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11/07/2026 1:54 pm
TL;DR: Top-down AI mandates can drive pilots and demos without real process change, while merchants remain cautious about conversational AI agents handling high-stakes customer interactions, according to Riskified’s coverage of Bret Taylor’s remarks at the RILA CEO Forum 2026. The real test is not prompt quality but workflow design, guardrails, and identity-aware decisioning around who is making a request.
NHIMG editorial — based on content published by Riskified: AI tourism, merchant adoption, and the guardrails needed for customer-facing AI
By the numbers:
- When 100 to 200 basis points of fraud can hide in refund claims alone, merchants must treat refund workflows as a control surface, not just a service process.
Questions worth separating out
Q: How should security teams govern AI systems that handle customer service actions?
A: Start by separating informational interactions from transactional ones.
Q: Why do customer-facing AI agents create fraud risk in refund workflows?
A: Because refund workflows combine natural-language interaction with value transfer.
Q: What do organisations get wrong about AI automation in merchant operations?
A: They often treat AI as a prompt layer instead of a process layer.
Practitioner guidance
- Classify customer intents by risk tier Separate informational requests, low-risk service actions, and high-impact account or refund changes before enabling automation.
- Bind AI actions to identity and fraud signals Use account age, device context, prior dispute history, and transaction anomalies as part of the authorisation decision.
- Limit delegated privilege for customer-facing agents Define the exact actions an AI system can perform and revoke any permission that exceeds its current business function.
What's in the full article
Riskified's full analysis covers the operational detail this post intentionally leaves for the source:
- McKinsey-linked business impact estimates for AI-powered next best experience in customer operations
- The identity clustering and identity-based decisioning angle behind refund claims abuse
- The full merchant context around why CEOs are reluctant to loosen guardrails for conversational AI agents
- The difference between low-stakes and high-stakes customer interactions in live merchant workflows
👉 Read Riskified's analysis of AI tourism, merchant AI adoption, and refund risk →
AI tourism in merchant teams: what does it mean for CX automation?
Explore further
11/07/2026 2:05 pm
AI adoption is stalled less by model capability than by governance debt. The article’s core point is that better models and lower token costs do not solve the organisational problem of unsafe process design. Merchant teams are discovering that without workflow redesign, AI remains a pilot artefact rather than a control-bearing system. The governance lesson is that adoption stalls when automation is bolted onto human processes instead of embedded into them.
A question worth separating out:
Q: Who should approve high-stakes actions taken by customer-facing AI systems?
A: High-stakes actions should either require human approval or a tightly constrained policy path with strong verification. If the AI can refund, override, or change account details on its own, the organisation has effectively given it transactional privilege. Accountability should remain with the business owner of the workflow.
👉 Read our full editorial: AI tourism is slowing merchant adoption, but workflow AI still matters
