AI that understands your business – how to increase accuracy in Microsoft 365 with semantic context

Executive summary

The rapid development of AI and collaboration platforms such as Microsoft Copilot creates enormous opportunities, but also new challenges for organizations. Many AI initiatives fail or produce less than expected results. This is often because the information lacks semantic context, the AI simply does not understand the language, structure and logic of the organization.

To maximize the return on AI investments, it is crucial to create a clear semantic context. This means that data needs to be structured, relevant and provided with the right context. By creating a semantic middle layer on top of Microsoft 365, data can be given context and meaning, allowing AI tools like Copilot to deliver real value.

A semantic middle layer transforms Microsoft 365 from a chaotic repository to a structured and living platform where users can easily find, understand and update the right information. This leads to a focused and self-learning information landscape that enables AI to deliver real value and turn data overload into a strategic resource.

AI is changing the playing field

AI, especially Generative AI through Microsoft Copilot, promises great productivity gains. However, many organizations invest in licenses without managing to convert the potential into actual business benefits. Often this is because the data lacks context and structure. For AI to deliver value, you need access to the right data in the right context.

The semantic void in Microsoft 365

The problem is the lack of order, structure and context. There is a huge information overload in Microsoft 365 environments. Organizations are drowning in information: too many Teams channels, disorganized documents, a growing number of SharePoint sites, and a fragmented tool landscape. According to Jeff Teper, President of Collaborative Apps & Platforms at Microsoft, two million new SharePoint sites are created every day[1]. This uncontrolled expansion exacerbates the challenge and creates a paradoxical state: the information exists, but is effectively inaccessible due to a lack of structure. This makes relevant information difficult to find and limits Copilot’s ability to deliver value.

What makes Microsoft 365 particularly challenging is:

  • Changing goals and priorities: Business strategies and objectives are constantly changing, which means that the relevance and context of information shifts rapidly.
  • Constant content production: Every day, users create and share new content across Microsoft 365 apps. Every day, new documents and data are created and shared across apps, rapidly increasing both volume and complexity exponentially.
  • An ever-changing ecosystem: Microsoft is continuously evolving the platform with new features, templates and tools that change the way people work.
  • External and internal changes: New legal requirements, regulatory changes and competition are forcing organizations to reassess and restructure their information.

Microsoft 365 users therefore find themselves in a mobile information environment where their idea of where information should be often does not match reality. Files move, versions multiply, new sites are added. The result: uncertainty, inefficiency, and a constant search that leads to duplication of effort.

Ironically, new digital initiatives tend to increase rather than decrease complexity, as each new tool or system changes the information structure of others. The result is often that individuals and teams become siloed, inhibiting collaboration, innovation and access to the organization’s collective knowledge.

Traditional solutions such as intranets, file management via Teams have often proved insufficient. They quickly create outdated and rigid structures that drive users towards their own unregulated solutions, so-called “Shadow IT”. The consequence: more time is spent looking for information than creating value.

Microsoft Copilot currently costs around $30 per user per month, a significant investment that must deliver tangible results. But Copilot and other AI tools have limited capacity to interpret and use unstructured and disorganized information. For these investments to be effective, the data needs to be structured, relevant and contextualized.

Only then can Copilot do what AI promises: free up time, boost productivity and create real business value.

Industry leaders underline this:

  • “No matter how much you invest… you won’t get an ROI on AI-driven investments without clean data,” says Quantis.ai, citing Gartner’s estimate that “bad data” costs organizations an average of $12.9 million per year. The McKinsey Global Institute confirms that poor data quality can lead to 20% lower productivity and 30% higher costs. [1]
  • According to Accenture, 75% of executives consider “good data quality” to be the most important factor in improving generative AI, and 47% of CXOs identify data cleanliness as the biggest challenge.[2]

=>These figures clearly show that the potential of AI remains untapped without a robust data foundation.

  • A realization in the industry that AI alone does not solve these complex data problems. Despite great enthusiasm around Generative AI, organizations now face the practical difficulties of implementation. McKinsey’s reports (June 26, 2025) show that almost all companies are investing in AI, but only about 1% consider themselves mature in its application – often due to weak strategies and shortcomings in data access and quality. [3]
  • According to Accenture, many European companies (56% of those surveyed) have not yet managed to scale up major AI investments, pointing to “a robust data foundation” as a key barrier.[4]

=>So the problem is not about acquiring AI tools, but about preparing the ecosystem to really benefit from AI that delivers impact.

The solution: A semantic middleware

The solution lies in creating a semantic middle layer, a structuring layer on top of Microsoft 365 that gives data context and meaning. It’s not just about indexing information, but about transforming data into relevant and trusted sources of information.

This gives AI access to relevant, authoritative and verified information, which is crucial for generative AI tools like Copilot to work accurately and efficiently.

