2026 AI Spend Outlook: Navigating from Experimentation to Enterprise Value

Key Takeaways:

• AI investment is no longer the question; ROI is.
• Strong foundations drive returns.
• Execution matters more than ambition.

Industry forecasts continue to show healthy growth in overall IT spending, driven largely by AI. In 2026, AI software spending alone is expected to grow by $270 billion, signaling that AI has moved from experimentation into core enterprise planning.

At the same time, CIOs are exercising caution elsewhere. New, non-AI initiatives are facing tighter review, and technology leaders are being asked to show faster returns and lower risk. AI budgets may still be growing, but they are no longer insulated from scrutiny.

Two shifts stand out as CIOs plan for 2026:

• AI is becoming a material portion of IT budgets, often targeted at 15–20 percent.
• Vendor strategy is changing, with many CIOs factoring in digital sovereignty and regional resilience alongside capability.

The message is clear: AI spend is expected to perform. This AI spend outlook is one part of the broader article, Top AI Trends Every CIO Must Watch in 2026, which brings together the full set of trends CIOs are tracking this year.

The ROI Reality Check

For all the investment flowing into AI, tangible returns remain harder to achieve than many anticipated. If we look at the numbers, only 6% of organizations report a strong financial impact from AI initiatives, and many have quietly scaled back efforts that failed to move beyond pilots this year.

When AI efforts fall short, it’s usually because of a lack of a clear plan, disagreement over what success looks like, and timelines that don’t align with reality. Teams that do well tend to keep things simple. AI doesn’t fail because it’s overhyped; it fails because it wasn't followed through on.

Five Investment Priorities That Matter in 2026

1. Data Foundations and Integration Modernization

AI struggles when it’s asked to sit atop messy data and aging systems. In many organizations, stalled AI efforts can be traced back to environments that were built for batch processing and manual handoffs, not real-time intelligence.

This becomes even more visible with agentic AI. Gartner expects a large share of these initiatives to fall short by 2027, largely because legacy platforms can’t support real-time execution, modern APIs, or strong identity controls. CIOs who invest early in modern data platforms and integration layers give AI the room it needs to grow.

2. Digital Sovereignty and Vendor Resilience

Digital sovereignty used to feel like something legal teams worried about once a year. That’s no longer the case. It now influences everyday technology choices, from where data lives to which vendors to trust.

Rules around data residency keep shifting, and geopolitical uncertainty hasn’t helped. As a result, many CIOs are taking a closer look at how dependent they are on specific platforms or regions. The ones getting ahead of this aren’t waiting for a regulation to land; they are quietly building flexibility into their stacks so they are not forced into last-minute changes.

3. Governance for Agentic AI

As AI systems move beyond assisting and start taking action with Agentic AI getting into the picture, governance quickly becomes non-negotiable.

Task-specific agents can speed things up, but without guardrails, they also introduce new risks around security, compliance, and decision ownership. The organizations making steady progress are the ones putting in place visibility, policies, and human oversight early, before scale magnifies risk.

4. Focusing on Proven, High-Value Use Cases

Most successful AI efforts begin with familiar problems, not bold experiments. Things like cleaning up legacy systems, reducing manual checks, or speeding up how information moves through the business may not sound exciting, but they tend to work. When AI is plugged into real processes with clear owners, it keeps moving. When it’s disconnected from day-to-day operations, it slows down or quietly fades away.

5. Preparing Teams for Human–AI Collaboration

AI adoption doesn’t fail because teams resist it. It fails when teams aren’t prepared for how work actually changes.

The future of IT work is shared, with humans and AI working together. Organizations that invest in skills, rethink workflows, and support change tend to see stronger results over time. The goal is to remove friction from their work so they can focus on higher-value decisions.

The Path Forward: Discipline Over Disruption

The initial excitement around GenAI may have disappeared, but AI itself is now part of everyday enterprise software. In 2026, success will come from treating AI like any other major investment, with clear expectations around value, risk, and accountability.

The teams that are seeing results aren’t doing anything dramatic. They are getting the basics right. Clean data. Clear ownership. Practical controls. And people who understand how AI fits into their work. AI doesn’t create discipline on its own. It simply highlights whether it exists.

At Jade Global, we work with organizations to help them move past pilots and into steady, real-world use cases. Through platform modernization, AI-ready data, and the right guardrails, we help turn AI into a value driver for their business.

Discover how Jade Global supports disciplined AI adoption at scale.