaborative Fit
Now, imagine a different experience at the tailor. This time, the tailor greets you warmly and asks about the occasion for the suit, your personal style, and what you want to achieve with it. They observe your body type, feel different fabrics, and offer suggestions based on their expertise.
They might say, "For a business meeting, a navy blue suit with a classic cut would be a great choice. We can use a breathable wool blend for comfort. How about a two-button jacket with a notched lapel? And what kind of fit are you looking for – something slim or more relaxed?"
The tailor acts as a translator, bridging the gap between your general need and the specific details of the suit. They anticipate your needs based on their understanding of tailoring and your initial input. They take the burden of technical specification off your shoulders.
This is what "customer-friendly data engineering" is all about. Instead of expecting business users to be data architects, the AI team takes on the role of the expert tailor.
How This Benefits Everyone:
AI That Understands Your Business: Just like the well-crafted suit fits your body perfectly, AI built on customer-friendly data engineering truly aligns with how your business operates. It understands the nuances and complexities because the underlying data foundation reflects that reality.
Intuitive User Experience: When the "invisible work" of data preparation is done right, the AI tools become much easier to use. You don't get bombarded with confusing questions or irrelevant options, just like you wouldn't get asked about thread counts at the tailor if you just need a functional suit.
Meaningful AI Adoption: Ultimately, this approach leads to more successful AI initiatives. When the technology is built on a solid foundation that reflects actual business needs, it delivers real value and becomes an integral part of how the enterprise operates.
The Takeaway:
Building effective AI isn't just about fancy algorithms. It's about understanding the needs of the business users and translating those needs into a robust and relevant data foundation. By embracing customer-friendly data engineering, we shift the burden of technical articulation and create AI solutions that truly fit the enterprise, just like a perfectly tailored suit. It's about collaboration, anticipation, and ultimately, building AI that genuinely understands and serves its purpose.
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