While meeting resilience, sovereignty, audit and compliance requirements, CIOs need to govern voice, contact center, collaboration and routing systems across markets.

Across the banking industry, CIOs are being asked not just to introduce AI capabilities, but to scale them across the organisation while navigating requirements around operational resilience, data sovereignty, conduct, audit and regulatory compliance.

Yet, as banks accelerate their AI ambitions, attention is increasingly turning to the infrastructure and governance environments beneath those tools.

AI is exposing a challenge many multinational banks have lived with for years: fragmented communications environments spanning voice, contact centers, collaboration, routing and customer engagement, and the observability and governance capabilities that sit across them.

Legacy infrastructure problems surfaced by AI

For many multinational banks, communications environments have evolved over decades, shaped by acquisitions, local market requirements, regulatory obligations and successive waves of technology investment.

Regional teams adopt platforms that best suit local needs. Regulatory requirements vary from one jurisdiction to another. New technologies are introduced to address emerging business priorities, while legacy systems continue to support critical operations.

Over time, organisations find themselves managing a complex mix of voice, contact centre, routing and collaboration platforms across different markets. This complexity is not necessarily the result of poor technology decisions. In many cases, it reflects the realities of operating across multiple jurisdictions, where local requirements, regulatory obligations and business needs require a degree of flexibility.

Historically, banks had found ways to work around this complexity. Employees adapted and integration layers were added where needed. However, AI now changes the equation, not because it creates these underlying challenges, but because it makes their operational and economic consequences harder to ignore.

Unlike many previous technology initiatives, AI does not operate in isolation. It depends on access to information, workflows, customer interactions and institutional knowledge that span multiple systems and teams. The more ambitious an organisation’s AI strategy becomes, the more those underlying connections matter.

This is where a manageable level of complexity can become a strategic challenge. Many institutions are already experimenting with AI. The next phase is embedding those capabilities across the organisation in a way that is consistent, secure and scalable across multiple markets, platforms and regulatory frameworks. That is where fragmented foundations can become a strategic liability.

Layering AI onto fragmented foundations?

While many banks have successfully deployed AI within individual functions or markets, fragmented communications environments can become a barrier when those capabilities need to be scaled across the wider organisation.

In fragmented environments, every new AI deployment creates additional integration requirements, governance considerations and operational dependencies. The challenge extends beyond connecting different systems. Fragmented governance can also result in inconsistent controls, audit trails and approaches to operational resilience across markets. Rather than simplifying operations, AI can inadvertently add another layer of complexity:

  • Technology teams may find themselves supporting multiple integrations across different communications platforms
  • Employees may encounter different workflows and capabilities depending on where they are located.
  • For customers, this can translate into inconsistent experiences across markets and channels, potentially creating service-quality issues and conduct risk.
  • For banks, the same fragmentation can increase operational complexity, create gaps in auditability and compliance, and make AI capabilities harder to govern and scale consistently.

Consider a customer of a multinational bank who would engage a relationship manager, contact center, mobile application or branch. AI is increasingly expected to support all those interactions, but when the communications systems underpinning those channels vary significantly across markets, delivering consistent experiences and governance becomes far more difficult.

When communications environments become overly fragmented, organisations may find themselves deploying and governing AI market by market, rather than scaling capabilities across the enterprise. The result can be higher costs, duplicated effort and an innovation agenda that stalls where it should accelerate.

Balancing innovation with regulatory reality

As AI becomes more deeply embedded into operations, banks will need to balance innovation with data security, operational resilience, governance and regulatory requirements.

This challenge is particularly pronounced across the Asia Pacific region where requirements around data sovereignty, cybersecurity and risk management can vary significantly between jurisdictions. In fact, according to one IDC poll, more than 60% of specified Asia-Pacific respondents had cited they were already experiencing moderate to significant disruption to IT operations because of evolving data privacy, cybersecurity and AI regulations.

When communications environments and governance frameworks are also fragmented across markets, maintaining compliance consistently while scaling AI becomes harder.

Communications architecture can no longer be treated simply as background infrastructure. For banks, it is becoming part of the governance framework through which AI is controlled, audited and scaled while maintaining operational resilience and meeting jurisdiction-specific requirements.

Therefore, a more connected communications environment can help create the conditions for AI to be scaled responsibly and consistently.

Communications infrastructure as a strategic enabler

As banks enter the next phase of AI adoption, the conversation should extend beyond individual tools and use cases. Institutions need to address underlying complexity rather than continuing to layer new tools on top of it. This does not mean replacing every system or enforcing rigid standardisation.

  • ✓ For multinational banks, local flexibility should remain a design principle, allowing institutions to meet jurisdiction-specific regulatory and business requirements while establishing common capabilities and governance where they add value.
  • ✓ The objective is a more connected communications environment that enables interoperability across voice, collaboration, customer engagement and AI-enabled services, while greater visibility, consistent policy controls, auditability and operational resilience, with the ability to apply jurisdiction-specific controls where required: Not standardization for its own sake, but a deliberate reduction of complexity so innovation can move more freely across the organization.
  • ✓ Historically, communications infrastructure had been viewed in the industry largely as operational plumbing. In the AI era, it is becoming the connective layer linking employees, customers and intelligent systems across the enterprise — as well as an increasingly important part of how banks maintain visibility, control and resilience.

AI may not have created the complexity that exists across multinational banking environments, but it is making the cost of that complexity increasingly visible. Succeeding in the next wave of AI adoption in banking will therefore not necessarily be focused on deploying the most AI tools, but on creating the conditions for AI to operate consistently across markets, channels and business functions — in a way that is scalable, governed and resilient.

For CIOs, the challenge is no longer simply to sponsor more AI pilots. It is to create the technology and governance environment that allows successful use cases to scale responsibly across the enterprise.