Breaking: Tech Giants Unite to Solve AI’s Biggest Bottleneck
The Open Semantic Interchange was announced by Snowflake in their official blog On September 23, 2025, something unprecedented happened in the data industry. Open Semantic Interchange (OSI), a groundbreaking initiative led by Snowflake, Salesforce, BlackRock, and dbt Labs, was announced to solve AI’s biggest problem. These 15+ technology companies would give away their data secrets—collaboratively creating the Open Semantic Interchange as an open, vendor-neutral standard for how business data is defined across all platforms.
This isn’t just another tech announcement. It’s the industry admitting that the emperor has no clothes.
For decades, every software vendor has defined business metrics differently. Your data warehouse calls it “revenue.” Your BI tool calls it “total sales.” Your CRM calls it “booking amount.” Your AI model? It has no idea they’re the same thing.
This semantic chaos has created what VentureBeat calls “the $1 trillion AI problem“—the massive hidden cost of data preparation, reconciliation, and the manual labor required before any AI project can begin.
Enter the Open Semantic Interchang (OSI)—a groundbreaking initiative that could become as fundamental to AI as SQL was to databases or HTTP was to the web.
What is Open Semantic Interchange (OSI)? Understanding the Semantic Standard
Open Semantic Interchange is an open-source initiative that creates a universal, vendor-neutral specification for defining and sharing semantic metadata across data platforms, BI tools, and AI applications.
The Simple Explanation of Open Semantic Interchange
Think of OSI as a Rosetta Stone for business data. Just as the ancient Rosetta Stone allowed scholars to translate between Egyptian hieroglyphics, Greek, and Demotic script, OSI allows different software systems to understand each other’s data definitions.
When your data warehouse, BI dashboard, and AI model all speak the same semantic language, magic happens:
- No more weeks reconciling conflicting definitions
- No more “which revenue number is correct?”
- No more AI models trained on misunderstood data
- No more rebuilding logic across every tool
Open Semantic Interchange Technical Definition
OSI provides a standardized specification for semantic models that includes:
Business Metrics: Calculations, aggregations, and KPIs (revenue, customer lifetime value, churn rate)
Dimensions: Attributes for slicing data (time, geography, product category)
Hierarchies: Relationships between data elements (country → state → city)
Business Rules: Logic and constraints governing data interpretation
Context & Metadata: Descriptions, ownership, lineage, and governance policies
Built on familiar formats like YAML and compatible with RDF and OWL, this specification stands out by being tailored specifically for modern analytics and AI workloads.
The $1 Trillion Problem: Why Open Semantic Interchange Matters Now
The Hidden Tax: Why Semantic Interchange is Critical for AI Projects
Every AI initiative begins the same way. Data scientists don’t start building models—they start reconciling data.
Week 1-2: “Wait, why are there three different revenue numbers?”
Week 3-4: “Which customer definition should we use?”
Week 5-6: “These date fields don’t match across systems.”
Week 7-8: “We need to rebuild this logic because BI and ML define margins differently.”
According to industry research, data preparation consumes 60-80% of data science time. For enterprises spending millions on AI, this represents a staggering hidden cost.
Real-World Horror Stories Without Semantic Interchange
Fortune 500 Retailer: Spent 9 months building a customer lifetime value model. When deployment came, marketing and finance disagreed on the “customer” definition. Project scrapped.
Global Bank: Built fraud detection across 12 regions. Each region’s “transaction” definition differed. Model accuracy varied 35% between regions due to semantic inconsistency.
Healthcare System: Created patient risk models using EHR data. Clinical teams rejected the model because “readmission” calculations didn’t match their operational definitions.
These aren’t edge cases—they’re the norm. The lack of semantic standards is silently killing AI ROI across every industry.
Why Open Semantic Interchange Now? The AI Inflection Point
Generative AI has accelerated the crisis. When you ask ChatGPT or Claude to “analyze Q3 revenue by region,” the AI needs to understand:
- What “revenue” means in your business
- How “regions” are defined
- Which “Q3” you’re referring to
- What calculations to apply
Without semantic standards, AI agents give inconsistent, untrustworthy answers. As enterprises move from AI pilots to production at scale, semantic fragmentation has become the primary blocker to AI adoption.
The Founding Coalition: Who’s Behind OSI
OSI isn’t a single-vendor initiative—rather it’s an unprecedented collaboration across the data ecosystem.
