What Enterprises Want From AI in 2026, According to SAP and Gartner

LONDON — February 19, 2026 — Enterprise AI priorities are crystallizing around trust, measurable outcomes, and seamless integration with data and workflows as platform vendors expand capabilities and clients move deployments into production across regulated industries.

Executive Summary

• Enterprises emphasize governance, data integration, and business-aligned use cases, supported by platforms from SAP, ServiceNow, and Snowflake.
• AI agents, retrieval-augmented generation (RAG), and secure fine-tuning drive ROI when paired with robust data pipelines from providers like Databricks and Palantir.
• Boards and CIOs prioritize risk management, compliance, and transparent model operations, informed by analyses from Gartner and Forrester.
• Edge, industrial, and sector-specific AI gain traction via solutions from Siemens, Honeywell, and ABB for operational efficiency and safety.

Key Takeaways

• Trust, governance, and auditability are now baseline requirements for enterprise AI adoption, per Gartner.
• Integration with existing data platforms and business systems determines time-to-value, as noted by SAP and ServiceNow.
• AI agents are moving beyond pilots where retrieval, orchestration, and guardrails align with policy frameworks, according to Forrester.
• Industrial and edge deployments emphasize deterministic safety and lifecycle maintenance, guided by firms like Siemens and Honeywell.

Lead: What Is Happening and Why It Matters Reported from London — In a January 2026 industry briefing, analysts noted enterprises are transitioning from experimentation to programmatic rollouts of AI agents and copilots, focusing on governance, data readiness, and measurable outcomes that align with board-level priorities (Gartner). Per January 2026 vendor disclosures, platform providers have emphasized secure model operations, retrieval-augmented generation, and policy-driven orchestration, with clients seeking to integrate AI into ERP, HR, and IT workflows via ecosystems from SAP, Workday, and ServiceNow. According to demonstrations at recent technology conferences and hands-on evaluations by enterprise technology teams, organizations prioritize initiative-level guardrails and audit trails to satisfy compliance teams and regulators, with deployment strategies anchored in existing data platforms from Snowflake and Databricks. Based on survey commentary collated by analyst firms in Q1 2026, CIOs increasingly request architectural patterns that combine RAG, fine-tuning, and policy control to mitigate hallucination risk and ensure traceable decisions (Forrester; Gartner). Key Market Trends for AI in 2026

Trend	Enterprise Priority	Implementation Approach	Source
Trust & Governance	Model transparency, auditability	Policy frameworks, monitoring, lineage	Gartner
AI Agents & Copilots	Task automation, decision support	RAG + orchestration with guardrails	Forrester
Data Integration	Time-to-value	Unified data clouds, semantic layers	Snowflake, Databricks
Industrial & Edge AI	Reliability and safety	Deterministic control, lifecycle mgmt	Siemens, Honeywell
Regulatory Readiness	Compliance alignment	GDPR/SOC 2/ISO 27001 controls	ISO 27001, GDPR

Context: Market Structure and Competitive Landscape As documented in IDC and Gartner’s early-2026 technology briefings, market power is consolidating around platforms that combine data engineering, model operations, and workflow integration, with ecosystems from SAP, ServiceNow, and Workday competing to serve core business processes (Gartner). According to corporate regulatory disclosures and compliance documentation, enterprise buyers also weigh certifications and controls, prioritizing providers that meet GDPR, SOC 2, and ISO 27001 requirements across regions (GDPR; ISO 27001), including deployments referenced by Palantir in public sector domains. Per January 2026 vendor briefings, data-centric AI strategies increasingly emphasize retrieval pipelines, governance overlays, and semantic modeling; providers such as Snowflake and Databricks frame AI as an application layer atop the modern data stack. As highlighted in annual shareholder communications and management commentary in investor presentations, industrial players like Siemens and ABB position AI in maintenance, quality, and safety workflows, with deterministic controls and lifecycle management to meet industrial compliance ( Siemens; ABB).

