SNP Advances AI for Unstructured Data in M&A with Kyano Oros

M&A teams spend great effort understanding what is inside ERP systems. They map financial data, customer records, vendors, open orders, plants, materials, contracts, employees, and legal entities. For SAP customers, that work is already complex because business processes often span SAP and non-SAP systems.

But a large part of the deal picture sits outside structured ERP data. Unstructured data is often where deal risk hides.

Per SNP, unstructured data accounts for approximately 80% of enterprise data volumes and has remained largely inaccessible to traditional transformation tools. For M&A teams, that creates a blind spot. The structured ERP data may appear well understood, while the obligations, dependencies, and risks around it remain fragmented and buried.

To close that gap, SNP is advancing its AI capabilities for M&A through Kyano Oros, a new extension of its Kyano platform. Introduced during SNP’s Transformation World 2026, Kyano Oros brings document-based information into the transformation workflow, including contracts, HR records, IT documentation, customer complaints, service agreements, trade secrets, and other records that can shape deal risk, separation planning, compliance exposure, and Day 1 readiness.

Why Kyano Oros Matters for M&A

Data risk rarely sits in one system. The relevant records may be spread across shared drives, content systems, email archives, HR platforms, contract repositories, IT documentation, and regional business applications. That makes it difficult for deal teams to know whether they have identified the documents that should move with a business, remain behind, be protected, or be excluded from a transaction.

In a carve-out, that distinction can determine whether the separation is clean. Sellers need to identify which information belongs to the business being separated and which information must remain behind. Buyers need enough visibility to understand what they are acquiring, where obligations sit, and which data will support Day 1 operations. Sensitive employee information, customer commitments, change-of-control clauses, trade secrets, and records belonging to the retained business can all affect diligence, compliance exposure, and post-close operations.

Kyano Oros builds on SNP’s joint venture with Structify through Oros Data LLC, which focuses on AI-enabled processing of unstructured enterprise data at scale. SNP is extending Kyano’s existing role in migration, restructuring, divestiture, integration, archiving, and decommissioning work, rather than treating unstructured data as a separate document search problem.

That platform context is what makes the M&A use case practical. AI can help organizations find and interpret document-based information faster, but deal teams still need governance over what is classified, protected, retained, transferred, or excluded. The value is faster review with traceability, policy control, and defensible decisions.

What SAP Teams Should Do Before the Next Deal

The announcement should prompt SAP and data leaders to revisit how they define M&A readiness. Too often, preparation focuses on structured data and application scope, while unstructured data remains a later legal, records management, or compliance workstream.

That sequencing creates risk. If document repositories, contract stores, HR records, and IT documentation are not understood until late in the transaction, the business may discover critical issues after timelines are already compressed.

SAP teams should start by identifying where high-value and high-risk unstructured data sits across the enterprise. They should map which repositories are tied to legal entities, business units, regions, customers, suppliers, and regulated information. They should also define how sensitive data will be classified, protected, retained, transferred, or excluded during a transaction.

This work also connects to SAP S/4HANA transformation. As organizations modernize ERP, retire legacy systems, or move data into lower-cost storage, they need to decide what happens to the documents and records surrounding the structured data. If unstructured content remains unmanaged, the landscape may look cleaner in ERP while risk continues to sit in disconnected repositories.

What This Means for SAPinsiders

Unstructured data expands the scope of SAP transformation. SAPinsiders should not treat contracts, HR records, IT documentation, customer complaints, and other document-based information as separate from ERP modernization or M&A planning. These records often carry the obligations, risks, and dependencies that determine whether a transaction or transformation can be executed cleanly.

M&A data readiness requires both speed and defensibility. AI-enabled processing can help teams review and contextualize large volumes of unstructured data faster, but SAP teams still need governance over sensitive information, trade secrets, personal data, retention rules, and transfer decisions. The next phase of M&A readiness will favor organizations that can move quickly without losing control over what data is exposed, retained, or separated.

The SAP data landscape is broader than the SAP data model. Structured ERP data remains essential, but it no longer provides the full picture of business operations, risk, or value. SAPinsiders should build transformation plans that connect structured and unstructured data across migration, divestiture, archiving, decommissioning, and AI use cases so future decisions are based on a more complete view of the enterprise.