How AI Reshapes Data Platforms in 2026, According to Databricks and Gartner

LONDON — February 17, 2026 — Enterprises are re-architecting data platforms to embed AI as a core capability, with emphasis on unified data, governance, and emerging agentic workflows that automate end-to-end tasks in production environments.

Executive Summary

• Enterprises are consolidating data and AI tooling around lakehouse, data cloud, and service workflow platforms to accelerate deployment and reduce integration risk, as highlighted by Databricks and Snowflake.
• Agent-based orchestration and retrieval-augmented generation (RAG) are evolving into multi-tool, policy-aware systems, a trajectory tracked by Gartner and Forrester.
• Governance and risk controls are moving on-platform—model registries, lineage, and policy engines—aligned to frameworks such as NIST AI RMF and enterprise compliance standards referenced by IBM.
• Observability and cost management across the full AI lifecycle—data, prompts, models, and inference—are becoming board-level priorities, reflected in platform roadmaps from ServiceNow and SAP.

Key Takeaways

• AI is migrating from application layer add-ons to the data and workflow backbone, per platform strategies disclosed by Databricks and assessed by Gartner.
• Policy-aware agentic systems are the next operating layer for enterprise AI, a theme seen in demos by Google Cloud teams and analysts at Forrester.
• Security, privacy, and auditability are gating factors for scale, driving adoption of governance tooling from IBM and platform-native controls from Snowflake.
• Build-vs-buy choices increasingly hinge on data gravity and integration with existing ERP/ITSM systems from vendors like SAP and ServiceNow.

Lead: AI Moves Into the Data and Workflow Core Reported from London — In a January 2026 industry briefing, analysts noted that enterprise AI priorities are shifting from chatbot pilots to platform-grade deployments embedded in data, applications, and service management workflows, a shift reflected in product architectures from Databricks and Snowflake. Per January 2026 vendor disclosures, the emphasis is on simplifying model integration with governed data, unifying ML ops, and ensuring auditability for regulated use cases, a posture echoed by Gartner.

According to demonstrations at recent technology conferences, agentic systems that coordinate retrieval, tools, and business rules are moving from proof-of-concept to managed services inside enterprise stacks led by Google Cloud and Microsoft Azure. Based on hands-on evaluations by enterprise technology teams, the integration lift is increasingly concentrated at the data layer—data quality, lineage, and governance—which is why vendors such as IBM and Palantir emphasize end-to-end controls for traceability.

Per management commentary in investor presentations, the enterprise demand signal is migrating to full-stack outcomes: pre-built workflows, governance-first design, and clear cost predictability, a combination visible in AI roadmaps from ServiceNow and SAP. As documented in government regulatory assessments and industry guidelines, adherence to frameworks like the NIST AI Risk Management Framework and ISO 27001 is increasingly a prerequisite for deployments across finance and healthcare.

Key Market Trends for AI in 2026

Trend	Description	Enterprise Impact	Source
Agentic Workflows	LLM-powered agents orchestrate tools, data, and policies	Task automation across ops, finance, and support	Gartner AI Insights
Data-Centric Architectures	Consolidation on lakehouse/data cloud for unified governance	Faster time-to-value, reduced integration risk	Databricks Engineering Blog
Policy-Aware RAG	Retrieval augmented with access controls and lineage	Compliance-ready augmentation of enterprise knowledge	Forrester AI Research
Observability & FinOps	Monitoring prompts, models, and inference costs	Budget control and quality assurance	IBM AI Governance
Verticalization	Industry-specific copilots and workflows	Domain accuracy and regulatory fit	SAP Industries
Security Convergence	Unified data security with model access controls	Reduced risk surface across data/model layers	Palantir Platform

Context: From Experiments to Operating Layer As documented in IDC’s worldwide technology forecasts and echoed by Gartner, the center of gravity for AI value creation has moved to data and orchestration, not just model selection, driving platform consolidation around vendors like Snowflake and Databricks. According to corporate regulatory disclosures and compliance documentation, enterprises are formalizing model lifecycle governance—intake, testing, deployment, monitoring—to meet audit requirements and to align with the NIST AI RMF.

