Why Infrastructure Determines Success or Failure
You probably know the situation: The CEO comes back excited from the latest AI presentation. “We need a chatbot like that too!” is the message. The marketing team dreams of automated content generation. And you, as the person responsible for IT? You ask the truly crucial question: “Will that even run reliably on our current infrastructure?”
A valid concern. While the standard deployment of tools like ChatGPT or Microsoft Copilot is often quite straightforward, things get complicated quickly with custom AI solutions. The stumbling block? More often than not, it’s the existing IT infrastructure.
The reason is clear: AI applications have completely different demands compared to classic software systems. While an ERP system handles structured transactions, an AI system works with huge amounts of unstructured data—often in real time.
To put it bluntly: The IT landscape that has performed flawlessly up to now often reaches its limits with AI workloads. That doesn’t mean the architecture is bad—it’s just that new rules apply.
According to a recent Bitkom survey (2024), two-thirds of the companies surveyed—among medium-sized businesses, even over 70 percent—say that missing technical prerequisites are delaying or blocking their AI projects. This is hardly surprising once you look at the requirements.
But what exactly is different? In essence, there are three factors your infrastructure must deliver to be AI-ready:
Computational Intensity: Modern AI models require tremendous parallel computing power. With CPU-optimized servers, you quickly hit physical limits.
Data Hunger: The more data, the better the AI system learns. That calls for robust storage and data transfer paths—far beyond traditional database requirements.
Real-Time Demands: Users expect answers within seconds, often instantly. High latency is like sand in the gears—annoying and inefficient.
The good news: You don’t have to start from scratch. With a clear view of your real requirements—and a few targeted adjustments—you can unlock a lot more AI potential from your existing setup than you might think.
The Four Pillars of an AI-Ready IT Infrastructure
A robust AI infrastructure is built on four pillars. Each is essential: Neglect one, and it quickly becomes a bottleneck for your projects. Let’s take a closer look:
Computing Power and Hardware Requirements
Unlike traditional software, AI workloads are massively parallelized. While your accounting department processes each record sequentially, machine learning algorithms fire off thousands of calculations concurrently.
This makes graphics cards (GPUs) indispensable. Market leaders like NVIDIA set performance benchmarks with models such as the A100, H100, or RTX series. A single NVIDIA A100 delivers computing power that used to require entire server racks.
But beware: Not all GPUs are created equal! For running models (“inference”), entry-level GPUs (like the NVIDIA T4) may suffice, while training large, proprietary models typically demands high-end cards such as the H100. Edge solutions from Google (Coral TPU) or Intel (Movidius) offer specialized efficiency for decentralized scenarios.
What about memory? Large models are intensive: Hosting a local LLM like Llama 2 with 70 billion parameters requires at least 140GB RAM—not including pure text processing.
The CPU remains your workhorse for preprocessing, postprocessing, and system management. CPUs with multiple cores and ample PCIe lanes are ideal for AI—think AMD EPYC or Intel Xeon Scalable.
Data Architecture and Storage Systems
AI is hungry for data—and in its own peculiar way. Classic ERP systems store structured tables in databases; AI models consume all types of information: text, images, audio, video.
That calls for more flexible storage architectures. Object storage (such as Amazon S3 or Azure Blob) has established itself as the new standard. Those staying on-premises look to solutions like MinIO. The key: The architecture scales virtually without limit and handles rapid growth with ease.
Speed also matters: Modern NVMe SSDs deliver high throughput, but are often insufficient for large-scale model training. Distributed file systems like Ceph or GlusterFS bundle the performance of multiple drives and servers—a multiplier for parallel AI computations.
In practice? A manufacturing company running a predictive maintenance project generates terabytes of sensor data in no time. Traditional storage solutions struggle with rapid access and high data volumes. Object-based architectures and distributed systems help you sidestep these bottlenecks.
Equally important is data preprocessing. Data is prepared for AI via ETL pipelines (Extract, Transform, Load)—Apache Kafka is a popular tool for streaming scenarios, while Elasticsearch enables fast search and indexing.
The old AI adage is truer than ever: “Garbage in, garbage out.” Set data quality standards—think data governance or automated quality checks. Every AI solution stands or falls with the quality of its inputs.
Network and Connectivity
The old server-to-user paradigm is no longer sufficient for AI. Any form of real-time AI—be it a chatbot or document analysis—pushes your network to its limits.
For example: A RAG system (Retrieval Augmented Generation) scans millions of documents with every user query. If storage is on a NAS or even distributed, a traditional network can quickly collapse.
That’s why modern AI infrastructures rely on at least 10 Gigabit Ethernet—often more (25GbE to 100GbE). InfiniBand remains the high-performance standard, but it isn’t viable for every budget or use case.
