Technical Challenges of Implementing AI in a Company – What Should a CTO Know Before Talking to Us?

Artificial intelligence and automation are among the hottest trends in business today. More and more companies are deciding to implement AI, hoping to reduce costs, increase efficiency, and make better use of data. However, the path from idea to working solution is full of technical pitfalls.

That’s why it’s important for a CTO or the person responsible for technology to know what problems may arise before we sit down for a conversation about implementation.

1. Data Quality and Availability

AI is only as good as the data it’s trained on. The most common problems include:

• Insufficient data volume – models learn from examples, so the fewer the data, the higher the risk of errors.
• Poor data quality – outdated, inconsistent, or poorly labeled data lead to wrong decisions.
• Data silos – lack of a unified infrastructure (CRM, ERP, spreadsheets spread across departments).

👉 A CTO should know the current state of “data hygiene” in the company and whether the data is ready to be used by AI.

2. Integration with Existing Infrastructure

A frequent challenge is connecting new AI solutions with existing IT systems. Problems occur when:

• the company uses many legacy systems,
• APIs are limited or don’t exist at all,
• integration requires major changes in system architecture.

👉 A CTO must be aware of which systems are critical and how open they are to integrations.

3. Performance and Scalability

AI models, especially those based on machine learning and neural networks, can require significant computing power. Challenges include:

• infrastructure costs (GPU servers, cloud),
• model training time,
• the need to optimize algorithms to work in real time.

👉 A CTO should consider whether the company has sufficient IT resources or if it would be better to rely on cloud-based solutions.

4. Security and Privacy

AI operates on data, often sensitive. This raises questions about:

• GDPR and regulatory compliance – especially in finance and healthcare,
• cybersecurity – AI models can become targets of attacks,
• data anonymization – how to train models without violating customer privacy.

👉 A CTO should know the company’s data security policy and understand which regulations apply.

5. Model Maintenance and Monitoring

Implementing AI is only the beginning. Models must be:

• regularly updated – as data and business realities change,
• monitored – to ensure they don’t start producing errors,
• scaled – as the number of users and queries grows.

👉 A CTO must understand that AI is not a “one-off installation” but a process requiring ongoing supervision.

6. Team Competence

Even the best AI solutions won’t work if the team doesn’t know how to use them. Typical challenges include:

• lack of data specialists (data engineers, ML engineers),
• lack of business knowledge on how to apply AI in practice,
• employee resistance to new tools.

👉 A CTO should assess which competencies exist in the company and which need to be added.

Conclusion

AI and automation can significantly improve a company’s efficiency, but implementation is not always simple. Before meeting with us, a CTO should ask themselves a few key questions:

• Are the data ready to be used?
• Does the IT infrastructure allow for integrations?
• How will the company ensure security and monitoring?
• Does the team have the right competencies?

Thanks to this preparation, a conversation about automation and AI with AutomationMoon will be not only inspiring but also concrete and tailored to the real business needs.

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