AI without architecture is just a fancy workaround
By Rafael Thomazelli – CEO at Verzel
Artificial intelligence is no longer just a promise, it's operational reality. According to McKinsey’s The State of AI in 2024 report, 55% of companies already use AI in at least one area of their business. Of those, 40% are seeing real results. But here’s the inconvenient truth: most still lack the software architecture needed to support that growth. That’s when the house comes crashing down.
It works fine in the POC, but doesn’t make it to production. The system becomes a bottleneck. The app is unstable, slow, and vulnerable. It’s not that AI fails, it’s that engineering was left out of the process.
AI without solid infrastructure, clean data, and a production-ready pipeline is just a fancy-looking experiment. And that doesn't scale in the real world.
To truly extract value, you have to rethink everything: architecture, data flow, infrastructure, and the operating model. It’s product engineering, not just a trained model.
The main challenges show up fast:
Scalability
Deep learning demands heavy processing. Without elastic architecture, load balancing, parallel computing, and orchestration with Kubernetes, the model becomes dead weight. According to Gartner, by 2025, 70% of AI projects will fail due to lack of scalability, not model flaws.
Security and privacy
AI consumes large volumes of sensitive data. End-to-end encryption, granular access control, and constant monitoring are not differentials — they’re the bare minimum. LGPD and GDPR aren’t optional.
Operational efficiency
POCs don’t pay the bills. For AI to function day to day, it needs clear pipelines, end-to-end automation, and robust tools like Kafka, MLflow, and TFX.
What actually works:
Modularity
It allows you to evolve models without affecting the rest of the system. It makes rollbacks, A/B tests, and deployments easier and safer.
Observability
Monitoring accuracy, latency, GPU usage, and anomalies isn’t luxury, it’s survival. Tools like Prometheus and Grafana help keep AI as a strategic asset, not an operational liability.
Data governance
Bad data leads to worse models. Versioning, anonymization, labeling, and validation must be part of the architecture from day one. Without it, your model just replicates noise.
MLOps
Without an AI-specific CI/CD pipeline, the model never reaches production reliably. Being able to produce, version, and deliver models efficiently is what separates real AI from mere hype.
At Verzel, AI is part of our product core. We work with microservices-based architecture, elastic infrastructure, and decoupled APIs.
We’ve implemented AI solutions in finance systems, educational platforms, public security, and loyalty programs.
And in all those cases, the standard remains: there’s no magic, only solid engineering. AI without architecture is just a fancy workaround. And sooner or later, you’ll pay the price.