AI adoption in payments is widespread but accompanied by deep concerns
We’re looking at near-full adoption of artificial intelligence in payment operations, 99% of organizations are using AI in some way. That’s massive. It shows the financial services sector isn’t just toying with automation. It’s moving ahead fast. But here’s the catch: 91% of executives who’ve made those moves have real concerns about what AI brings with it.
The core issues are risk-related. Hallucinations, where AI confidently produces false or misleading information, are a known problem and can’t be ignored. There’s also synthetic fraud, where AI-generated identities or transactions can fool traditional systems. And most executives are nervous about data leakage, what happens when sensitive payment data is incorrectly stored, accessed, or even shared unintentionally because the AI wasn’t properly configured or managed.
These concerns are logical outcomes when tools are pushed into production ahead of governance. This early-stage anxiety is what you’d expect when new systems outpace readiness. That’s the current phase we’re in.
Srinivasan Seshadri, Chief Growth Officer and Global Head of Financial Services at HCLTech, puts it plainly, there’s a visible gap between bold expectations and real-world readiness. Companies want the benefits of AI-driven payments, but they haven’t laid the groundwork to stabilize the transition. He talks about the need for Responsible AI, governance that reduces risk without killing innovation. That’s exactly it.
The takeaway for executives is simple: You’ve already deployed AI, now manage it. Get ahead of these issues before they define your limits.
A lack of formal governance frameworks is undermining effective AI utilization
There’s a straightforward problem in this space: lots of AI, not enough rules. Nearly half of all organizations deploying AI in payments don’t have a formal policy for how it should be used. That’s 49% operating without a roadmap or safety net. When the tech starts making decisions, and there’s no framework behind it, breakdowns are almost inevitable.
AI is a series of systems making autonomous or semi-autonomous choices. That demands structure. If teams don’t have a defined AI policy, they can’t enforce responsible use, can’t audit what happens when something goes wrong, and can’t guarantee consistent behavior across teams or geographies.
This is a governance void. And that void is a risk. For enterprise leaders, it’s time to treat this more seriously. If you’ve deployed AI and you’re scaling it, you need rules, oversight, and clear accountability. Shortcuts here will cost you, not just in compliance but in credibility.
Frameworks don’t need to slow you down. They help scale faster and safer, especially when AI becomes a core part of operational decision-making. Becoming AI-ready isn’t about a single tool or team. It’s about alignment across function, product, and leadership, with policies that anchor how AI is trained, tested, deployed, and improved.
This is the part of AI maturity most companies are behind on. Fixing it is the next clear step.
Existing AI-based fraud detection tools are viewed as inadequate
Despite the broad rollout of AI across payment operations, 60% of industry leaders report that current fraud detection tools powered by AI still fall short. These systems aren’t keeping up. Fraud is evolving, faster, smarter, harder to detect, and most AI solutions in use today can’t effectively identify or prevent the kinds of threats emerging in real time.
The investment is there. But tools developed in previous tech cycles weren’t designed to defend against synthetic fraud, multi-layered identity spoofing, or adaptive threat tactics powered by generative models. You’re dealing with adversaries that can evolve quickly. Rigid or slow-learning AI doesn’t match that pace.
Executives face a trade-off between enriching the customer experience with frictionless services and protecting the integrity of their systems. AI is often relied on to deliver both, smooth user journeys and tough fraud control. But that dual goal is only achievable if the systems can actually learn and adapt in real time without compromising accuracy.
This is where current tools are losing ground. Overcautious systems flag legitimate behavior and create friction; under-trained systems fail to catch sophisticated fraud. Neither outcome is acceptable for leaders tasked with operational scale, trust, and risk control.
If you’re an executive scaling digital payment systems, now’s the time to push vendors harder or invest in building smarter fraud detection in-house. AI is capable of extraordinary precision, but only when it’s trained on real-world threats and stress-tested in production. Most companies haven’t done that yet.
A gap exists between aspirations for autonomous payments and current implementation
Autonomy is on the agenda, but the reality doesn’t match the timeline most executives are setting. While 52% of surveyed organizations expect to achieve autonomous payment operations in the next 18 to 24 months, only 17% currently have these systems in full operation. That’s a clear execution gap.
What’s slowing this down is integration, infrastructure, and readiness. Autonomous payment systems require real-time decision-making, secure infrastructure, dynamic fraud protection, and uninterrupted uptime. Most organizations are still working with fragmented systems or legacy workflows that can’t handle that standard at scale.
This doesn’t mean the vision is wrong, it just means the groundwork is incomplete. Scaling to autonomous operations involves far more than automation of manual steps. It demands interoperable platforms, confident use of AI in critical workflows, and governance protocols that allow those systems to run independently without spiraling out of control.
For C-suite leaders watching this space, the takeaway is clear: Expecting full autonomy within 18 months without major investment and internal capability uplift isn’t realistic. If you want to hit that target, start engineering the architecture and compliance systems now. Speed is possible, but only with focus and execution discipline.
