The Battle for the AI Orchestration Layer Heats Up

By 2025, the financial services sector stopped “testing” artificial intelligence and started living in it. The industry has moved past the novelty of generative chatbots to a more profound structural reality: the redesign of the global movement of money around autonomous decision-making.

As we look toward 2026, the industry isn’t just deploying AI; it is competing for the “orchestration layer” of the digital economy. What began as a quest for efficiency has evolved into a high-stakes contest over agency — specifically, who (or what) controls the decision, the data and the final settlement in an AI-mediated ecosystem.

Agent-Native Infrastructure Becomes Table Stakes

The most significant shift for 2026 is the transition from “AI-enabled” to agent-native infrastructure. Banks and payment networks are no longer just adding artificial intelligence to legacy stacks; they are building with the assumption that autonomous software will initiate transactions, move liquidity and resolve exceptions without a human in the loop.

As PYMNTS CEO Karen Webster wrote on the protocol layer reshaping AI-driven commerce, “This time, the shift is not about making payments invisible or shaving a few seconds off the checkout flow. It is about something much bigger: who or what makes the decision about what to buy and how to pay.” In 2026, agents are not simply accelerating transactions. They are assuming control over choice itself, redefining how value is created and captured across financial networks.

Precision Over Scale: The Move to Specialized Models

Alongside infrastructure change, the AI stack itself is evolving. The era of the general-purpose LLM is giving way to small language models (SLMs) and specialized systems. As reported by PYMNTS, financial institutions are moving toward smaller, more specialized systems designed for discrete tasks such as fraud detection, reconciliation, underwriting and compliance monitoring.

Smaller models are cheaper to run, easier to govern and more predictable in regulated environments where explainability and control matter. In production systems, breadth is giving way to precision. By 2026, specialization will become a prerequisite for scaling AI safely and economically within core financial workflows.

Discovery and the ‘Zero-Click’ Journey

As specialized agents proliferate, the traditional customer journey is being dismantled. The industry is moving from search-based menus to intent-driven discovery layers. In this world, the “wallet” as we know it begins to fade.

In her analysis of AI agents and the declining relevance of traditional wallets, Webster observed, “In a world where agents shop and pay, they will not fill out forms. Consumers will not go to a checkout page. One-Click will become Zero-Click.”

As agents act end to end on behalf of users, the competition for loyalty shifts away from the user interface. Control over discovery increasingly depends on data access, authorization frameworks and protocol-level participation rather than interface design alone that caters to consumers.

ROI Becomes the Gatekeeper for AI Expansion

By 2026, return on investment has become the central gatekeeper for artificial intelligence expansion. According to PYMNTS, CFOs are reallocating budgets away from broad AI experimentation and toward agentic systems that deliver measurable economic outcomes.

Productivity gains, faster cycle times, lower fraud losses and improved working capital performance now define success. AI initiatives that cannot demonstrate clear, repeatable impact struggle to secure continued funding. The result is a more disciplined deployment environment where autonomy advances only when it aligns with financial accountability.

Fraud and Risk Management Become Real-Time

As reported by PYMNTS, AI-powered scams are forcing banks to shift fraud prevention from post-event reviews to real-time defense. Attackers increasingly combine social engineering with stolen credentials, allowing them to pass traditional authentication checks and deceive even well-informed customers. This has pushed banks to deploy AI systems that continuously monitor behavior, device signals and transaction context to detect fraud.

Rather than applying blanket restrictions, institutions are using targeted “smart friction,” such as real-time warnings or step-up verification when risk indicators spike. The growing use of AI-generated voices and impersonation techniques has further blurred the line between legitimate and fraudulent activity, raising the stakes for faster detection, stronger identity verification and adaptive controls that operate during the transaction itself rather than after losses occur.