For demanding interactions, every millisecond of latency counts. Modern switches and redundant cabling (e.g., via LACP) are as essential as consistent monitoring. Teams spread across regions? Consider edge servers—these reduce latency and ease WAN bandwidth.
You can further enhance stability and performance by storing relevant data locally (edge computing) and actively planning your network for fault tolerance. In short: Redundancy isn’t just nice-to-have—it’s a must for AI workloads.
Security and Compliance
AI expands the attack surface. Many of the most exciting use cases involve personal data or directly influence business processes—making security a core pillar.
The GDPR (General Data Protection Regulation) demands explainable decisions—black-box AI is especially problematic in regulated industries. You should ensure the use of traceable models (“explainable AI”), or at minimum, thorough documentation and auditability.
A modern attack vector: Manipulation of training data (“model poisoning”). The results can be disastrous. Protect your training data with access controls and monitor data flows closely.
Encryption “at rest” and “in transit” is a non-negotiable. Hardware security modules (HSM) are standard in many data centers. Today’s AI GPUs offer features for confidential computing—a big plus for sensitive data.
Zero trust is more than a buzzword: Ensure minimum-access policies, keep production data and AI services strictly separated, and control all data flows granularly. Container orchestration (Kubernetes) and network policies can help secure your setup.
Regular security trainings are the secret sauce: Fake attachments or targeted attacks on infrastructure are still the biggest entry points—think social engineering.
AI Use Cases and Their Specific Requirements
There’s no such thing as “the one” AI application. Every use case brings its own infrastructure requirements. Let’s look at the main scenarios for medium-sized businesses—and what you need to watch for most:
Chatbots and Conversational AI
Chatbots are the entry point into AI for many—simple on the surface, but surprisingly demanding under the hood. The typical bottleneck: latency. Users expect instant replies, and every second of delay costs trust.
A Google study shows that load times over three seconds cause users to drop off—even smaller delays can cost chatbot interactions.
(Note: The Google study is about website load time, not specifically chatbot responsiveness. Still, the analogy holds.)
For basic FAQ bots, modern CPUs are often enough. Tools like BERT or DistilBERT run on cloud instances or decent server hardware—an Azure D4s_v3, for instance, handles medium requirements well.
More complex conversational AI—using large models like GPT-4—require GPUs such as an NVIDIA T4 or better. A single card can handle dozens of parallel conversations, depending on the model and context length.
Scaling is often underestimated: If a chatbot jumps from 10 to 200 conversations at once, your infrastructure can get overwhelmed. Auto-scaling with Kubernetes or similar solutions is a must—rate limiting protects backend systems.
And don’t forget session management. Context must be kept safe; Redis or similar in-memory stores ensure fast access. Lost chat histories lead to frustration and extra support calls.
RAG Systems (Retrieval Augmented Generation)
So what’s a RAG system? Retrieval Augmented Generation combines large language models with your company’s unique expertise. The architecture is trickier than a standard chatbot: a retrieval engine first finds relevant documents, then the LLM generates an answer based on the facts.
The core: a vector database (e.g. Pinecone, Weaviate, Qdrant) that stores text passages as embeddings—compact vector representations. One million embeddings require about 5GB of storage; large datasets need much more.
Generating these embeddings is compute-intensive, usually GPU-accelerated. In live operation, the database must search millions of vectors in milliseconds—algorithms like HNSW or IVF deliver the necessary performance.
In practice: A manufacturer loads thousands of technical documents as a knowledge base. Without a specialized search architecture, answering a user query might take five seconds. With an optimized vector database? Under 200 milliseconds.
Your documents are changing constantly? Automated ETL processes are a must to keep vectors up to date—ideally set up so new or changed data can be partially re-indexed, instead of always reprocessing the full archive.
Another vital point: context window limits of language models. GPT-4, for example, can currently process up to 128,000 tokens at once. For larger document structures, you’ll need clever chunking and summarization.
Your objective: speed and up-to-date information must not be an either-or. Caching solutions boost performance and cut costs—Redis is also a good fit here.
Document Processing and OCR
The paperless company relies not just on digital files but on intelligent document processing with AI. Modern OCR systems (optical character recognition) combine excellent text recognition with structural awareness—they can automatically capture tables, forms, or signatures.
The kicker: computer vision models need significant GPU power. A standard document scan at 300 DPI is several megapixels in size. Basic graphics cards are not sufficient here.
Think in terms of workloads: Batch processing (e.g. scanning receipts overnight) runs cost-effectively on standard GPUs; live analyses for customer access require high-end models.
Pro tip: Great OCR is only as good as the preprocessing. Skewed pages, shadows, and poor lighting? OpenCV-based pipelines fix it. Models like LayoutLM even analyze structure and context within documents—but require robust hardware in return.
Mind your storage: Object storage works best for archiving both originals and extracted data, ideally with automated archiving and deletion routines. If you’re subject to the GDPR, audit trails and strong data management are essential.
