A warehouse can tell you that supplier costs increased 12% last quarter. It may not tell you whether the increase came from a contract clause, a freight surcharge described in a PDF, or an operational issue captured in an inspection photograph. Those explanations often sit outside the structured tables that executives use every day.
Multimodal analytics brings some of that evidence into analytical workflows. The opportunity is not to put every file into a data warehouse. It is to make selected unstructured information usable alongside governed business data, with traceability and controls strong enough for the decisions it will support.
On September 14, 2026, Snowflake announced multimodal support for AI_SUMMARIZE in public preview. The function can summarize text, images and supported document files through SQL. Snowflake’s September 14 release note describes theme extraction from reports, contracts, diagrams and other content, including processing a collection of staged files. That is a useful platform capability, but it does not automatically turn a document summary into an audited business fact.
For CIOs, analytics leaders and operations executives, the key decision is where document understanding belongs in the architecture, what should remain in specialist applications, and which outputs deserve to influence reporting or automation.
What multimodal analytics actually means
Traditional analytics works primarily with structured records: transactions, accounts, dates, amounts, quantities and defined dimensions. Multimodal analytics expands the inputs to information that does not naturally fit into rows and columns, including PDFs, images, scanned forms, charts and narrative reports.
The output may still be structured. A contract can produce a renewal date, an inspection report can produce a category of finding, and a product photograph can produce a descriptive tag. But there is an important distinction between understanding content and establishing a verified fact. A language model’s summary is an interpretation. A validated contract renewal date, linked to the relevant clause and approved by a reviewer, is a business record.
Three patterns are commonly confused. Document summarization condenses content for a reader. Information extraction identifies specific fields for downstream processing. Search and question answering retrieve relevant passages to support a user’s question. They require different accuracy tests, evidence handling and user interfaces. One model function should not be assumed to replace all three.
What changed in Snowflake in September 2026
Snowflake’s AI_SUMMARIZE function accepts text or a FILE reference and returns a text summary. The documented SQL syntax supports AI_SUMMARIZE(TO_FILE(‘<stage>’, ‘<file_path>’)) for staged files. A table of FILE references can also be used to process a document library row by row. Snowflake presents this as a way to avoid building custom parsing, chunking, model-selection and prompt pipelines for straightforward summarization.
That changes the integration surface. A team can keep metadata and file references close to analytical data, call a managed function, and persist the resulting summary with its source identifier. A developer does not necessarily need a separate document AI microservice simply to generate a concise overview.
The AI_SUMMARIZE function reference also sets boundaries. The feature remains labeled Preview, supported file formats and limits vary by deployment region, and multilingual quality can differ by language. For some configurations, supported document formats include PDF, TXT and Markdown; other configurations also support Office documents and CSV, subject to smaller file and page limits. Image format and size support also vary. Teams must check their own region and current documentation before designing a production ingestion contract.
Snowflake’s documentation notes that very short text inputs may be returned unchanged, that token processing is billed, and that row-level error details can be requested. It recommends AI_AGG rather than concatenating many rows for cross-row summarization. These details matter when processing thousands of files, not just one demonstration PDF.
The release is significant because it lowers the friction of summarizing unstructured content within SQL workflows. It is not a promise of perfect extraction, legal interpretation, chart accuracy or compliance readiness.
When documents and images belong in the analytical workflow
The strongest candidates have a clear relationship to an existing business entity or decision. A document should be linked to something that the organization already measures: a supplier, contract, claim, shipment, asset, customer, product or project. Without that link, summaries become another disconnected collection of text.
Consider procurement. A spend dashboard may show price variance by supplier. Contract PDFs can add context about escalation clauses, minimum commitments or renewal windows. The value comes from joining approved contract facts to purchasing records, not from displaying a paragraph of AI-generated prose next to every chart.
In operations, inspection images and reports can enrich an asset maintenance view. The structured system might record a failure code and repair cost; a reviewed summary of inspection evidence may help explain recurring patterns. A model-generated observation should remain distinguishable from a technician’s confirmed finding.
In customer service, support attachments and correspondence may help classify the causes of escalations. In finance, management commentary and footnotes can contextualize numerical results. In logistics, bills of lading and exception documents may explain delays that event timestamps alone cannot. These are candidate use cases, not claims that a particular deployment has achieved a measured result.
A useful selection test asks four questions. Does the content change a decision? Can it be linked reliably to an entity? Can its interpretation be checked? Is there a repeatable process for handling errors? If the answer to any is no, start with a smaller research or search use case instead of feeding it into an executive metric.
