After two years of flashy AI demos, rushed agent prototypes, and optimistic predictions, by 2026, enterprise technology leaders are shifting to a more pragmatic approach. In a recent webinar hosted by OutSystems, software leaders and business experts argued that the most impactful AI applications today lie not in impressive demos, but in fundamental elements such as governance, coordination, and integration of AI into existing systems.
Businesses are shifting their focus back to core objectives: using AI to increase productivity, improve deployment speed, and deliver measurable business results.
Three key trends are shaping how businesses deploy AI: the shift from agent prototypes to actual agent systems with clear ROI, the growing role of enterprise platforms in governing and scaling AI, and the rise of general programmers and enterprise architects as the most valuable assets in the AI coding era.
When AI agents enter the real-world environment.
For AI agents to function effectively in enterprise environments, a unified platform is needed to support development, testing, and deployment. According to Rajkiran Vajreshwari from Thermo Fisher Scientific, tools like OutSystems' Agent Workbench help businesses build and manage agent systems at scale.
Thermo Fisher's team has moved from a single-tasking AI assistant to building a system of multiple specialized agents working together. When a customer requests support, an initial agent categorizes the request, then forwards it to specialized agents such as a prioritization agent, a product information agent, a fault handling agent, or a compliance agent.
Each agent is designed with a specific role and clear boundaries, making the system more accurate and easier to control. This approach shows that businesses are shifting from standalone AI to truly collaborative agent systems.
A new risk emerges when AI allows employees to create code or applications without IT oversight. This is known as 'shadow AI' — spontaneous solutions that can cause numerous problems such as data leaks, policy violations, or model errors.
According to Luis Blando from OutSystems, businesses need to build "guardrails" to control the use of AI. He argues that employees will use AI whether the company allows it or not, so the best approach is to use AI to manage AI.
Eric Kavanagh from The Bloor Group also argues that AI governance requires multiple layers of control, from data security and model monitoring to controlling the integration of AI into existing business processes. Many enterprise platforms already have these mechanisms built in to mitigate risk.
The big challenge: Coordinating AI, not choosing a model.
Previously, businesses focused on selecting the best AI model. But now, the bigger challenge is coordinating multiple AI models and systems together.
Scott Finkle from McConkey Auction Group argues that large language models are only part of a complex process, not a complete solution. Businesses need to build systems that can flexibly switch between models like Gemini, ChatGPT, or Claude without having to rebuild the entire system.
Coordination platforms help manage the AI lifecycle, ensuring stable operational processes and maintaining long-term value, even as AI technology continuously evolves. As AI begins to integrate into critical processes such as finance and supply chain, businesses will need to invest more in security, compliance, and AI management platforms.
However, experts recommend that businesses should not expect immediate leaps forward. Instead, they should focus on small but practical improvements that can be implemented right away.
Some businesses are choosing to redesign entire processes using AI, while many others want to integrate AI into existing systems to leverage existing infrastructure and data. An agent-based approach allows both strategies to work effectively.
Enterprise AI is entering a mature phase.
As AI speeds up the coding process, bottlenecks in software development are gradually disappearing. Instead, systems thinking skills are becoming more important than ever.
Enterprise architects and general-purpose programmers are becoming the most valuable roles. These individuals possess the ability to understand the overall system, analyze business problems, and integrate AI into existing infrastructure.
According to experts, this is the age of the 'generalist'—those who understand both technology and business. AI helps them focus on complex tasks instead of repetitive work, thereby accelerating deployment and reducing errors in the system.
After the initial testing and hype phase, enterprise AI is moving into a more mature stage. Instead of focusing on technology, businesses are concentrating on real-world value, scalability, and long-term integration.
In this context, successful organizations will be those that know how to build sustainable AI systems, manage them well, and leverage existing data to create real business value.