Boost’s Aiala Links AI Performance to Strong Governance Frameworks

Companies must establish AI oversight with clear metrics before implementing the technology, Boost Payment Solutions Chief Compliance Officer Elly Aiala writes in a new PYMNTS eBook, “AI Runs Payments. Governance Decides What Happens Next.”

AI governance doesn’t typically fail because companies lack policies. It fails in the gap between intent and behavior.

One of the most common breakdowns is simple: employees using AI tools to move faster and unintentionally exposing sensitive or proprietary information. It’s not malicious; it’s convenience. People want quick answers, and AI delivers. But without guardrails, that convenience can introduce profound risk.

Some organizations are investing in private LLM environments or segmented data layers. That helps, but it introduces a different question: is the ROI there? Building or buying controlled environments is expensive. Choosing not to invest, however, means accepting the risk that employees will use external tools without oversight. Governance becomes a business decision, not just a security one.

That tension shows up in the age-old trade-off between speed and control. There is significant pressure to adopt AI quickly, especially in payments. That urgency can lead to rushed decisions — moving forward in partnerships and new processes without appropriate visibility. The more loosely artificial intelligence tools are adopted, the harder it becomes to track usage, enforce compliance and maintain consistent security standards.

In a highly regulated environment with sensitive data, that trade-off is real. You can move faster, but you then increase exposure across data management, third-party dependencies and regulatory requirements. Most organizations do not yet have full visibility into how AI is being used across their teams, which makes controlled acceleration difficult.

The challenge becomes more complex when AI depends on third parties. Governing AI in that environment requires due diligence that goes beyond traditional vendor onboarding or third-party reviews. If you cannot clearly explain to a colleague, customer, partner or auditor what your AI is doing, how it is doing it and why it is being used, you are not ready to deploy it.

One governance decision many organizations may wish they made earlier is defining success metrics before implementation. Without clear metrics, governance is reactive and much more complex than if you had tackled it prior to build/integration. These are the questions boards and CEOs should be asking of their teams implementing AI. Instead of asking the general question of whether AI is being used, leadership should be asking: Who owns governance? What metrics support that governance? Do we have the internal expertise to appropriately implement and monitor our AI usage? At Boost, we take the same approach. Governance is treated as a prerequisite, not an afterthought, with a focus on maintaining visibility and accountability across how AI is used.

Finally, cost remains underexamined. Many organizations are investing in artificial intelligence without a clearly defined business case, chasing after the latest “hype.” When you factor in due diligence, hiring, implementation, infrastructure and ongoing maintenance, the cost can be significant. If AI is not tied to specific, high-value use cases, the risk is not just operational, it is financial.

AI has the potential to transform payments, but without disciplined governance, it can scale risk just as quickly. Regardless of where you are on your AI journey, take inventory now. Poll your team on what and how AI is being used today. You may be surprised.