The enterprise AI race is moving away from simply choosing the largest or most capable foundation model. As increasingly powerful models become widely available, the differentiator for businesses will be how effectively they turn those models into agentic systems that can execute real, multi-step workflows. The author points to the 2026 Gartner Data & Analytics Summit, where the emphasis was on autonomous, interconnected agents rather than standalone AI models.
The article recommends starting with high-frequency, low-complexity tasks where agents can deliver measurable value without introducing excessive risk. Rather than immediately giving an agent broad autonomy, organizations should define clear objectives, connect agents to the right enterprise data and tools, and establish guardrails around what they are allowed to do. This makes agent deployment an operational challenge as much as a technical one.
A key point is that better agents are not necessarily built from better models alone. An effective agent needs access to relevant business context, reliable tools, clearly defined workflows, memory, permissions and mechanisms for checking its own work. The surrounding organizational infrastructure therefore becomes increasingly important. This aligns with the article's argument that companies should focus on building systems capable of solving specific business problems rather than continually switching to the newest model.
The broader shift is from “Which AI model should we buy?” to “Which work should AI agents perform, and under what conditions?” As agents become more capable of planning, using tools and executing tasks, organizations will need to invest in governance, workforce training and workflow redesign alongside model access. The competitive advantage may ultimately come not from possessing the biggest model, but from building the most reliable agent around it.