Predictive Analytics and Business Intelligence
Predictive analytics gives you tomorrow’s insights—whether for sales forecasts or predictive maintenance. Common: LSTM or transformer models for time series. Their training seldom happens in just a few hours: depending on data size, weeks-long training cycles aren’t uncommon.
Key: feature engineering—turning and providing the right variables for the models. Parallelization is vital: With Apache Spark, even very large datasets can be processed quickly.
For real-time inference, like in stock trading, you need latency below ten milliseconds—not every system can achieve this out of the box. Specialized infrastructure and a solid understanding of your processes are necessary for effective automation.
Example: A logistics provider uses predictive analytics for environmental planning and scheduling. New models can be trained in a few hours on powerful hardware; in production, the system is latency-optimized.
Don’t forget: Models lose accuracy over time when the data landscape changes (“model drift”). Monitoring and regular retraining are mandatory, not optional. Explainable AI requires additional computation—tools like SHAP or LIME increase transparency but need extra resources.
Cloud vs. On-Premises: Making the Right Choice
The big question: cloud or on-premises? Both sides have their fans—and make solid arguments. What matters is your actual use case and your appetite for risk.
Point for the cloud: It scales flexibly, you pay as you go, and get access to cutting-edge hardware with no big upfront investment. AWS, Azure & co. offer GPU instances from just a few euros per hour—perfect for testing and pilot projects.
But beware the cost avalanche: running workloads in the cloud 24/7 can get very expensive. A large GPU instance can cost as much per month as buying a new server—when utilization is high and permanent, on-premises often makes sense beyond a certain threshold.
Latency and data privacy are crucial factors. The best GPU instance is worthless if your data sits five countries away, or the GDPR forbids moving sensitive information abroad. Always check availability and compliance scenarios early.
Hybrid solutions offer flexibility: sensitive workloads run onsite, and load peaks are dynamically offloaded to the cloud (“cloud bursting”). Orchestration and monitoring become much more complex, though.
Edge computing brings AI answers right where they’re needed—on the company premises or with your customer. This slashes latency and boosts security even more. For some businesses, edge is the secret ace up their sleeve.
If you need maximum control and predictability, on-premises is usually the best option—but comes with power, maintenance, and hardware overheads. Modern solutions are increasingly containerized, making it easier to switch between cloud and in-house systems.
Integration into Existing Legacy Systems
The real challenge in many AI projects is integrating with existing (often old) systems. You can have the most advanced AI—but if it lacks access to data from your ERP, MES or other sources, it’s little more than an academic exercise.
The problem: many legacy applications don’t speak modern APIs, and their data is buried deep in historic databases. Accessing data without disrupting live operations takes a delicate touch.
ETL pipelines (e.g. with Apache Airflow) have proven effective in periodically and safely extracting necessary data. Read-only database replicas protect production systems, while message queues like Apache Kafka synchronize old and new asynchronously.
Pro tip: Use well-defined interfaces and favor incremental modernization (microservice architecture) rather than replacing everything at once. Change data capture (CDC) can bring real-time data into new systems—even with older databases.
Caching frequently used data with Redis or Memcached eases the stress on legacy systems. Monitoring and rollback mechanisms are a must—outages and surprises are as unwelcome in medium-sized companies as in large enterprises.
Don’t forget: Many legacy systems are data hodgepodges! Validate data quality and structures during preprocessing—or your AI will come up empty-handed.
Scaling and Performance Optimization
Making your AI project successful means planning for growth, too. The challenges are particular: scaling at the GPU level is nothing like scaling classic web servers.
Horizontal scaling—lots of small instances instead of few big ones—works for CPUs with little hassle. For GPUs it’s more complex and more costly: instances aren’t always available, cold starts cause delays, and sharing resources on the same GPU is tricky.
Kubernetes and other orchestration tools help by managing GPU nodes in separate pools. Node autoscalers provide dynamic scaling; NVIDIA’s multi-instance GPU technology ensures resource isolation.
Smart model serving is crucial for performance. Pre-loaded models in stateless services scale more efficiently. TensorFlow Serving and TorchServe are proven solutions for many enterprise setups.
Optimized caching and load balancing are key: round-robin won’t cut it, while response-time-based routing distributes the workload better.
Batch workloads and real-time services require different optimization strategies—don’t deviate from a clear operations concept too soon. Quantizing models (8/16 bit instead of 32 bit) reduces memory usage and latency costs.
In the end, visibility is what counts: GPU utilization, model accuracy, and memory consumption should be monitored continuously with tools like Prometheus and Grafana. The circuit breaker pattern protects against domino effects during overload. And: edge caching helps bring AI answers as close as possible to your users—cutting latency even further.