Infrastructure limitations impede broader innovation and transformation
There’s strong momentum behind modernization. Most executives don’t want to keep enhancing aging systems. In fact, 58% of them favor adopting innovative, next-generation technologies rather than optimizing legacy infrastructure. That’s the right mindset, but only 20% of organizations have the modern, cloud-native, real-time data systems needed to support scalable innovation.
This mismatch between ambition and infrastructure is a bottleneck. Real innovation in payments doesn’t stop at the software layer. It relies on architecture that can handle streaming data, deliver instant processing, and integrate AI in real time. Without that, performance hits a wall, and execution suffers.
Transformation strategies need strong technical foundations. If your infrastructure can’t handle the velocity and complexity of AI-powered payments, the benefits of automation, fraud detection, and real-time decision-making simply won’t scale. This stalls the value you’re trying to extract from innovation.
Leadership should focus on reducing system fragmentation, committing to cloud-native platforms, and building data pipelines that can support real-time operations. If your systems are still batch-processing transactions or integrating through middleware, you’re already falling behind.
Modernization is more than a checkbox project. It’s a readiness step for competing in a digital-first economy driven by speed, accuracy, and adaptability.
Customer demands and market competitiveness drive investment in payment technology
The pressure to deliver better, faster payment experiences is no longer internal, it’s coming straight from the market. About 87% of executives say they fear losing customers if they can’t offer instant payment capabilities. That kind of urgency is attached to retention, loyalty, and growth.
Today’s customers expect transactions to be immediate, seamless, and reliable. Whether in B2C or B2B, payment delays or friction are seen as product weaknesses. If you’re not delivering a smooth experience, someone else is, and that’s where the business goes.
This expectation shift is pushing companies to accelerate investment in payment modernization and real-time infrastructure. It’s no longer efficient to rely on outdated systems with lagged settlement windows or interrupt-prone integrations.
For leadership, responsiveness to evolving consumer expectations must stay at the center of investment priorities. The gap between customer experience leaders and laggards is widening. Meeting demand is now a function of both technological agility and strategic urgency.
You don’t need to overcomplicate this. Customers want speed. Businesses that deliver it win.
Regional differences highlight a cautious approach in Europe compared to other regions
Not every market is moving at the same pace when it comes to AI in payments. In Europe, the shift is more conservative. Just 19% of European executives feel fully ready for the future of payments. That number reflects a regional preference for caution, where risk management takes priority over fast deployment.
More than half, 57%—of executives in this region prefer updating existing systems rather than deploying entirely new solutions. At the same time, 12% express skepticism about the long-term potential of Agentic AI, which refers to AI systems that make independent decisions within a defined purpose. That skepticism is hesitation based on perceived maturity, risk, and complexity.
While this posture limits exposure to unproven risks, it also slows down innovation. Conservative upgrades may extend the life of legacy technologies, but they don’t always create competitive advantages. For global decision-makers, the contrast is important: pushing the same AI rollout strategy everywhere won’t work. Deployment models that succeed in North America or Asia may need recalibration when entering European markets.
Localized readiness levels must be accounted for in planning. That includes regulatory context, organizational culture, and regional infrastructure standards. Leaders managing multinational operations will need flexibility, standardizing ambition while customizing execution.
Establishing robust and responsible AI governance is critical for the future of payments
Across the board, Responsible AI is the missing link. As more AI systems are deployed in critical operations like payments, the demand for governance grows stronger. Right now, most organizations are operating with limited guardrails, 49% don’t even have formal AI policies in place. That’s a risk no executive should tolerate much longer.
Payment systems deal with sensitive data, real-time transactions, and regulatory exposure. Without clear governance, any AI decision, right or wrong, can escalate quickly. This isn’t just about compliance; it’s about predictability, accountability, and sustained performance.
Governance brings structure. It defines how models make decisions, when human oversight is required, and how ethics, liability, and model drift are addressed. The more autonomous your systems become, the more governance becomes a necessity, not a checkbox. Without it, you risk cascading failures, reputational loss, and regulatory intervention.
Srinivasan Seshadri of HCLTech emphasized this point clearly. For AI systems to deliver long-term value, governance must advance in parallel. Infrastructure, policies, and strategic alignment must work together to ensure AI is not only powerful but trusted.
If you’re investing in AI without investing equally in governance, you’re taking unnecessary risks. Long-term growth in this space will favor companies that scale safely.
Recap
AI in payments isn’t experimental anymore, it’s operational. Nearly every organization is deploying it, but deployment without discipline isn’t progress. The tech is maturing fast, but most systems, policies, and infrastructures haven’t caught up.
Leaders need to recognize that effective AI integration demands more than a strong product roadmap. It requires actionable governance, modern systems, and a clear view of operational risk. Autonomous payments, real-time fraud detection, and instant customer experiences sound great. But without foundational readiness, they remain goals, not results.
This isn’t about slowing down. It’s about scaling responsibly. Build the internal frameworks now to avoid expensive course corrections later. Don’t let weak infrastructure or missing policies limit what AI can deliver for your business.
If you want to compete at the front of the payments economy, readiness isn’t optional, it’s the strategy.