When to keep the files outside Snowflake
Not every unstructured workload belongs inside a warehouse. A document management system may already provide authoritative versioning, legal hold, approvals, e-signatures and retention controls. A product content platform may manage millions of high-resolution images with specialized editing and delivery needs. A clinical or legal workflow may require review processes that a SQL function cannot supply.
In those situations, Snowflake can hold approved metadata, controlled file references, extracted attributes and analysis outputs while the authoritative original stays in its system of record. This often gives analysts the necessary context without duplicating sensitive documents or recreating a document-management application.
The boundary should be explicit: which system owns the original, which one owns the approved fact, which one serves the summary, and which one records the human decision. A warehouse is excellent at joining and analyzing records. It is not automatically the right interface for editing contracts, approving claims or managing evidentiary photographs.
For a broader approach to making existing data usable for enterprise AI, Actiknow’s AI-ready data architecture guide explains why lineage, permissions and data quality matter before selecting an AI interface.
A practical reference architecture
A controlled design separates six responsibilities rather than treating summarization as one giant pipeline.
- Source and intake. Files arrive from approved repositories, APIs, email workflows or operational applications. Capture an immutable source ID, originating system, owner and document type. Use a stable business key when linking to a contract, order or asset.
- File storage and access. Keep originals in an approved repository or a Snowflake-accessible stage according to security and retention requirements. Enforce least privilege on both the file and the derived output. A FILE reference is a means of access, not a substitute for an authorization policy.
- Metadata and quality checks. Store file name, content type, checksum or version identifier, received time, size, language where known, and processing status. Reject unsupported formats and flag corrupted or incomplete files before running AI.
- AI processing. Apply summarization, classification or extraction according to the business question. Record the function, execution time, source version and processing status. Retain errors for reprocessing. Do not overwrite a previously approved result simply because the same file was processed again.
- Validation and approval. Use deterministic rules for identifiers, amounts, dates and known reference values. For high-impact decisions, route uncertain or consequential interpretations to a named reviewer. Preserve the difference between raw AI output and approved business attributes.
- Consumption and monitoring. Publish approved fields to analytical marts and dashboards. Expose summaries with links back to the source where permitted. Track processing volume, failure rates, review load, costs and downstream corrections.
This architecture fits naturally with business intelligence and data modeling services that already address integration, modeling, dashboards and refresh mechanisms. The new component is an evidence-processing step, not a replacement for the governed reporting model.
A small SQL pattern, and what it does not prove
Snowflake documents creating a table of FILE objects from a stage directory, then calling AI_SUMMARIZE on each row. An illustrative pattern is:
CREATE TABLE doc_files AS SELECT TO_FILE(‘@APPROVED_DOCS’, RELATIVE_PATH) AS doc FROM DIRECTORY(@APPROVED_DOCS);
SELECT FL_GET_RELATIVE_PATH(doc) AS source_file, AI_SUMMARIZE(doc) AS summary FROM doc_files;
This example assumes a properly configured stage with directory metadata and a role authorized to read the files and use the AI function. It demonstrates summarization, not production readiness. A production design would also capture stable file identity, version or checksum, processing state, error details, data sensitivity, review status and the link to the business entity.
For a one-off review, returning a summary in a query may be enough. For a recurring workflow, persist results only after defining refresh and reprocessing behavior. If a source file changes, the old summary must not silently remain the authoritative version. If the function fails on one file, the batch should record that exception rather than imply the document was successfully reviewed.
Summarization also does not guarantee extraction of all material clauses, correct arithmetic in a scanned table, or faithful interpretation of a complex diagram. Those outcomes require separate tests and, sometimes, different tooling.
Summarization, extraction and retrieval: choose the right pattern
Use summarization when a person needs a quick overview of a long document or a collection of reports. Examples include briefing notes, initial triage of vendor reports or identifying themes across a research library. The result should be treated as a navigation aid until validated.
Use structured extraction when a downstream process needs exact fields. An invoice approval process needs invoice number, supplier, currency, total, tax and purchase order reference, not a fluent paragraph. Define a schema, validation rules and a route for exceptions. If an extracted amount cannot be reconciled to the source or accounting record, do not post it automatically.
Use retrieval or search when users need to ask questions and inspect evidence. A contract manager asking which agreements have termination rights needs the relevant clauses and citations, not only document-level summaries. Search may require indexing, chunking and permissions-aware retrieval even if summarization itself does not.
Use a hybrid approach when the workflow moves through stages: classify a file, extract selected fields, validate them, summarize unusual findings, and send exceptions for human approval. The order matters. A polished summary should not become a shortcut around validation.
Why governance becomes harder with unstructured data
Structured data usually has named columns and relatively visible relationships. Unstructured content can contain information that is easy to miss: personal details in an image, confidential pricing in an appendix, handwritten notes in a scan, or a privileged legal clause buried in a PDF.