Companies Leading the Open Semantic Interchange Initiative
Snowflake: The AI Data Cloud company spearheading the initiative, contributing engineering resources and governance infrastructure
Salesforce (Tableau): Co-leading with Snowflake, bringing BI perspective and Tableau’s semantic layer expertise
dbt Labs: Furthermore,contributing the dbt Semantic Layer framework as a foundational technology
BlackRock:Moreover, representing financial services with the Aladdin platform, ensuring real-world enterprise requirements
RelationalAI:Finally, bringing knowledge graph and reasoning capabilities for complex semantic relationships
Launch Partners (17 Total)
BI & Analytics: ThoughtSpot, Sigma, Hex, Omni
Data Governance: Alation, Atlan, Select Star
AI & ML: Mistral AI, Elementum AI
Industry Solutions: Blue Yonder, Honeydew, Cube
This coalition represents competitors agreeing to open-source their competitive advantage for the greater good of the industry.
Why Competitors Are Collaborating on Semantic Interchange
As Christian Kleinerman, EVP Product at Snowflake, explains: “The biggest barrier our customers face when it comes to ROI from AI isn’t a competitor—it’s data fragmentation.”
Indeed, this observation highlights a critical industry truth. Rather than competing against other vendors, organizations are actually fighting against their own internal data inconsistencies. Moreover, this fragmentation costs enterprises millions annually in lost productivity and delayed AI initiatives.
Similarly, Southard Jones, CPO at Tableau, emphasizes the collaborative nature: “This initiative is transformative because it’s not about one company owning the standard—it’s about the industry coming together.”
In other words, the traditional competitive dynamics are being reimagined. Instead of proprietary lock-in strategies, therefore, the industry is choosing open collaboration. Consequently, this shift benefits everyone—vendors, enterprises, and end users alike.
Ryan Segar, CPO at dbt Labs: “Data and analytics engineers will now be able to work with the confidence that their work will be leverageable across the data ecosystem.”
The message is clear: Standardization isn’t a commoditizer—it’s a catalyst. Like USB-C didn’t hurt device makers, OSI won’t hurt data platforms. It shifts competition from data definitions to innovation in user experience and AI capabilities.
How Open Semantic Interchange (OSI) Works: Technical Deep Dive
The Open Semantic Interchange Specification Structure
OSI defines semantic models in a structured, machine-readable format. Here’s what a simplified OSI specification looks like:
Metrics Definition:
- Name, description, and business owner
- Calculation formula with explicit dependencies
- Aggregation rules (sum, average, count distinct)
- Filters and conditions
- Temporal considerations (point-in-time vs. accumulated)
Dimension Definition:
- Attribute names and data types
- Valid values and constraints
- Hierarchical relationships
- Display formatting rules
Relationships:
- How metrics relate to dimensions
- Join logic and cardinality
- Foreign key relationships
- Temporal alignment
Governance Metadata:
- Data lineage and source systems
- Ownership and stewardship
- Access policies and sensitivity
- Certification status and quality scores
- Version history and change logs
Open Semantic Interchange Technology Stack
Format: YAML (human-readable, version-control friendly)
Compilation: Engines that translate OSI specs into platform-specific code (SQL, Python, APIs)
Transport: REST APIs and file-based exchange
Validation: Schema validation and semantic correctness checking
Extension: Plugin architecture for domain-specific semantics
Integration Patterns
Organizations can adopt OSI through multiple approaches:
Native Integration: Platforms like Snowflake directly support OSI specifications
Translation Layer: Tools convert between proprietary formats and OSI
Dual-Write: Systems maintain both proprietary and OSI formats
Federation: Central OSI registry with distributed consumption
Real-World Use Cases: Open Semantic Interchange in Action
Use Case 1: Open Semantic Interchange for Multi-Cloud Analytics
Challenge: A global retailer runs analytics on Snowflake but visualizations in Tableau, with data science in Databricks. Each platform defined “sales” differently.
Before OSI:
- Data team spent 40 hours/month reconciling definitions
- Business users saw conflicting dashboards
- ML models trained on inconsistent logic
- Trust in analytics eroded
With OSI:
- Single OSI specification defines “sales” once
- All platforms consume the same semantic model
- Dashboards, notebooks, and AI agents align
- Data team focuses on new insights, not reconciliation
Impact: 90% reduction in semantic reconciliation time, 35% increase in analytics trust scores
Use Case 2: Semantic Interchange for M&A Integration
Challenge: A financial services company acquired three competitors, each with distinct data definitions for “customer,” “account,” and “portfolio value.”
