Agentic AI success depends less on selecting models and more on building strong organizational foundations
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For organizations looking to gain an edge by adopting AI tools, the decision has been dominated between large language models.
Every new release promises better reasoning, greater accuracy and more capabilities than the last.
But as foundation models become increasingly powerful and widely available, the real differentiator for organizations won't be which model they choose – it will be how effectively they deploy agentic AI to solve real business problems.
At the recent 2026 Gartner Data and Analytics Summit, the message from analysts was clear; standalone AI models are outdated and autonomous, interconnected AI agents are the future. But to get real value from agentic AI, IT leaders should prioritize “high-frequency, low-complexity use cases, apply guardrails and upskill the workforce.”
To this statement, I would add that the next phase of enterprise AI isn't about having access to the biggest model. It's about building better agents.
That means moving beyond seeing AI as simply another chatbot and instead treating it as an operational capability that can augment employees, automate repetitive work and unlock efficiencies throughout the organization.
For many people, AI still means asking ChatGPT a question and receiving an answer. That's certainly one application, and conversational interfaces have played an important role in making AI accessible. But that’s only scratching the surface of what's possible.
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Some of the most valuable agentic AI deployments are focused on operational work rather than conversation. For example, in higher education, AI agents are helping students navigate support services by answering routine questions about enrolment, campus facilities or administrative processes, freeing staff to deal with more complex issues.
In transport, agents can combine timetable information, onward travel options and customer support into a single interaction, saving users from having to search across multiple services.
Elsewhere, organizations are using AI agents to qualify sales enquiries, summarize complex documentation, and extract information from contracts. Tasks that previously required hours of manual effort.
The common thread isn't simply that these agents can answer questions. It's that they remove administrative burden and enable people to focus on work that requires judgement, expertise and human interaction.
One of the biggest misconceptions surrounding agentic AI is that organizations should begin by deciding where they want an AI agent.