Start with classification and access policy. Define which file types may be processed, which roles may invoke the function, and who may see summaries. Derived text can itself be sensitive even if it is shorter than the original. Apply appropriate retention and access rules to both.
Next, preserve provenance. A summary should carry the original file ID, version, processing timestamp and reviewer status. If an executive challenges a conclusion, the team must be able to retrieve the exact source version used at the time. A generic ‘summarized by AI’ label is not sufficient audit evidence.
Then test cross-boundary leakage. A user who cannot access a supplier’s confidential contract should not gain its details through a broad summary table or an analytics assistant. Review stage privileges, result-table permissions, sharing behavior and downstream BI filters together. Do not assume the original repository’s permissions automatically flow to every derived table.
Finally, define deletion and correction behavior. If a source is withdrawn, redacted or superseded, determine what happens to stored summaries, indexes, caches and exported reports. Governance is a lifecycle problem, not merely a one-time access grant.
Accuracy testing: what executives should require
A useful pilot needs a representative evaluation set, not just attractive demonstrations. Sample short and long documents, scanned and digital PDFs, diagrams, mixed-language content, poor-quality images, tables, unusual layouts, and documents with conflicting or outdated statements.
For summaries, reviewers should score whether critical facts are retained, unsupported claims are introduced, qualifiers are lost, and numerical statements remain faithful. A good-looking summary can still omit the one clause that changes a contract decision. Assess omissions as seriously as outright hallucinations.
For extraction, measure field-level accuracy and business-rule failures separately. An incorrect renewal date may be more consequential than an imperfect description. For visual content, test whether the model distinguishes labels, arrows, units and legends correctly; do not assume that understanding the general theme of a chart means reading its values accurately.
Segment results by document type and quality. An overall pass rate can conceal poor performance on the most sensitive category. Include a defined human-review threshold, even if the threshold is a business rule rather than a model confidence score. Track the proportion of documents requiring intervention and the time spent correcting outputs.
Most importantly, measure the business workflow. If analysts still need to open every original and verify every line, a summarization step may improve navigation but not reduce review effort. That can still be valuable, but the benefit should be described accurately.
Cost, performance and operational controls
AI processing has different economics from ordinary SQL aggregation. Snowflake’s Cortex AI function cost guidance explains that token-based functions such as AI_SUMMARIZE can incur charges for input and output tokens, while warehouse compute costs may also apply. Media token accounting and regional support should be checked against the current service documentation.
Estimate volume before scaling. Count documents per day, average pages or image sizes, expected reprocessing, and whether users will repeatedly request the same summary. A simple design can process a changed file once and reuse a reviewed result, rather than resummarizing an unchanged document every time someone opens a dashboard.
Introduce budgets and usage visibility. Monitor function consumption, document counts, failure rates, average processing time and the proportion of summaries that are actually used. An alert on a sudden increase in processed files can catch a bad ingestion loop before it becomes a large bill.
Plan for asynchronous processing when volumes are substantial. A dashboard should not need to wait for hundreds of PDF summaries to complete. Keep analytical consumption separate from batch processing, and make the last successful processing time visible.
Finally, compare alternatives. A specialist OCR or document extraction service may be preferable for high-volume standardized forms. A search index may be better for interactive knowledge discovery. A custom application may be needed for approvals and audit trails. Snowflake’s advantage is strongest when the outputs are closely tied to existing analytical data and governed SQL workflows.
An illustrative decision: supplier contract analytics
Imagine a company with purchasing transactions in Snowflake and supplier contracts stored as PDFs. Procurement wants to identify suppliers whose current prices may conflict with contract terms. This is a hypothetical design example, not a client case study.
The first temptation is to summarize every contract and ask an AI assistant to identify overcharges. That skips important controls. Contracts may have amendments, different currencies, effective dates, exceptions and tiered pricing. A summary cannot by itself establish whether a charge is contractually invalid.
A better workflow begins by mapping each contract and amendment to the correct supplier and period. Summarization helps reviewers locate relevant commercial terms. A separate extraction process identifies candidate price clauses and dates. Deterministic checks compare approved fields with purchase-order and invoice data. Discrepancies go to a procurement specialist with the original clause attached.
The dashboard then reports reviewed exceptions, potential exposure and resolution status. It does not label every model-suggested discrepancy as confirmed savings. This design connects multimodal analysis to a measurable decision while preserving accountability.
The same discipline applies to maintenance images, claims attachments and compliance reports. Start with an entity and a decision, not a warehouse full of unclassified files.
