Software budget growth outpaced peers in 2026, with AI and agentic automation becoming core areas of incremental CIO spending

Overview

This report compiles the key takeaways from Morgan Stanley's 1Q26 CIO conference call held on April 9, 2026, and integrates 1Q26 CIO survey data to discuss 2026 IT budgets, software spend, AI/ML priorities, buy-versus-build decisions, and signs that agentic workflows are moving from pilots to production. The overall conclusion is that the budget environment is only moderately improving, but incremental spending is increasingly concentrated in software and AI-related layers.

Core views

Key views include: first, expected 2026 IT budget growth is expected to rise slightly from 3.6% in 2025 to 3.7%, but overall still remains cautious; second, software is expected to become the fastest-growing major IT category at 4.1% year-over-year and the only major category expected to accelerate; third, AI/ML continues to be the CIO's most important incremental initiative; fourth, enterprises still prefer buying mature vendor capabilities, but when platform openness, data access, or pricing pressure is a constraint, they may consider building capabilities around orchestration and data layers in-house; fifth, early value from agentic automation is more likely to come from narrow use cases such as ticketing, workforce scheduling, content generation, and report automation rather than immediately rewriting the entire enterprise software stack.

Analysis framework

The report uses a combined approach of CIO survey data and three CIO conference call transcripts: it quantifies budget growth, priority ranking, and vendor preferences through the survey, then uses CIO commentary to explain practical constraints on enterprise AI deployment, software procurement, build-versus-buy willingness, data platforms, and workflow orchestration.

Methodology notes

• survey_and_expert_call1Q26 CIO Survey and CIO Conference Call

  Survey and call cross-validation

  Quantitative results from the CIO survey and execution feedback from conference calls are used to cross-validate each other, helping assess 2026 IT budget trends, software spending, and AI rollout direction.

• technology_spendingBuy vs Build

  Buy-versus-build decision

  Enterprises still lean toward buying mature vendor products for AI and software capability building, but may build in-house at the orchestration layer, data layer, or specific processes when platform openness, cross-system data access, or pricing becomes a constraint.

• ai_adoptionAgentic Workflow Automation

  Agentic workflow automation

  AI agents are shifting from general productivity tools to execution of specific processes, such as ticket routing, workforce scheduling, content generation, report automation, and cross-platform micro-agent coordination.

Asset mapping & comparison

Structured mapping from thesis to named assets (strengths, weaknesses, peers, risks).

• Microsoft (MSFT.O)

  Principal beneficiary of AI and software spending

  Strengths

  It shows leadership in GenAI incremental spending, agentic automation, custom AI applications, hybrid cloud management, Azure IaaS/PaaS, and Copilot deployment.

  Weaknesses

  Copilot still faces ROI quantification and cost recovery challenges on a per-user, per-month basis.

  Comparison

  Compared with other vendors, Microsoft has leading coverage across infrastructure, productivity, developer tools, workflow, and enterprise distribution layers.

  Risks

  If enterprises cannot demonstrate the efficiency benefits of AI subscriptions, the pace of deployment expansion may slow.

• ServiceNow Inc (NOW.US)

  Beneficiary in application layer and ITSM agentic automation

  Strengths

  Listed as a potential agentic automation vendor by CIOs, with a strong entry advantage in ITSM workflows and enterprise process orchestration.

  Weaknesses

  If platform openness is limited or pricing pressure is high, customers may build substitute capabilities at the orchestration or data layer.

  Comparison

  Compared with cloud infrastructure vendors, ServiceNow is closer to workflow and ticket execution scenarios.

  Risks

  Cross-platform interoperability, customer self-build, and pricing constraints may limit value capture.

• Salesforce (CRM.N)

  Potential beneficiary in application software and GenAI spending

  Strengths

  The report suggests Salesforce could benefit from GenAI spending, custom AI application deployment, and the spread of application-layer AI budgets.

  Weaknesses

  Some CIOs believe enterprises will not immediately rebuild or fully replace Salesforce, but may gradually use agents to substitute portions of functionality.

  Comparison

  Similar to ServiceNow, Salesforce is positioned in enterprise applications and process layers, but must show AI capabilities that retain business logic rather than only serve as data repositories.

  Risks

  If agents progressively abstract away application logic, traditional application platforms could be reduced to data-storage roles.

• Snowflake Inc (SNOW.N)

  Enterprise analytics and AI data access platform

  Strengths

  CIO feedback indicates Snowflake is easy to adopt for business-team analytics, ETL transformation, and low-skill-barrier use.

  Weaknesses

  Respondent CIOs have not seen significant direct changes to Snowflake from AI itself; more AI activity appears to occur in adjacent ecosystem tooling.