Analysis: Technology Approaches and Implementation Patterns

According to Gartner's early-2026 insight series, enterprise AI architectures are converging on a layered pattern: content moderation and guardrails; retrieval with curated knowledge bases; model orchestration across providers; and workflow embedding inside ERP, HR, ITSM, and CRM systems maintained by SAP and ServiceNow. Based on analysis of over 500 enterprise deployments across multiple industry verticals compiled in Q1 2026 analyst briefings, successful teams modularize components to avoid lock-in, manage risk, and align outputs with governance policies ( Forrester). As documented in peer-reviewed research published by ACM Computing Surveys and findings in IEEE Transactions on Cloud Computing (2026), robust retrieval and policy enforcement reduce hallucination and improve traceability when combined with human-in-the-loop review processes, an approach increasingly referenced by providers including Palantir and Databricks. Meeting GDPR, SOC 2, and ISO 27001 requirements is now table stakes for enterprise deals, with vendors outlining controls such as encryption, identity, data residency, and audit capabilities—frameworks detailed in trust centers from Snowflake and Workday. "Enterprises want AI that is reliable, governable, and directly tied to business outcomes," said Christian Klein, CEO of SAP, in January 2026 corporate communications, emphasizing platform-level alignment with ERP and process automation to deliver measurable value (SAP Newsroom). "The infrastructure requirements for enterprise-grade AI are forcing a rethink of data architecture and security controls," noted Avivah Litan, Distinguished VP Analyst at Gartner, in a January 2026 research briefing focused on operational resilience and governance guardrails ( Gartner). These insights align with broader AI trends observed across CIO communities and industry associations that converge on risk-aware deployments and multi-model orchestration. Per live product demonstrations reviewed by industry analysts in early 2026, enterprises increasingly test agentic workflows in limited domains—IT helpdesk, procurement triage, and financial reconciliation—utilizing integration hooks from ServiceNow, SAP, and data platforms like Snowflake. Company Positions: Platforms, Capabilities, and Differentiators SAP underscores process-centric AI integrated with ERP, finance, and supply chain, focusing on industry-specific workflows and governance—per the company’s official communications in January 2026 (SAP Newsroom). ServiceNow positions AI for ITSM, employee service, and operations, highlighting agent orchestration and guardrails tied to enterprise policies; during recent investor briefings, company executives noted client demand for embedded AI in workflows rather than standalone tools ( ServiceNow News). Snowflake and Databricks emphasize unified data foundations for AI, with retrieval, governance, and MLOps capabilities documented in platform update materials published in January–February 2026 (provider blogs and trust centers). Palantir focuses on mission-critical decision environments, highlighting provenance, lineage, and auditability in public sector and industrial contexts—per company documentation and government regulatory assessments referencing its platform architecture. Industrial AI leaders like Siemens, Honeywell, and ABB differentiate through safety certifications, deterministic control patterns, and lifecycle support in plants and critical infrastructure. "Operational AI must be safe, maintainable, and auditable over decades," said a Siemens Digital Industries executive in February 2026 guidance, reflecting customer requirements in manufacturing and energy sectors (Siemens Press Office). These positions are echoed by analyst commentary framing industrial deployments as a distinct category where governance and environmental constraints dominate design ( Forrester; Gartner). Company Comparison

Company	Core Focus	Integration Strength	Governance Signals
SAP	ERP-centric AI	Deep process integration	Policy, audit aligned (Trust Center)
ServiceNow	ITSM/employee service AI	Workflow-native agents	Controls and guardrails (Trust)
Snowflake	Data cloud for AI	Unified data pipelines	Security/compliance (Trust)
Databricks	Lakehouse + MLOps	Open ecosystem	Governance frameworks (Newsroom)
Palantir	Mission-critical decisions	Operational integration	Provenance/lineage (Platforms)

Governance, Risk, and Regulation As documented in government regulatory assessments and commission guidance updated in early 2026, enterprises are aligning AI operations with regional requirements including GDPR, SOC 2, ISO 27001, and sector-specific controls—frameworks reflected in trust documentation from Workday and Snowflake. "Risk-managed AI requires policy-first architecture," said a Fortune 500 CIO interviewed in a January 2026 analyst survey, highlighting the need for standardized review, approval, and monitoring workflows ( Gartner). Figures independently verified via public financial disclosures and third-party market research indicate rising investments in governance tooling across platform ecosystems, with market statistics cross-referenced to multiple independent analyst estimates ( Forrester). To reduce operational risk, enterprises pair retrieval with curated domain knowledge, enforce model input/output controls, and maintain audit trails—approaches described in peer-reviewed work in IEEE Transactions on Cloud Computing (2026) and echoed in platform guidance from SAP and ServiceNow. Certification mentions, including GDPR, SOC 2, and ISO 27001, are increasingly found in RFPs and procurement policies, with vendor attestations reviewed by risk committees for cross-border deployments—practice notes published by Gartner and Forrester in Q1 2026. Outlook: What to Watch During a Q1 2026 technology assessment, researchers found enterprises are moving toward standardized AI reference architectures: modular orchestration layers, retrieval services, model registries, and policy enforcement integrated into existing data and workflow systems—patterns reinforced by providers including Databricks, Snowflake, and ServiceNow. "We see customers advancing from pilots to scaled programs where AI is embedded into daily work," said Bill McDermott, CEO of ServiceNow, in January 2026 management commentary focusing on workflow-native agents and outcomes ( ServiceNow News). These insights align with AI coverage showing the market favoring providers that meet enterprise security baselines, integrate with core systems, and enable multi-model flexibility. The next phase will focus on operational reliability and traceable ROI metrics, with boards asking for program-level measures tied to risk frameworks and business KPIs—assessments presented by Gartner and Forrester. Timeline: Key Developments

• January 2026 — Analyst briefings emphasize AI governance and programmatic rollouts (Gartner; Forrester).
• January 2026 — Platform vendors outline trust, policy enforcement, and integration roadmaps (SAP; ServiceNow).
• February 2026 — Industrial AI guidance highlights deterministic controls and lifecycle maintenance (Siemens Press Office; Honeywell).

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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