The next AI phase is better agents not bigger models

The upcoming phase of artificial intelligence will focus on sophisticated agents rather than merely larger models. While AI technology continues to advance rapidly, many businesses are still deliberating the best approach to adopt. At the 2026 Gartner Data and Analytics Summit, analysts emphasized that standalone AI models are becoming obsolete, and autonomous interconnected AI agents will become the future.

For organizations seeking an edge in the AI race, the emphasis should not be on choosing the most powerful model, but rather on effectively deploying agentic AI to solve real business problems. Instead of viewing AI as merely another chatbot, it should be considered an operational capability that can augment employees, automate mundane tasks, and enhance overall efficiency across the organization.

The potential of agentic AI extends far beyond conversational applications. In higher education, AI agents are aiding students in navigating support services by answering routine inquiries about enrollment, campus facilities, or administrative procedures, thereby freeing up staff to handle more complex issues. Similarly, in the transport industry, AI agents can consolidate timetable information, onward travel options, and customer support within a single interaction, saving users from the need to search across multiple platforms.

Furthermore, businesses are leveraging AI agents to qualify sales inquiries, summarize intricate documentation, and extract essential information from contracts - tasks that previously required hours of manual labor.

The foundation of effective agentic AI lies in identifying and addressing operational pain points. Rather than rushing to implement AI technologies, organizations should first pinpoint areas where employees spend excessive time on repetitive administrative tasks, information retrieval, and manual data transfer. By focusing on these challenges, agentic AI can serve as a valuable operational tool, rather than a solution seeking a problem.

As AI models continue to evolve at an unprecedented pace, the competitive advantage is shifting away from the choice of the foundation model itself. Instead, success will increasingly hinge on factors such as data quality, prompt engineering, and governance. High-quality data is crucial, as an AI agent's output is only as good as the information it is based on.

Similarly, well-designed prompts establish clear guidelines for the agent's behavior, ensuring consistent and focused results. Governance plays a critical role in managing the agent's capabilities, preventing it from answering unrelated questions and maintaining security by adhering to the principle of least privilege.

Unlike large-scale transformation initiatives, successful agentic AI deployments begin with focused use cases that deliver tangible value. This approach not only builds employee trust and demonstrates return on investment but also allows for the refinement of governance measures before scaling to more complex workflows. Treat agentic AI as an evolving product, rather than a finished project.

Testing, monitoring, and continuous refinement are essential elements of a successful implementation, as AI agents are inherently probabilistic in nature. Finally, involving employees throughout the development process is vital for building trust in the technology and ensuring its long-term success.