  Comparison

  Compared with Databricks, Snowflake is more weighted toward business analytics and data consumption use cases, while Databricks is more oriented to processing platforms and systems-of-record.

  Risks

  If AI value is mostly captured by adjacent tools or new vector databases, direct incremental impact on the platform may be limited.

• Databricks

  Data processing, systems-of-record, and AI infrastructure-related platform

  Strengths

  CIOs view it as a processing platform serving systems-of-record, ERP offloading, and AI-related data infrastructure.

  Weaknesses

  The report does not provide direct evidence of increased AI revenue or budget share for Databricks.

  Comparison

  Compared with Snowflake's business analytics positioning, Databricks is more oriented toward data processing and underlying engineering capabilities.

  Risks

  Enterprise AI data architectures are still evolving, and new data platforms and vector databases may compete for spend.

• GitLab (GTLB.O)

  Agentic automation and development toolchain participant

  Strengths

  It is listed as an early-choice option in agentic automation vendor preferences, reflecting opportunities in AI-enabled development toolchains.

  Weaknesses

  Survey share is below Microsoft, Google, Amazon, and ServiceNow.

  Comparison

  Compared with integrated platform vendors, GitLab is more focused on development workflows and code-related agents.

  Risks

  Competition in AI developer tools is intense, and share may be squeezed by larger cloud and platform vendors.

Key data

• 2026 IT budget growth expectation+3.7%Above the 2025 expectation of +3.6%, but below the 10-year long-term average of +4.1%.
• 2026 software spending growth expectation+4.1%Software is viewed as the fastest-growing IT segment and the only major IT category expected to accelerate.
• AI/ML priority share17.7%AI/ML remains the top CIO priority, above 16.3% in 4Q25.
• Preferred enterprise AI vendor typeHyperscalers 22%; application vendors 21%; AI application development vendors 13%; data management vendors 11%This indicates that AI budgets are spreading from infrastructure toward application, development, and data management layers.
• Preferred agentic automation vendorsMicrosoft 42%; Google 10%; Amazon 6%; ServiceNow 5%; GitLab 4%; UiPath 4%Microsoft is clearly the dominant vendor preference in agentic automation.
• 2026 GenAI incremental spend leadersMicrosoft 32%Microsoft is identified as the largest beneficiary of GenAI incremental spend share in both one-year and three-year views.
• Microsoft custom AI application preferenceCurrent 48%; three-year outlook 32%The survey indicates Microsoft is in a leading position for enterprise custom AI application build-outs.
• Microsoft hybrid cloud management preference46%Azure remains the leading IaaS and PaaS choice in both current and three-year perspectives.
• Copilot pricing discussionapproximately $30 to $35 per user per monthCIOs noted that ROI and payback mechanics are still being evaluated as Copilot expands.

Impact & implications

For the software industry, the modest budget improvement does not imply a full recovery; the real opportunity is concentrated in AI, application software, workflow automation, data management, and cross-system orchestration capabilities. Large platform vendors, especially Microsoft, benefit from strengths in productivity tools, cloud, developer tools, and enterprise distribution advantages. Vendors such as ServiceNow, Salesforce, GitLab, Databricks, and Snowflake may also gain incremental opportunities in application layers, ITSM, data, and the AI development chain. For investors, it is important to distinguish between generic AI narratives and vendors that can actually embed into enterprise workflows, control data entry points, and improve ROI.

Risks

• Overall IT budget growth remains below the long-term average, indicating a cautious spending backdrop rather than broadly robust expansion.
• AI project ROI remains difficult to quantify, especially for per-user billed products like Copilot, which need to prove that time savings convert to economic value.
• Insufficient platform interoperability may limit cross-system execution by agents.
• If software vendors cannot expose data and process interfaces, customers may build at the orchestration or data layer.
• Enterprises may advance AI only in narrow workflows rather than undertaking enterprise-wide core system rewrites, meaning revenue conversion may be slower than market expectations in the near term.
• Pricing pressure may prompt customers to seek alternatives or delay upgrades.

What to watch

• Whether 2026 CIO budgets continue to move from cautious improvement toward broader IT spending recovery.
• Whether software spending growth continues to outpace hardware, communications, and services.
• Whether AI/ML priority continues to rise and budget shifts from pilots into production deployments.
• The pace of Microsoft Copilot expansion and ROI evidence in sales, marketing, accounting, HR, and legal contexts.
• The openness and agentic orchestration capabilities of workflow platforms such as ServiceNow, Salesforce, Atlassian, Moveworks, and Helix.
• How the roles of Snowflake, Databricks, vector databases, and data ingestion platforms evolve in AI-agent data access.
• Changes in the split between purchasing mature AI capabilities and building orchestration layers in-house.