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How to Prepare Your Infrastructure for AI

Using AI tools to streamline processes and workflows has become increasingly accessible in recent years. Many are eager to avoid being left behind in AI adoption. At the same time, according to Gartner, as many as 85% of AI implementations fail to meet expectations or reach completion. Fortunately, there are simple solutions to better prepare your infrastructure AI and future-proof your IT, so your AI investment delivers business value.

Assess your current IT infrastructure for AI

The first step is to map out your existing systems and data sources. Review what systems and data sources are currently in use and identify which ones can support infrastructure AI. It's also important to understand how data flows through your organisation.

If you are currently using a private cloud solution, you will need to consider cloud integration to take full advantage of what infrastructure AI has to offer. To achieve the scalability and flexibility this requires, we recommend cloud platforms like Microsoft Azure. These also offer specialised AI services and facilitate seamless AI implementation.

Focus on building a scalable infrastructure AI solution to handle the changing resource requirements of AI models. You need the flexibility and adaptability to effectively manage the dynamic demands of AI development and deployment, while optimising your resource management and costs. With the right cloud solution in place, you can integrate different environments, optimise resource usage, and ensure smooth transitions between platforms.

 

“If you are currently using a private cloud solution, you will need to consider cloud integration to take advantage of all that AI has to offer.

 

Prepare your data for AI

Avoid the temptation to upload all your company data into the AI model. Inaccurate or outdated data not only leads to irrelevant answers but also increases costs as the AI processes material that is no longer relevant—for example, a financial report from 2015.

Therefore, set clear data limits to ensure that the AI only works with current and relevant information. Restricted SharePoint Search is an example of how you can effectively prevent old or outdated data from entering infrastructure AI systems. By proactively restricting and quality-assuring the data you use, you can significantly improve accuracy and reduce operational costs.

These are five simple steps to start with:

  1. Limit the AI to only find documents from the last year. This could be strategy documents, quote templates, manuals or policies, for example.
  2. Only include meeting notes from the last year.
  3. Filter customer data to the last two years.
  4. Select reports and analyses from relevant time periods, preferably from the last 12-24 months.
  5. Set rules for when documents should be archived and removed from AI access. This could apply to documents older than 24 months.

Of course, the timeframes that apply to your particular company may differ from how other companies operate. But with simple data policies, you and your infrastructure AI models will be able to quickly create business value and keep costs down.

 

“Avoid the temptation to upload all your company data into the AI model.”

 

Review security for infrastructure AI

A robust and comprehensive security implementation should include:

  • Security controls to protect AI models and sensitive data. Implement access controls, firewalls, and intrusion detection systems to prevent unauthorised access.
  • Version management to track changes to models and data over time. Version management tools document and track changes to infrastructure AI models, enabling easy recovery and troubleshooting.
  • Compliance with regulations such as GDPR for data management. Ensure that all data management and AI processes comply with laws and regulations—especially those related to personal data and privacy.
  • Encryption of data. Strong encryption protocols protect data both when it is stored and when it is transferred between systems, minimising the risk of data leaks.
  • Access controls and authentication for AI systems. Implementing robust authentication and role-based access controls ensures that only authorised personnel can use infrastructure AI systems.

 

“Create a flexible and scalable IT infrastructure that can be easily adjusted to meet new business requirements and take advantage of the latest innovations in the field of AI.”

 

Next step towards AI

Develop an AI policy that governs how your organisation uses AI, focusing on security, ethics, privacy, sustainability, and legal compliance. Employee training is also crucial at this stage. 

Read more: How to create an AI policy

Once you have integrated infrastructure AI into your IT infrastructure and daily work processes, you should continuously measure the performance of AI models in production. This means monitoring the tools to identify anomalies, operational issues, or reduced accuracy. Use these insights along with user feedback to fine-tune and update your infrastructure AI models for continuous improvement and relevance.

Your infrastructure AI will need to adapt as business needs, security threats, and technologies evolve. Create a flexible and scalable IT infrastructure that can be easily adjusted to meet new business requirements and take advantage of the latest innovations in the field of AI.

Contact us

Preparing your infrastructure AI is about more than just technology—it's about creating sustainable processes, good data quality, and a strong security culture.

If you don't have the expertise in-house, you should enlist the help of an experienced IT partner with a focus on close collaboration and good business insight—who can deliver solutions that suit your business.

Need advice or support in your infrastructure AI journey? Don't hesitate to get in touch!

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