Cost-Benefit Analysis and Budget Planning
Anyone planning an AI project must consider not just what’s feasible but what’s affordable. In practice, even modest projects can quickly grow into the five- or six-digit range—especially if cloud services or custom hardware are involved.
Hardware is only the tip of the iceberg: top GPUs (e.g. NVIDIA H100) easily cost €25,000 or more, and additional expenses for power, cooling and networking add up fast (experience shows 40 to 60 percent extra is realistic).
Cloud costs can spiral out of control—so auto-scaling should always be capped by budgets and alerts. On-premises expansions require investment and depreciation planning, but offer more cost control over the long term.
Development and expertise also drive up costs. Skilled professionals are in short supply and expensive; external consultants can help—expect €1,000 to €2,000 per day for experienced specialists, with the upside of quick results and fewer mistakes.
Don’t forget software licenses! TensorFlow and others are open source, but products like NVIDIA AI Enterprise come with licensing costs. Always calculate total costs over at least three years (Total Cost of Ownership, TCO).
Use a phased approach—pilot projects with manageable scope (“Minimum Viable Product”) provide rapid learning effects and save budget. This keeps you agile and helps you avoid nasty surprises.
Implementation: A Pragmatic Roadmap
Sounds complex? It’s manageable—with a clear, phased roadmap. Here are the four key stages to getting started with AI in practice:
Phase 1: Assessment and Proof of Concept (4–8 weeks)
Put all your data, processes, and infrastructure under the microscope: What’s available, what still needs to be created, and where are the clear business potentials? The biggest hurdle is almost always data quality.
A mini-proof-of-concept using off-the-shelf cloud tools (for example AWS SageMaker, Azure ML) gives instant insight into whether your use case actually works.
Phase 2: Pilot Implementation (8–12 weeks)
At this stage: Only a well-defined use case with measurable targets (e.g. a customer service chatbot) avoids wasted effort. Managed services reduce initial complexity and offer valuable experience without having to invest heavily in your own hardware.
Implement monitoring and success measurement from day one: Without usage data and feedback, you’re flying blind.
Phase 3: Scaling and Optimization (12–24 weeks)
The next step is targeted expansion. Based on pilot results, you can right-size hardware and training—systems that are too big or too small are both liabilities long-term.
Machine learning operations (MLOps) become critical. Automate model deployments, backups, and monitoring. Tools like MLflow or Kubeflow help keep things organized.
Phase 4: Production and Maintenance (ongoing)
Finally, ongoing retraining and team workshops are on the agenda. AI projects are continuous efforts: data and application fields evolve constantly. Change management and thorough documentation are now essential.
Business impact and ROI should be measured and communicated continuously—so your AI project doesn’t become an end in itself in the long run.
Frequently Asked Questions
What are the minimum hardware requirements for AI applications?
For simple AI applications—like chatbots—modern CPUs with 16–32GB RAM are often sufficient. Machine learning workloads benefit greatly from GPUs: Entry-level models such as NVIDIA RTX 4090 or similar are the starting point; production environments usually require T4-class or better. For large language models, high-end GPUs like the A100 or H100 with 64+ GB RAM are virtually essential.
Should we run AI in the cloud or on-premises?
Both make sense: cloud environments are great for experiments or highly variable loads. On-premises is worthwhile with high, permanent workloads and when data control is crucial. Hybrid models offer flexibility—letting you keep sensitive data internal while computationally intense tasks run in the cloud.
How do we integrate AI into existing legacy systems?
ETL pipelines and event-based messaging (e.g. using Apache Kafka) are common approaches. APIs are ideal, but in older systems they’re often not available yet. Intermediate steps such as database replicas or event streaming bridge the gap. In the long-term, a microservice architecture cleanly separates legacy systems from new AI components.
What security risks do AI systems introduce?
AI increases your attack surface—think attacks on training data or targeted manipulation (“model poisoning”). Adversarial attacks are a real risk for images. Zero-trust principles, encryption for all data traffic, and regular audits of models and data interfaces are crucial. The GDPR requires that decisions remain explainable.
What costs should we expect?
Proof of concepts often start at €10,000 to €20,000. A productive system can quickly climb to €50,000–200,000, depending on hardware, licensing and personnel needs. A high-end GPU like the H100 costs €25,000 and up; consider energy, cooling and licensing costs as well.
How long does an AI implementation take?
Proof of concepts can be completed in 4–8 weeks; pilot projects generally take 2–3 months. Building complex machine learning systems—especially with substantial data preparation—can require six months or more. Data quality is often the key factor affecting timelines, not just the development work itself.
What qualifications should our employees have?
At the outset, external experts or your existing IT staff with data and API skills are usually enough. Python knowledge is helpful, but not absolutely needed to get started. Over time, experience with your chosen cloud platforms, data architectures, and MLOps become more important—you don’t need dedicated AI specialists on day one.
