Microsoft SharePoint has evolved into one of the most essential collaboration and document management platforms for organizations worldwide. Businesses rely on it to store, organize, and share vast amounts of information efficiently. However, as the volume of data grows exponentially, traditional search methods face increasing challenges in delivering accurate, relevant, and quick results.

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This is where artificial intelligence (AI) and other modern technological advancements come into play. The future of SharePoint search functionality is being shaped by AI-driven tools, machine learning, natural language processing, and cloud-based solutions. These innovations are not only improving search accuracy but are also enhancing user experience, security, and productivity.

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Let us explore how AI and modern solutions have already and will soon transform SharePoint search tools and what the future holds for this evolving technology.

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Microsoft SharePoint has evolved into one of the most essential collaboration and document management platforms for organizations worldwide. Businesses rely on it to store, organize, and share vast amounts of information efficiently. However, as the volume of data grows exponentially, traditional search methods face increasing challenges in delivering accurate, relevant, and quick results.

‍

This is where artificial intelligence (AI) and other modern technological advancements come into play. The future of SharePoint search functionality is being shaped by AI-driven tools, machine learning, natural language processing, and cloud-based solutions. These innovations are not only improving search accuracy but are also enhancing user experience, security, and productivity.

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Let us explore how AI and modern solutions have already and will soon transform SharePoint search tools and what the future holds for this evolving technology.

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AI-powered search: improving accuracy and relevance

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The biggest limitation of traditional search mechanisms is their dependence on keyword matching. Users often struggle with retrieving the exact information they need, even when using the right keywords. AI-powered search is changing this by introducing intelligent algorithms that understand context, intent, and patterns.

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Microsoft is integrating AI with SharePoint search functionality to enhance accuracy and relevance. 

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To understand the current context of search in SharePoint, you must understand the three major available search systems from Microsoft. Two are integrated into SharePoint and the third, much more powerful, is not, and requires coding to implement.

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1. SharePoint Search

SharePoint Search is a descendent of the SharePoint-specific search engine originally built into SharePoint on Premise.  It is an excellent classic search engine still available in many SharePoint search functionalities.  It is tightly integrated with document-level user permissions, for the best access control.  Users will not see things that they are not entitled to.

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2. Microsoft Search

This is the modern search experience in SharePoint.  It has a more rudimentary set of customizations and API, exposed via Microsoft Graph, but it integrates some machine learning.  Its data sources extend to other M365 products, not just SharePoint.  It does not have dedicated settings for document-level security; instead, it leverages the existing access controls within the underlying data sources.

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3. Azure AI Search

Formerly called Azure Search and Azure Cognitive Search, Azure AI Search includes a lot of sophisticated search and AI capabilities.  It is not part of SharePoint and it has its own index but you can implement it using its API.  It can search a wide variety of data sources, not just M365. However, Azure AI Search doesn't provide native document-level permissions and can't vary search results based on user identity.  Instead, you can use workarounds based on groups of users to build security trimming of results. The ability for Azure AI search to index SharePoint files is in public preview.

A lot of advanced AI search features are available in M365 only if you build them and compromise a bit on access control, and many more are on the drawing board.

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Search components and where AI can be applied

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Search has many components, including indexing, query elicitation, query processing, retrieval, ranking, and display, and each one can be enhanced with AI. Let’s look at each one in turn to see some of the key ways AI is used to improve search

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1. Indexing

Indexing is typically a process done long before the query, where every word in every document and every piece of metadata is stored in an index, and it is this index that is used to respond to user queries.

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a. Enrichment

All three search systems have the ability to infer certain properties of the text they are indexing and include them in the index.  Azure AI Search calls these functionalities “Skills” and can use various AI tools to find extra  information to add to the index.  For example, it can extract keywords, or translate, or find personally identifiable information or analyze the sentiment of each sentence, or OCR and describe images, etc.  These properties are only in the search index. Within SharePoint, Syntex and sometimes Copilot can mine and store some of this metadata in the source document’s metadata in an asynchronous process that indexing will then find.  In the past, Viva Topics, formerly Project Cortex, would classify documents into topics but the product was retired on February 22, 2025.

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b. Vector index

By storing concepts that are represented by words or groups of words in a context in the form of numerical vectors, when you retrieve based on a query, you can retrieve similar concepts that have similar vectors.  This works for synonyms, or multilingual search, or search on different media like audio and images.  The vector index is used for both retrieval and ranking.

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c. Semantic index

Semantic indexing is used for re-ranking rather than querying.  It uses AI language understating to dramatically improve the ranking of results, so that the most relevant results are presented first.  It also allows programmers to build semantic answers, natural language answers in response to natural language questions.

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d. Entity graph

This more advanced feature indexes not only aspects of the text and entities it finds within it, but the relationships between them.  This can be used for structured queries, in finding related information that was not explicit, as well as structured hierarchical search like GraphRAG (Retrieval Augmented Generation using a Knowledge Graph).

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2. Query elicitation and query processing

Surely we can do better than a static query text search box and exact match of the words in the query.

Classic algorithms as well as AI can be used to help the user formulate a query.  Simple tools like predictive text and spellchecking can be used to help the user modify their search before it is submitted.  Others, like acronym expansion or synonym expansion, can be based on existing lists, like the ones that are already in SharePoint Search, or use LLMs or small language models (SLMs).  One exciting innovation is “Tip of the Tongue Query Elicitation”, where the user doesn’t recall the exact words or title they are looking for, and an AI assistant helps them remember it. Other innovations include suggesting a modified variant of the  query after the results have been found, for a second round of searching.