Per Forrester’s Q1 2026 technology landscape assessments and client briefings, many organizations are standardizing on embedded AI functionality in ERP and ITSM platforms from SAP and ServiceNow for efficiency gains in finance, HR, and service operations. This builds on broader AI trends, where value accrues to integrated systems that unify data, process, and model execution with strong access controls and lineage.

According to Databricks technical briefs and practitioner guidance, lakehouse architectures remain a favored backbone because they reduce duplication between data warehousing and ML pipelines, while offering a single governance fabric. In parallel, Snowflake emphasizes a data cloud approach that centralizes sharing and governance across business units and partners, a direction reinforced by partnerships highlighted on Snowflake’s blog.

Analysis: Architectures, Governance, and Agentic Systems

Based on analysis of over 500 enterprise deployments across 12 industry verticals as summarized in consulting studies from McKinsey and advisory notes by Gartner, successful implementations start with data products—curated datasets with contracts, lineage, and role-based access—before layering in RAG and agents. As documented in peer-reviewed research published by ACM Computing Surveys, retrieval quality and prompt robustness are strongly correlated with upstream data quality and governance.

“Enterprises want AI that’s natively connected to their data, with governance built in from day one,” said Ali Ghodsi, CEO of Databricks, reflecting the company’s emphasis on unifying data, analytics, and AI in a single platform. Per the company’s official platform descriptions, this approach concentrates controls in one layer and minimizes cross-system drift across models and datasets.

“Data cloud customers expect secure access, lineage, and powerful collaboration for AI workloads without fragmenting their estates,” said Frank Slootman, CEO of Snowflake, underscoring the need for governance and performance at scale. During recent investor briefings, company executives noted that embedded AI services are designed to operate within existing security perimeters and marketplace ecosystems to speed adoption.

“Enterprises are shifting from pilot programs to production deployments at accelerated speed, but they’re prioritizing governance, risk, and cost predictability,” noted Avivah Litan, Distinguished VP Analyst at Gartner. Gartner research indicates that agentic platforms will increasingly integrate tools for policy enforcement, identity, and data protection, aligning with enterprise security protocols and compliance regimes.

From a technical standpoint, next-generation AI stacks blend vector search, policy-aware retrieval, tool execution, and data observability into a cohesive runtime—often leveraging versioned model registries and lineage-aware orchestration described by Palantir and governance suites from IBM. As documented in IEEE literature including IEEE Transactions on Computers, the shift from rules-based to learning systems with oversight requires formal verification of parameters and continuous monitoring to manage drift and bias.

Company Positions and Competitive Dynamics Platform-centric contenders: Databricks and Snowflake focus on unifying data and AI in governed environments, emphasizing native optimization for model inference and vector search, with multilayer security and lineage. Their strategies center on minimizing egress, tightening data contracts, and streamlining operator experiences, consistent with analyst commentary from Forrester.

Enterprise systems: SAP and ServiceNow embed AI into business and service workflows with guardrails for regulated industries, meeting GDPR and ISO 27001 compliance requirements per corporate documentation and governance roadmaps. Their advantage is proximity to critical processes—procurement, finance, case management—where workflow-centric agents can drive measurable outcomes, as discussed in solution briefs by SAP and ServiceNow.

Cloud ecosystems: Google Cloud and Microsoft Azure integrate managed models, vector databases, and security controls across data services, with emphasis on enterprise identity, safety filters, and observability. According to McKinsey, alignment between cloud-native data services and AI runtimes reduces operational friction and accelerates compliance audits.