Before OSI:
- 18-month integration timeline
- $12M spent on data mapping consultants
- Incomplete semantic alignment at launch
- Ongoing reconciliation needed
With OSI:
- Each company publishes OSI specifications
- Automated mapping identifies overlaps and conflicts
- Human review focuses only on genuine business rule differences
- Integration completed in 6 months
Impact: 67% faster integration, 75% lower consulting costs, complete semantic alignment
Use Case 3: Open Semantic Interchange Improves AI Agent Trust
Challenge: An insurance company deployed AI agents for claims processing. Agents gave inconsistent answers because “claim amount,” “deductible,” and “coverage” had multiple definitions.
Before OSI:
- Customer service agents stopped using AI tools
- 45% of AI answers flagged as incorrect
- Manual verification required for all AI outputs
- AI initiative considered a failure
With OSI:
- All insurance concepts defined in OSI specification
- AI agents query consistent semantic layer
- Answers align with operational systems
- Audit trails show which definitions were used
Impact: 92% accuracy rate, 70% reduction in manual verification, AI adoption rate increased to 85%
Use Case 4: Semantic Interchange for Regulatory Compliance
Challenge: A bank needed consistent risk reporting across Basel III, IFRS 9, and CECL requirements. Each framework defined “exposure,” “risk-weighted assets,” and “provisions” slightly differently.
Before OSI:
- Separate data pipelines for each framework
- Manual reconciliation of differences
- Audit findings on inconsistent definitions
- High cost of compliance
With OSI:
- Regulatory definitions captured in domain-specific OSI extensions
- Single data pipeline with multiple semantic views
- Automated reconciliation and variance reporting
- Full audit trail of definition changes
Impact: 60% lower compliance reporting costs, zero audit findings, 80% faster regulatory change implementation
Industry Impact by Vertical
Financial Services
Primary Benefit: Regulatory compliance and cross-platform consistency
Key Use Cases:
- Risk reporting across frameworks (Basel, IFRS, CECL)
- Trading analytics with market data integration
- Customer 360 across wealth, retail, and commercial banking
- Fraud detection with consistent entity definitions
Early Adopter: BlackRock’s Aladdin platform, which already unifies investment management with common data language
Healthcare & Life Sciences
Primary Benefit: Clinical and operational data alignment
Key Use Cases:
- Patient outcomes research across EHR systems
- Claims analytics with consistent diagnosis coding
- Drug safety surveillance with adverse event definitions
- Population health with social determinants integration
Impact: Enables federated analytics while respecting patient privacy
Retail & E-Commerce
Primary Benefit: Omnichannel consistency and supply chain alignment
Key Use Cases:
- Customer lifetime value across channels (online, mobile, in-store)
- Inventory optimization with consistent product hierarchies
- Marketing attribution with unified conversion definitions
- Supply chain analytics with vendor data integration
Impact: True omnichannel understanding of customer behavior
Manufacturing
Primary Benefit: OT/IT convergence and supply chain interoperability
Key Use Cases:
- Predictive maintenance with consistent failure definitions
- Quality analytics across plants and suppliers
- Supply chain visibility with partner data
- Energy consumption with sustainability metrics
Impact: End-to-end visibility from raw materials to customer delivery
Open Semantic Interchange Implementation Roadmap
Phase 1: Foundation (Q4 2025 – Q1 2026)
Goals:
- Publish initial OSI specification v1.0
- Release reference implementations
- Launch community forum and GitHub repository
- Establish governance structure
Deliverables:
- Core specification for metrics, dimensions, relationships
- YAML schema and validation tools
- Sample semantic models for common use cases
- Developer documentation and tutorials
Phase 2: Ecosystem Adoption (Q2-Q4 2026)
Goals:
- Native support in major data platforms
- Translation tools for legacy systems
- Domain-specific extensions (finance, healthcare, retail)
- Growing library of shared semantic models
Milestones:
- 50+ platforms supporting OSI
- 100+ published semantic models
- Enterprise adoption case studies
- Certification program for OSI compliance
Phase 3: Industry Standard (2027+)
Goals:
- Recognition as de facto standard
- International standards body adoption