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3. Retrieval

Once the query is ready, the search engine will search for matches in the index.  The days of searching for exact matches or even of exact matches of all the words are over (we hope.  Sometimes they come back)  The search system in SharePoint and in Azure AI search will also find variants.  This includes expanding acronyms, using morphological variants (plural and past and participle, some prefixes and suffixes in English, a lot more in other languages) and synonyms.  It can also augment results with vector similarity search, and include contextual information like user and recent history.  Here, the use of AI can make searching much more powerful and accurate.

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4. Ranking

Once the results are found, they will be ranked, probably by relevance.  The classic algorithm is BM25 or RRF, and that is the basic way to rank, looking at how often each word appears in the document.  That doesn’t guarantee that the concept of the query is in there.  Search engines often use other factors as well and re-rank the results.  Microsoft Search uses document popularity and history of the document and user interactions.  Azure AI search goes considerably further with AI, using vector ranking, semantic ranking, and hybrids.  These algorithms are too computationally expensive to run on the entire index, but they are used to re-rank hits that are already in the top 50 of the simpler ranking, and use more language understanding and look beyond the words that were matched.  Even better generative query engines with dramatically better relevance will soon  be available

Ranking can be personalized, taking a page from Microsoft Search by giving different rankings to different users based on their position and previous interactions.

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5. Display

One search results have been found, they have to be displayed.  Generally there are a couple of lines worth of information about each document it found.  Can AI do better? The best example of AI-driven display is PointFire Search Summarizer’s query-focused abstractive summaries.  Other possibilities include AI-powered responsive display: search results don’t need to always look the same, the display can be adapted to the query and highlight the fields that are most relevant to the query.  AI-driven display can also highlight context that is not obvious, such as when a document that is searchable is no longer relevant or has been superseded.

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6. Question Answering

One way to display results, when the query was a question, is in the form of an answer.  This is where Copilot and other generative AI systems shine.  Instead of returning a bunch of documents, it can return the answer to the question that was asked, informed by the documents in the scope where you were searching.  In that case they are often searching a different sort of index: a trained neural network rather than a text index.

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7. RAG and GAR

Generative AI is limited in that its answers are based on the data set on which it was trained, which may be out of date and contain general knowledge that is not applicable. Retrieval Augmented Generation (RAG) is a set of techniques that allows the Generative AI to supplement its training by querying the more recent documents that it has access to.  In this case, it is the Gen AI system that issues a query and looks at the response.  The search system and its index and all the AI improvements discussed above are used by the Generative AI, not by the user directly.  The better the search, the better the answer it can provide.  The other side of the coin is Generation Augmented Retrieval  (GAR) which uses text generation to enhance the search.  The two are really a continuum, the point of both is to answer the question that is being asked, whether in the form of an answer or a list of documents.

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8. Voice interface and dialogue mode

With improvements in speech recognition and generation, AI can use a voice interface for search, and even a dialogue with the user to further specify and disambiguate the request in a human-like way, and to explain the results and recommend other actions.

AI-powered SharePoint search tools can suggest related documents, sites, and content that users might find useful, reducing the time spent searching for information.

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Enhancing security and compliance with AI-driven search

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As organizations handle vast amounts of sensitive and confidential data, security is a major concern. AI is playing a significant role in ensuring that SharePoint search tools not only improve search efficiency but also enhance security and compliance.

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1. Automated compliance monitoring

AI-driven compliance monitoring tools help organizations identify and manage sensitive data more effectively. They can automatically detect documents that contain personally identifiable information (PII), financial data, or discussions of legal issues that require strict access controls, even if it was mistakenly missed or is within an otherwise accessible document.

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For example, if an employee searches for confidential reports, AI-powered search tools can flag restricted passages and prevent unauthorized access based on predefined security policies, or blank parts of them from the results.

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2. Threat detection and anomaly identification

AI-powered SharePoint search tools can detect unusual search patterns that may indicate a security threat. If an employee suddenly starts searching for sensitive financial data outside of their usual work scope, the system can flag this behavior and alert administrators for further investigation.

These proactive security measures help organizations mitigate risks and maintain compliance with data protection regulations.

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The future of SharePoint search: what to expect

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AI and modern technologies are continuously evolving, and the future of SharePoint search functionality looks promising. Several upcoming advancements are expected to redefine the way organizations interact with enterprise search systems.

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AI-powered virtual assistants

AI-driven virtual assistants are expected to become more sophisticated, integrating deeply with SharePoint search tools. These assistants will provide contextual search assistance, answer queries, and even perform tasks like document summarization or data extraction on demand.  They can also synthesize multiple returned documents, like putting data from a year’s worth of weekly reports into a single table or graph, or generating an annual report from them.

Instead of searching for information manually, users could simply ask their AI assistant to generate a report or find key insights within a database.

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Final thoughts

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The future of SharePoint search functionality is being shaped by AI, machine learning, and modern digital solutions that enhance accuracy, security, and efficiency. AI-powered search tools are enabling businesses to retrieve information faster, refine search results with contextual intelligence, and enhance security measures for sensitive data.

Adopting AI-driven SharePoint search tools will help businesses improve productivity, streamline workflows, and stay ahead in an increasingly data-driven world. Organizations that leverage these modern search solutions will gain a significant competitive edge, ensuring efficient information management and seamless collaboration across teams.

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How to try the Search Summaries for SharePoint

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The PointFire Search Summarizer is currently ins closed preview. You can:

• Schedule a demo here

• Request early access to the preview here

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As always, if you have any questions, you can send us an email at sales@icefire.ca

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