Key Takeaways:
- Agentic AI pricing will become more predictable with all-you-can-eat enterprise license agreements
- Data tools and API economics will be a major headache for enterprises in 2026
- Agentic AI will be viewed as a feature, rather than a revolution, with decision velocity being the key theme
- Physical AI will start to gain traction, particularly in the manufacturing and industrial sectors
- The AI market will bifurcate, with the bubble popping in either 2026 or 2027, and enterprises will need to be cautious with their AI plans
Introduction to Enterprise Technology Trends in 2026
The year 2026 is expected to bring significant changes in the enterprise technology landscape. With the increasing adoption of agentic AI, enterprises will need to navigate new pricing models, data tool headaches, and the blurring of lines between AI and human decision-making. In this article, we will explore the trends and predictions for enterprise technology in 2026, grouped by confidence levels.
High Confidence Trends
One of the high-confidence trends for 2026 is the adoption of agentic enterprise license agreements (AELAs). These agreements will provide enterprises with all-you-can-eat access to agentic AI, allowing them to scale their AI deployments without worrying about unpredictable costs. Salesforce’s president and chief revenue officer, Miguel Milano, has already laid out the rationale behind Salesforce’s AELA, which includes a flat fee and shared risk. This pricing model is expected to become the norm in 2026, as CIOs and CFOs push for more predictability in their AI deployments.
Data Tool Headaches
Another high-confidence trend for 2026 is the headache that data tools and API economics will cause for enterprises. The lawsuit between Celonis and SAP over data access is just the beginning, and enterprises will need to be aware of the potential risks and costs associated with data tolls and connection fees. As agentic AI is deployed and agents connect, there will be multiple skirmishes over these data tolls, and enterprises will need to negotiate with their vendors to ensure that they own their data.
Agentic AI as a Feature
Agentic AI will also be viewed as a feature, rather than a revolution, in 2026. The real deal will be decision velocity, or how quickly smaller decision trees and processes can be automated at scale. Michael Ni, an expert in the field, notes that the game is decision velocity, and enterprises will need to put the pieces in place to get there. This will involve automating smaller decisions and collapsing decision trees to achieve 5x, 10x improvements.
Physical AI
Physical AI will also start to gain traction in 2026, particularly in the manufacturing and industrial sectors. As AI agents are deployed in physical environments, they will start to deliver real value, and physical AI breakthroughs will begin to rival the early days of LLMs. This will drive optimism about robotics and edge AI applications, and enterprises will need to be aware of the potential benefits and risks of physical AI.
Medium Confidence Trends
The AI market will also bifurcate in 2026, with the bubble popping in either 2026 or 2027. This will be driven by concerns about AI infrastructure capital expenses and debt, and enterprises will need to be cautious with their AI plans. The build vs. buy debate will also continue, with build looking like a great option as customers push back on SaaS deal inflation. AI benefits will broaden out to more levels of the enterprise stack, and software vendors will show revenue and productivity gains from AI.
Not Absurd but Unlikely Trends
Some trends that are not absurd but unlikely for 2026 include Nvidia’s shares closing the year flat to down, AI infrastructure being overbuilt, and OpenAI realizing it can’t continually raise money forever. Quantum use cases may also go mainstream in the enterprise, and Meta may revamp its AI operations again. High memory costs may create a buyers strike for PCs, servers, and smartphones, and 2026 may be one of the biggest years ever for IPOs.
Scorecard from 2025
Finally, it’s worth looking back at the predictions for 2025 to see what worked out and what didn’t. Some predictions that were on target include the volatility of 2025, AI productivity gains broadening in enterprises, and OpenAI and Microsoft becoming frenemies. Other predictions that were off base include the alleged run on vendor consolidation, agentic AI ushering in autonomous processes, and the AI data center buildout stalling. As we look to 2026, it’s clear that the enterprise technology landscape will continue to evolve, and enterprises will need to be aware of the trends and predictions that will shape the industry.