A 45-day pilot that produces a decision
Days 1-7: Select one document class and one business decision. Define the source repository, owner, sensitivity, required metadata, expected daily volume and success criteria. Collect a representative test set and document the manual baseline.
Days 8-15: Configure approved file access, stage or reference strategy, and a small metadata table. Test supported formats and limits in the actual Snowflake region. Confirm access restrictions using at least two roles with different privileges.
Days 16-25: Implement a bounded summarization or extraction workflow. Capture version identifiers, execution results and errors. Review outputs against the test set and refine the acceptance rules. Do not expand to more document types yet.
Days 26-35: Connect approved outputs to the business entity and analytical mart. Build a small report showing source coverage, review status, exceptions and processing cost. Validate that users can open only the source documents they are authorized to see.
Days 36-45: Run with real users. Measure factual accuracy, manual correction time, exception rate, latency and spend. Decide whether to expand, change tools, restrict the use case to research, or stop. A pilot is successful when it produces a defensible investment decision, even if that decision is not to deploy the feature broadly.
Executive checklist before production
Ask the team to demonstrate an end-to-end record: the original document, its source version, processing event, summary, any extracted fields, validation result, approval status and the final dashboard entry. If one link in that chain is missing, the workflow may not be ready for consequential decisions.
Require named owners for data access, AI output quality, operational monitoring and business approval. Set a review process for changes to source formats and analytical rules. Define which outputs are advisory and which can trigger actions.
Agree on success metrics in advance. Good candidates include the percentage of relevant documents covered, time to locate evidence, reviewed extraction accuracy, exception turnaround time, cost per accepted document and downstream correction rate. Avoid reporting only how many summaries were generated.
For organizations combining documents with operational systems, custom integration and automation work may be needed to manage intake, approvals and exception routing around the analytical layer. That surrounding workflow often determines whether the AI capability becomes useful in production.
Frequently asked questions
What is Snowflake multimodal analytics?
It is the use of non-tabular inputs such as documents and images within Snowflake-based analytical workflows. The output can be a summary, extracted attribute or other derived signal that is joined to business data, subject to validation and governance.
Is AI_SUMMARIZE generally available?
Snowflake’s September 14, 2026 announcement describes multimodal AI_SUMMARIZE as public preview, and the function reference labels it Preview. Verify current availability, file support and regional limitations in your own environment before committing to production dependencies.
Can AI_SUMMARIZE read PDFs and images?
The function reference documents FILE input for supported documents and images. Formats, size limits, page limits and context windows vary by region and deployment. Do not assume every Office file, scan or image is supported everywhere.
Does summarization replace OCR or document extraction?
No. Summarization produces a concise interpretation. OCR converts visible text into machine-readable text, while extraction targets specific fields. A business workflow may need more than one of these capabilities and independent validation.
Can the output be used directly in an executive dashboard?
A summary can be displayed as advisory context, with source attribution and appropriate permissions. Material figures, contractual obligations or compliance conclusions should be validated and approved before becoming authoritative dashboard metrics.
Should every enterprise document be copied into Snowflake?
No. Keep originals in the system best suited to their lifecycle and legal requirements. Bring approved references, metadata and derived attributes into Snowflake when they support an analytical use case.
How should sensitive documents be handled?
Classify them before processing, restrict stage and result access, test roles, preserve provenance and apply retention rules to both originals and generated summaries. Derived summaries can expose confidential information.
How do we measure accuracy?
Use representative documents and human-reviewed expected outcomes. Score omissions, invented statements, numerical fidelity and field-level correctness separately, and segment results by document type and quality.
How do we prevent runaway processing costs?
Process changed documents rather than every document on every query, monitor token and credit usage, retain approved results where appropriate, set spending alerts and make batch failures visible. Reconcile estimates with current Snowflake pricing and usage views.
When is a separate document AI service a better choice?
Consider it when the core workload is specialized form extraction, document editing, high-volume OCR, legal workflow or search that is not closely connected to governed warehouse data. Compare end-to-end accuracy, integration, control and cost rather than only the number of services.
The decision is about evidence, not file formats
The strategic benefit of multimodal analytics is the ability to connect explanations, clauses, diagrams and observations to the business records executives already trust. Snowflake’s new summarization capability makes one part of that work easier, but it does not eliminate source governance, validation or accountable review.
Start with one decision that structured data cannot explain well enough. Link the right documents to that decision. Test the outputs against the originals. Keep advisory summaries separate from approved facts. Scale only after the workflow, not just the function, proves useful.
If you are evaluating how document evidence could support your reporting or operational analytics, contact Actiknow to discuss the data integration, governance and dashboard requirements for a focused pilot.