Artificial intelligence (AI) is revolutionizing the way companies operate, make decisions and serve customers. But to effectively harness its potential, organizations must face a fundamental question: is the data they have at their disposal ready for AI? Together with Gimmal experts, we outline key principles for preparing data for AI deployments – compliant, secure and effective.
Key findings
Most organizations are investing in AI, but few are achieving real benefits from it – the reason is the lack of data readiness. Data must be organized, consolidated and classified before it can be used by AI models. Simple mistakes, such as lack of data hygiene or scattered files, can lead to disclosure of confidential information or wrong decisions. AI won’t fix data clutter for you – you have to do it before implementation.
Table of contents
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The reality of AI implementation
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The problem of poor data quality
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AI as a boost to information management
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The three pillars of data readiness: Comprehend, Combine, Classify
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Protecting critical data – not just PII
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Summary
The reality of AI implementation
Although AI is appearing on everyone’s lips, many companies are implementing it without clear goals. Employees are testing AI tools without a strategy, which ends up wasting resources and taking risks. According to an MIT study, although 75% of data directors believe in the transformative potential of AI, as many as 78% have not realized any value from it. The reason? Data unprepared for analysis and machine learning.
The problem of poor data quality
Organizations are rich in data, but poor in knowledge. AI doesn’t clean the data automatically – if you give it unstructured or outdated information, the results will be wrong. An example? Tools like Copilot can unknowingly access sensitive HR files and incorporate them into generated documents. Without access management and data classification, the risk of a data breach increases.
AI as a boost to information management
AI is the perfect excuse to improve information management. With Gimmal’ s solutions, it is possible to organize data not only for risk, but also for business value. AI requires data:
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structured and standardized,
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easily accessible and well described,
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adequately protected and compliant.
Only then can it really work to the benefit of the organization.
The three pillars of data readiness: Comprehend, Combine, Classify
Comprehend: Educate employees on data hygiene – where to store files, how to name them, when to delete them. Changing habits is the first step to order.
Combine: reduce data dispersion. Files on Google Drive, Box, SharePoint, Dropbox? Consolidate your data into a single environment for easier management and security.
Classify: Label data according to its sensitivity. A simple classification system allows you to control access and compliance.
Protecting critical data – not just PII
We often focus on personal data, but that’s not the only thing that needs protection. Sensitive data includes:
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Intellectual property (algorithms, source code, patents),
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Trade secrets (strategies, development plans),
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Financial data (forecasts, budgets),
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Personnel documentation (salaries, evaluations),
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Customer and partner information (contracts, arrangements).
All these components must be identified and protected from accidental leakage.
Summary
Artificial intelligence is a huge opportunity – but only for those who sort out the data before implementing it. It’s not about huge technology investments, but about thoughtful strategy, data classification and employee education. With the help of Gimmal’ s solutions, you can build the foundation for secure, compliant and valuable AI implementations. Data readiness is not a fad – it’s a requirement for success.
