Systems, Not Models, Will Define AI Leadership in 2026

In 2026, competition in AI will shift from building the best models to creating the best systems. According to Gabe Goodhart, Chief Architect of AI Open Innovation at IBM, “We’re going to hit a bit of a commodity point. It’s a buyer’s market. You can pick the model that fits your use case just right and be off to the races. The model itself is not going to be the main differentiator.”

From Models to Orchestration

What matters now is orchestration: combining models, tools, and workflows into cohesive systems. “If you go to ChatGPT, you are not talking to an AI model,” Goodhart explains. “You are talking to a software system that includes tools for searching the web, doing all sorts of different individual scripted programmatic tasks, and most likely an agentic loop.”

This shift represents a fundamental change in how companies should approach AI. Rather than investing in building proprietary models, organizations should focus on integrating models into effective systems that solve real business problems.

The model itself is not going to be the main differentiator. What matters is orchestration: combining models, tools, and workflows.

— Gabe Goodhart, Chief Architect, AI Open Innovation, IBM

Cooperative Model Routing

In 2026, we’ll see more cooperative model routing systems. “You’ll have smaller models that can do lots of things and delegate to bigger models when needed,” Goodhart predicts. This approach offers several advantages:

• Cost efficiency: Small models handle routine tasks, reserving expensive large models for complex queries
• Speed: Faster responses for common use cases that don’t require full model capabilities
• Flexibility: Easy to swap models in or out as better options become available
• Reliability: If one model fails, the system can route to alternatives

The Winner Takes the System Level

“Whoever nails that system-level integration will shape the market,” Goodhart says. This means the companies that dominate AI won’t be those with the best individual models, but those with the most sophisticated systems for orchestrating and integrating multiple models, tools, and workflows.

This has profound implications for AI strategy:

• Platform thinking: Companies need to think like platform builders, not just model developers
• Integration capabilities: The ability to connect AI systems with existing business infrastructure becomes critical
• User experience: The interface between humans and AI systems becomes a key differentiator
• Tooling: Building and maintaining the tools that AI agents use becomes more important than the models themselves

Practical Implications

For businesses, this shift means rethinking AI investment priorities:

• Less focus on training: Training custom models becomes less important for most organizations
• More focus on integration: Integrating existing models into business workflows becomes the priority
• Tool development: Building the tools and connectors that make AI useful becomes crucial
• System architecture: Designing robust AI systems that can route between models becomes a core competency

The New Competitive Landscape

In 2026, the companies that win will be those that build the best systems, not necessarily the best models. This democratizes AI in some ways—any company can access similar models—but raises the bar for system-level innovation.

The era of model competition is giving way to the era of system competition. And the companies that understand this shift and invest accordingly will be the ones leading the next phase of AI evolution.