We get it. Gemini looks great! You’re excited about AI and all the recent evolutions. You might have already dipped your toes in with a Proof of Concept (POC). That’s a great first step. POCs help you test the waters, get buy-in, and maybe even secure some extra funding. But what’s next?
The Limitations of POCs
The goal of a POC is about proving something works, not about making it work in the real world. While valuable, always keep in mind that POC’s have some limitations.
- Limited Scope: POCs often tackle a single, isolated problem without considering how it impacts the whole system. It’s like testing a car on a race track – smooth sailing, but real roads are full of potholes.
- Data Quality Issues: Your POC might struggle with the chaos of real-life data. It’s often messy and inconsistent – far from the clean datasets used in a POC.
- Lack of Integration: Imagine building a fancy new kitchen without plumbing or electricity. It’s the same with POCs – they often exist in a vacuum, without connecting to your existing data and systems.
- Security Concerns: Data is the new gold, your new gold. POCs might not have the robust security measures needed to protect your data treasure chest.
- Scalability Issues: Scaling from a small POC to a full-blown production environment isn’t just flipping a switch; it requires careful, strategic planning.
Building a Strong Foundation for AI Success
Well, we talked about the limitations but what now? First things first. You need to start with the basics: a robust foundation.
- Data Excellence: Garbage in, Garbage out, right? Invest in high-quality data, organize it, and make it accessible. It’s like building a strong house – you need a solid base before you start adding floors.
- Robust Infrastructure: Forget about chasing the latest algorithm. It’s like trying to catch a squirrel – exhausting. Instead, focus on building a flexible data platform that adapts to new trends. Platforms like Vertex AI are a great starting point.
- The Right People: And finally, you need the right people. You need people that understand the business challenges. But you also need people to think about data, security, networking,… And have you thought about the legal aspect? A whole lot of talented people! You might find them at GC innovate. 😉
From POC to Production: Overcoming Key Challenges
Okay, you got a great POC and got the basics right. Then you can start looking at productionising your AI system. These are some of the questions that should arise
- Model Deployment: How do you deploy your AI system? It’s great that your AI model works on your notebook but can it serve 1000 people on a global scale?
- Security and Privacy: Remember the phrase: trust is good, control is better? It can sound quite toxic but when it comes to an AI system, you definitely need some control: Who has access to the underlying data or even the AI model?
- Quality and Explainability: Can you explain why your AI model is doing something? What’s the quality of the data now? What will the quality of the data be in the future? How do you ensure that the quality of your AI model doesn’t start drifting after a while?
Spoiler alert: this isn’t an exhaustive list. Transitioning from POC to production demands serious planning and execution. Remember what we said about the right people? Our experts at Agiliz & Tensr have crafted a 6-step approach to help organizations like yours move from POC to ROI.
Discover our 6-step approach and request your discovery call now!