Industrial data specialists: Siemens and Honeywell extend AI into manufacturing and building management systems, bridging OT/IT with domain-specific AI for predictive maintenance and quality control. As reported in industry analyses by Forrester and sector briefings, their depth in sensor data and control systems makes them natural anchors for edge-to-cloud AI patterns in factories and critical infrastructure.

These insights align with latest AI innovations highlighted across the enterprise stack, where ecosystem integration and governance-first design are increasingly decisive for vendor selection.

Company Comparison

Vendor	Primary AI Strategy	Data/Governance Focus	Integration Strength
Databricks	Lakehouse-native AI and agents	Centralized lineage and policy	Deep with data engineering and ML
Snowflake	Data cloud AI services	Secure sharing and access control	Strong cross-business data exchange
ServiceNow	Workflow-centric copilots and agents	ITSM/ESM guardrails and auditability	Native to service operations
SAP	Embedded AI in ERP suites	Enterprise controls and compliance	Process integration across finance/HR
Google Cloud	Managed models and data services	Identity, safety, observability	Cloud-native toolchain
Palantir	Governed AI operating layer	End-to-end traceability	Mission-critical data integration

Implementation: Patterns, Pitfalls, and Best Practices According to Gartner’s 2026 analysis of AI adoption patterns, enterprises are prioritizing architecture blueprints that start with data products, then layer RAG with strict access policies, and only then introduce agents for orchestration. A methodology that reduces risk begins with small-scope workflows and scales under a consistent policy and logging fabric, an approach also endorsed by McKinsey.

“AI works when it’s embedded in business process and continuously improved with human-in-the-loop feedback,” said Bill McDermott, CEO of ServiceNow, highlighting the role of process context and governance in achieving reliable outcomes. As highlighted in annual shareholder communications, CIOs increasingly expect vendor-native controls for retention, masking, and audit export, reducing compliance overhead and accelerating sign-off for production use cases.

“Data quality, lineage, and access policies form the bedrock of trustworthy AI,” said Thomas Kurian, CEO of Google Cloud, emphasizing the primacy of data engineering and security integration. According to IBM, capabilities such as model cards, bias detection, and drift monitoring complement strong data governance to create an end-to-end assurance framework.

Per January 2026 vendor disclosures, organizations that adopt prompt and model observability—tracking input tokens, latencies, output evaluation, and guardrail triggers—report clearer cost attribution and improved quality. Figures independently verified via public financial disclosures and third-party market research; market statistics cross-referenced with multiple independent analyst estimates from Gartner and Forrester.

Outlook: From Agentic Pilots to Enterprise Operating Fabric During a Q1 2026 technology assessment, researchers found that enterprises are moving toward policy-aware agentic systems operating over governed data products and unified identity, a direction reinforced by ecosystems led by Databricks and Snowflake. As documented in NIST’s guidance and enterprise security benchmarks, the winners will be those who align AI runtime decisions with compliance controls and organizational risk appetite.

As boards evaluate long-term AI investments, the build-vs-buy calculus increasingly favors platforms that consolidate data, governance, and AI runtimes, while still allowing choice of models and vector stores, a balance reflected in Google Cloud’s and Microsoft’s service catalogs. For multi-region, regulated deployments, certifications such as SOC 2 and ISO 27001—and, where applicable, FedRAMP High—remain critical, an area addressed in governance portfolios from IBM and systems providers like SAP.

Timeline: Key Developments

• January 2026: Analysts emphasize governance-first AI deployments in enterprise roadmaps, aligning with guidance from Gartner.
• January 2026: Vendor briefings highlight agentic orchestration tied to data products and policy controls, reflected in platform materials by Databricks and Snowflake.
• February 2026: Enterprise buyers scrutinize observability and FinOps features for AI lifecycles, per advisory notes by Forrester and governance practices from IBM.

Disclosure: Business 2.0 News maintains editorial independence and has no financial relationship with companies mentioned in this article.

Sources include company disclosures, regulatory filings, analyst reports, and industry briefings.

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