- Regulatory recognition in key industries
- Continuous evolution through community
Vision:
- OSI as fundamental as SQL for databases
- Semantic models as reusable as open-source libraries
- Cross-industry semantic model marketplace
- AI agents natively understanding OSI specifications
Open Semantic Interchange Benefits for Different Stakeholders
Data Engineers
Before OSI:
- Rebuild semantic logic for each new tool
- Debug definition mismatches
- Manual data reconciliation pipelines
With OSI:
- Define business logic once
- Automatic propagation to all tools
- Focus on data quality, not definition mapping
Time Savings: 40-60% reduction in pipeline development time
Data Analysts
Before OSI:
- Verify metric definitions before trusting reports
- Recreate calculations in each BI tool
- Reconcile conflicting dashboards
With OSI:
- Trust that all tools use same definitions
- Self-service analytics with confidence
- Focus on insights, not validation
Productivity Gain: 3x increase in analysis output
Open Semantic Interchange Benefits for Data Scientists
Before OSI:
- Spend weeks understanding data semantics
- Build custom feature engineering for each project
- Models fail in production due to definition drift
With OSI:
- Leverage pre-defined semantic features
- Reuse feature engineering logic
- Production models aligned with business systems
Impact: 5-10x faster model development
How Semantic Interchange Empowers Business Users
Before OSI:
- Receive conflicting reports from different teams
- Unsure which numbers to trust
- Can’t ask AI agents confidently
With OSI:
- Consistent numbers across all reports
- Trust AI-generated insights
- Self-service analytics without IT
Trust Increase: 50-70% higher confidence in data-driven decisions
Open Semantic Interchange Value for IT Leadership
Before OSI:
- Vendor lock-in through proprietary semantics
- High cost of platform switching
- Difficult to evaluate best-of-breed tools
With OSI:
- Freedom to choose best tools for each use case
- Lower switching costs and negotiating leverage
- Faster time-to-value for new platforms
Strategic Flexibility: 60% reduction in platform lock-in risk
Challenges and Considerations
Challenge 1: Organizational Change for Semantic Interchange
Issue: OSI requires organizations to agree on single source of truth definitions—politically challenging when different departments define metrics differently.
Solution:
- Start with uncontroversial definitions
- Use OSI to make conflicts visible and force resolution
- Establish data governance councils
- Frame as risk reduction, not turf battle
Challenge 2: Integrating Legacy Systems with Semantic Interchange
Issue: Older systems may lack APIs or semantic metadata capabilities.
Solution:
- Build translation layers
- Gradually migrate legacy definitions to OSI
- Focus on high-value use cases first
- Use OSI for new systems, translate for old
Challenge 3: Specification Evolution
Issue: Business definitions change—how does OSI handle versioning and migration?
Solution:
- Built-in versioning in OSI specification
- Deprecation policies and timelines
- Automated impact analysis tools
- Backward compatibility guidelines
Challenge 4: Domain-Specific Complexity
Issue: Some industries have extremely complex semantic models (e.g., derivatives trading, clinical research).
Solution:
- Domain-specific OSI extensions
- Industry working groups
- Pluggable architecture for specialized needs
- Start simple, expand complexity gradually
Challenge 5: Governance and Ownership
Issue: Who owns the semantic definitions? Who can change them?
Solution:
- Clear ownership model in OSI metadata
- Approval workflows for definition changes
- Audit trails and change logs
- Role-based access control
How Open Semantic Interchange Shifts the Competitive Landscape
Before OSI: The Walled Garden Era
Vendors competed by locking in data semantics. Moving from Platform A to Platform B meant rebuilding all your business logic.
This created:
- High switching costs
- Vendor power imbalance
- Slow innovation (vendors focused on lock-in, not features)
- Customer resentment
After OSI: The Innovation Era
With semantic portability, vendors must compete on:
- User experience and interface design
- AI capabilities and intelligence
- Performance and scalability
- Integration breadth and ease
- Support and services
Southard Jones (Tableau): “Standardization isn’t a commoditizer—it’s a catalyst. Think of it like a standard electrical outlet: the outlet itself isn’t the innovation, it’s what you plug into it.”
