LLM platforms such as ChatGPT, Claude, and Gemini, and agent-based AI like Agentforce are transforming how enterprises operate. However, realizing their full potential requires a strong data foundation that enterprises find challenging.
While enterprises want a hybrid data stack, their data is often distributed across environments and limited by API constraints, making it difficult to extract data at scale, at high frequencies, and in real-time – all while maintaining security. Unlocking and unifying data is the next architectural step toward bridging the gap between AI ambition and execution.
External AI platforms face constraints: how can we easily maintain a complete, searchable copy of Salesforce data that can be queried instantly without strict limits or timeouts? Whether integrating Salesforce data into Snowflake, Databricks, or AI training pipelines, enterprises encounter significant challenges:
- Insufficient API data extraction speed disrupts scalability and real-time data access.
- Unstable API timeouts can break workflows and delay insights.
- Incomplete or inconsistent exports undermine analytics accuracy.
For Data 360 and Agentforce, the challenge is accessing historical records that are frequently stored in cold or off-platform environments, but are essential to achieving a unified and complete Customer 360 experience. Without them, AI agents operate only with the data they’re trained with – limiting visibility and reducing personalization and accuracy.
This creates a paradox: enterprises possess some of the world’s richest customer data yet cannot efficiently deliver it to the Data Science & Machine Learning platforms and AI systems that need it most, as fast and efficiently as they’d like. The bottleneck isn’t in the models: it’s in the architecture. How can teams ensure they harness all of the power Salesforce customer data has to offer in this new paradigm?
Insights from Salesforce’s Data Report
These challenges are highlighted by Salesforce’s State of Data and Analytics 2026 report. This report states that 84% of data and analytics leaders acknowledge that their data strategies need a complete overhaul before AI can achieve its full potential.
Salesforce’s recent research highlights the depth of the challenge:
- 89% of data and analytics leaders have experienced inaccurate or misleading AI outputs caused by poor data foundations.
- 19% of data remains siloed or inaccessible, and 70% of leaders believe their most valuable insights are trapped within that 19%.
- 49% of leaders admit they cannot consistently generate timely insights.
These statistics reflect a core issue: legacy data architectures simply cannot support the demands of modern AI workloads. Fragmented systems, manual ETL processes, and slow replication cycles prevent the agility required for AI.
The Case for a New Data Architecture
To close the gap between data strategy and AI execution, enterprises must rethink how Salesforce data is structured, accessed, and governed. Traditional ETL pipelines struggle to accommodate real-time and high-volume AI workloads. A new approach must emphasize:
- Zero-copy: enabling direct access of data without physical replication to and from systems like Data 360, Snowflake, Databricks, and multi-cloud data ecosystems.
- Real-Time Access: ensuring AI models always operate on the freshest data
- High Availability: ensuring data is always accessible, even during a Salesforce outage
- Scalability: maintaining stability and speed under intensive analytical and AI workloads.
- Security and Governance: bringing sovereignty, observability and end-to-end encryption while accessing or zero-copying data.
This architecture-first mindset allows Salesforce data to evolve from an operational asset to a strategic enabler of AI-driven transformation.
Unlock and Unify Customer Data at Scale
The culmination of 13 years of R&D: we announced Odaseva Data Edge at Dreamforce, a breakthrough architecture built for the AI era.
Data Edge unifies Salesforce, ServiceNow, and other CRM data outside of Salesforce into query-ready datasets for LLM training, autonomous agents, and advanced analytics.
It also provides real-time, zero-copy access secured with end-to-end encryption. This elevated access to Salesforce data allows customers to improve their Salesforce availability and operate at 99.99% uptime for mission-critical business processes.
A large bank compared the speed of Data Edge against Salesforce Bulk API V2.0 and measured it to be 1800× faster, demonstrating how enterprises can unlock the full potential of Salesforce data.
Powering the Hybrid AI Enterprise
Modern enterprises are moving toward hybrid AI strategies that combine Agentforce and Data 360 with external AI ecosystems. A unified architecture makes this possible:
- Fuel Agentforce and Data 360: Provide full historical and archived Salesforce data for richer context and smarter AI-driven automation.
- Enable LLMs: training and fine-tuning of models with complete Salesforce datasets, improving prediction accuracy.
- Power analytics: Supports real-time dashboards and insights that merge operational CRM data with broader enterprise datasets, without waiting for ETL cycles.
This architecture bridges the data divide that limits AI adoption. By unifying Salesforce data and eliminating silos, enterprises gain the foundation needed for scalable, secure, and efficiently governed AI operations.
The Road Ahead
Enterprises seeking to operationalize AI must first modernize the data foundations that feed it: the AI revolution will be defined by its architecture. The most forward-looking organizations are already adopting zero-copy integration, real-time pipelines, and governed Lakehouse frameworks to achieve this goal.
Odaseva is helping drive this transformation by combining its Salesforce enterprise expertise with modern data architecture principles, transforming data by making it highly accessible, scalable, searchable, at speed, with trust.
See for yourself in our demo: watch the Data Innovation Forum session here.