A semantic middleware transforms Microsoft 365 from a chaotic repository to a structured and living platform where users can easily find, understand and update the right information. The result is a focused and self-learning information landscape that enables AI to deliver real value, turning data overload into a strategic resource.

Overall, it consists of the following elements:

  • Index everything: Systematically collect and index all dispersed data sources inside and outside the organization’s Microsoft 365 environment, regardless of format, location or application. The goal is to create a complete, but as yet undifferentiated, information base that provides the raw material for further semantic refinement.
  • Extracting semantic context: Identifying and extracting crucial metadata that defines the meaning and relationships of the information. This includes primary sources, authority (who is the expert?), validity (is the information current and accurate?), ownership (who is responsible?) and other relevant business attributes.
  • Reorganize and tag sources: Once the semantic context is defined, the information is structured and tagged logically. For dynamic data, this is done directly in Microsoft 365, for example in SharePoint libraries and Teams files. For read-only or external information, the relevant tags and metadata are stored separately, but linked to the original source. This step builds a coherent knowledge graph that allows both humans and AI to navigate and interpret the information in its proper context.
  • Build a self-updating semantic middle layer: The core is an intelligent layer that is continuously updated. When new content is created or existing information changes in Microsoft 365, it is automatically analyzed and integrated into the semantic structure. This way, the information is always kept up-to-date without requiring manual re-indexing.
  • Provide AI with context and primary data: Once the semantic layer is in place, AI models – such as Copilot, ChatGPT Enterprise or custom AI apps – can first access a prepared and enriched context before dealing with the actual information.
    This step, called pre-contextualization, allows the AI to better understand the purpose, relevance and credibility of the information it processes. The result is more accurate, relevant and reliable answers.
    The difference is clear: from an AI that only searches to an AI that truly understands.
  • Measure and improve AI performance: Finally, clear metrics and continuous monitoring of AI performance linked to goals are implemented. By identifying gaps in the semantic understanding or data coverage, the model can be improved incrementally.
    This feedback loop ensures that both the semantic layer and AI performance evolve in line with the needs and changes of the organization.

Example scenario:

=> User asks Copilot, “What is our vacation policy?”

Copilot may draw generic information from various non-validated sources, leading to a broad but potentially misleading or incomplete answer.

=> User asks Copilot “What is our vacation policy?”

The semantic team retrieves and contextualizes relevant HR policies. It also includes an AI-generated document (created by crawling dozens of individual department pages, reviewed and approved by HR). This leads to a complete and accurate answer where both the general policy and the department-specific exceptions are clearly stated, reducing misinterpretations and improving the user experience.

This ensures that AI initiatives not only work technically, but also deliver strategic impact by leveraging organizational knowledge.

Conclusion

Successfully applying AI in Microsoft 365 is about more than just introducing new tools. For the investments to pay real dividends, the organization needs to give data a clear and relevant semantic context through structured solutions. This ensures that AI not only works technically, but also creates tangible business value and ensures that employees always have access to reliable information when they need it.

Glossary: AI and semantics

  • Generative AI: A branch of AI that creates new content, such as text, images or code, from existing information and patterns.
  • Copilot: Microsoft’s AI assistant that integrates into Microsoft 365 and helps users find, summarize, and create content by leveraging their organization’s data.
  • Semantic context: The meaning and context that makes information understandable to both humans and AI. Semantic context helps AI to interpret data in a relevant way.
  • Semantic middle layer: A structuring layer on top of Microsoft 365 that organizes, tags, and makes sense of data, making it easier for AI to find and interpret information.
  • Indexing: systematically collecting and cataloging data to make it searchable and easily accessible.
  • Metadata: Data about data. Descriptive information that helps to identify, locate and understand the content of a file or data source, such as author, date or subject category.
  • Knowledge graph: a set of structured information where different data sources and concepts are linked together to show relationships and create coherence.
  • Pre-contextualization: Process where AI accesses the most relevant and verified information before answering the question itself, to improve accuracy.
  • Data quality: How accurate, timely, complete and relevant a data set is. High data quality is essential for AI and analytical tools to work well.
  • Information silos: when data and knowledge are compartmentalized within different parts of an organization and therefore not shared freely, inhibiting collaboration and innovation.

Stockholm, Sweden August 4, 2025
Per Rolder, CEO & Consult, Ways Sweden AB
Jean-Francois Wipf, Founder & Principal Consultant, Refocus on Goals AB
Karsten Held, Certified Azure AI Engineer & M365 Specialist, Refocus on Goals AB

Sources:

[1] M365 Conference 2025 Highlights: Copilot, SharePoint & Big Announcements!

[1] https://www.quantis.ai/post/garbage-in-garbage-out-data-quality-c-suite-ai

[2] https://www.accenture.com/lv-en/services/data-ai

[3] https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

[4] https://newsroom.accenture.com/news/2025/accenture-report-european-firms-must-accelerate-ai-adoption-to-close-productivity-gap