46. IT Infrastructure
What prevents successful AI implementation?
Many organizations have outdated, fragmented, or insufficiently reliable IT infrastructure: different systems that are not connected, slow internet, weak network security, missing APIs, insufficient processing power, poorly organized servers, or chaotic databases. Without a stable and scalable IT environment, AI projects slow down, encounter technical barriers, frequently fail, or cannot be integrated with existing systems at all.
Why is it important to fulfill this prerequisite?
AI systems require quality data, a reliable network, system compatibility, and a strong technical foundation. Without adequate IT infrastructure, AI models run more slowly, produce poorer results, or cannot be implemented. Stable infrastructure ensures data security, proper functioning of AI solutions, fast information processing, and the ability to scale AI projects across multiple teams and processes.
How is this prerequisite fulfilled?
This prerequisite is fulfilled in the following way:
- Assessment of the current IT infrastructure — network, servers, cloud, local machines, security.
- System modernization — migration to cloud, network stabilization, server strengthening.
- Standardization of software and tools — eliminating duplicate systems and harmonizing platforms.
- Introduction of API integrations — enabling communication between systems (ERP, CRM, HRM…).
- Implementation of DevOps principles — automation of deploy processes, monitoring, CI/CD.
- Ensuring cyber security — firewall, encryption, multi-factor authentication.
- Centralized data management — DWH, lakehouse systems, organization of tables and data sets.
What are the concrete benefits for the organization?
The results and consequences of improving IT infrastructure are:
- A stable environment in which AI solutions can function smoothly.
- Faster and more reliable data processing for all business processes.
- Greater security and reduced risk of cyber incidents.
- Easier integration of new AI solutions into existing systems.
- Scalability — the ability to introduce AI into all parts of the organization.
Indicators that this prerequisite has been fulfilled
KPI (success indicators):
- % of systems that have API integrations (target: >80%).
- System availability (uptime) — target: 99%+.
- Reduction in the number of IT incidents (number per month).
- Data-processing speed (ms / processing).
- Increase in security — number of successfully blocked threats.
- % of modernized servers and workstations.
- Reduction in the number of “legacy” systems (%).
Average ROI for fulfilling this prerequisite
- Return on investment: N/A – directly affects the stability of AI systems
- Timeframe: 1 – 4 months of improvement
- Best for: every sector, especially companies with legacy systems and cloud migrations
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