This shift benefits customers through:
- Better products (vendors focus on innovation)
- Lower costs (competition increases)
- Flexibility (easy to switch or multi-source)
- Faster AI adoption (semantic consistency enables trust)
How to Get Started with Open Semantic Interchange (OSI)
For Enterprises
Step 1: Assess Current State (1-2 weeks)
- Inventory your data platforms and BI tools
- Document how metrics are currently defined
- Identify semantic conflicts and pain points
- Estimate time spent on definition reconciliation
Step 2: Pilot Use Case (1-2 months)
- Choose a high-impact but manageable scope (e.g., revenue metrics)
- Define OSI specification for selected metrics
- Implement in 2-3 key tools
- Measure impact on reconciliation time and trust
Step 3: Expand Gradually (6-12 months)
- Add more metrics and dimensions
- Integrate additional platforms
- Establish governance processes
- Train teams on OSI practices
Step 4: Operationalize (Ongoing)
- Make Open semantic interchange part of standard data modeling
- Integrate into data governance framework
- Participate in community to influence roadmap
- Share learnings and semantic models
For Technology Vendors
Kickoff Phase: Evaluate Strategic Fit (Immediate)
- Review Open semantic interchange specification
- Assess compatibility with your platform
- Identify required engineering work
- Estimate go-to-market impact
Next : Join the Initiative (Q4 2025)
- Become an Open semantic interchange partner
- Participate in working groups
- Contribute to specification development
- Collaborate on reference implementations
Strenghthen the core: Implement Support (2026)
- Add OSI import/export capabilities
- Provide migration tools from proprietary formats
- Update documentation and training
- Certify OSI compliance
Finally: Differentiate (Ongoing)
- Build value-added services on top of OSI
- Focus innovation on user experience
- Lead with interoperability messaging
- Partner with ecosystem for joint solutions
The Future: What’s Next for Open Semantic Interchange
2025-2026: Specification & Early Adoption
- Initial specification published (Q4 2025)
- Reference implementations released
- Major vendors announce support
- First enterprise pilot programs
- Community formation and governance
2027-2028: Mainstream Adoption
- OSI becomes default for new projects
- Translation tools for legacy systems mature
- Domain-specific extensions proliferate
- Marketplace for shared semantic models emerges
- Analyst recognition as emerging standard
2029-2030: Industry Standard Status
- International standards body adoption
- Regulatory recognition in financial services
- Built into enterprise procurement requirements
- University curricula include Open semantic interchange
- Semantic models as common as APIs
Long-Term Vision
The Semantic Web Realized: Open semantic interchange could finally deliver on the promise of the Semantic Web—not through abstract ontologies, but through practical, business-focused semantic standards.
AI Agent Economy: When AI agents understand semantics consistently, they can collaborate across organizational boundaries, creating a true agentic AI ecosystem.
Data Product Marketplace: Open semantic interchange enables data products with embedded semantics, making them immediately usable without integration work.
Cross-Industry Innovation: Semantic models from one industry (e.g., supply chain optimization) could be adapted to others (e.g., healthcare logistics) through shared Open semantic interchange definitions.
Conclusion: The Rosetta Stone Moment for AI
Conclusion: The Rosetta Stone Moment for AI
The launch of Open Semantic Interchange marks a watershed moment in the data industry. For the first time, fierce competitors have set aside proprietary advantages to solve a problem that affects everyone: semantic fragmentation.
However, this isn’t just about technical standards—rather, it’s about unlocking a trillion dollars in trapped AI value.
Specifically, when every platform speaks the same semantic language, AI can finally deliver on its promise:
- First, trustworthy insights that business users believe
- Second, fast time-to-value without months of data prep
- Third, flexible tool choices without vendor lock-in
- Finally, scalable AI adoption across the enterprise
Importantly, the biggest winners will be organizations that adopt early. While others struggle with semantic reconciliation, early adopters will be deploying AI agents, building sophisticated analytics, and making data-driven decisions with confidence.
Ultimately, the question isn’t whether Open Semantic Interchange will become the standard—instead, it’s how quickly you’ll adopt it to stay competitive.
The revolution has begun. Indeed, the Rosetta Stone for business data is here.
So, are you ready to speak the universal language of AI?
Key Takeaways
- Open Semantic Interchange launched Sept 23, 2025, by Snowflake, Salesforce, BlackRock, dbt Labs, and 15+ partners
- Solves the $1 trillion AI problem of data fragmentation and semantic inconsistency
- Creates vendor-neutral, open-source standard for defining business metrics and semantics
- Enables AI agents, BI tools, and data platforms to understand data definitions consistently
- Uses YAML format with compilation engines for platform-specific implementation
- Benefits all stakeholders: engineers, analysts, data scientists, business users, and IT leaders
- Initial specification expected Q4 2025, mainstream adoption by 2027-2028
- Shifts competitive dynamics from semantic lock-in to innovation in features and UX
- Real-world ROI: 60-90% reduction in reconciliation time, 35-70% increase in data trust
- Early adoption provides significant competitive advantage in AI-driven decision-making