As-a-service operating models

Cloud, AI and hybrid environments have shifted from peripheral systems to essential pillars of modern business.

These technologies now form the bedrock of resilience, innovation and security. And as organizations race to adopt them, the challenges and costs of managing intricate digital ecosystems are rising just as quickly.

Global spending on public cloud services is projected to reach US$723.4 billion in 2025, up more than 20% from 2024. At the same time, cloud security is emerging as a distinct market, projected to grow from US$9.25 billion in 2025 to more than US$50 billion by 2035.

Most enterprises now operate in hybrid environments, with more than 80% blending on-premises and cloud, and 63% using more than one provider. On average, organizations juggle up to three distinct environments.

AI is fueling the fire. More than half of organizations are running AI workloads in the cloud, but 34% of those deployments have already been linked to breaches. Meanwhile, the sensitivity of data stored in the cloud continues to rise. Today, more than half of cloud-based data is classified as sensitive, yet fewer than one in ten organizations encrypt all of it.

Unsurprisingly, cloud security ranks high on executive agendas, with 64% of leaders naming it a top-five concern. Budgets are shifting accordingly, with growing emphasis on AI-driven security tools. However, technology alone won’t close the gap.

Teams are still struggling with cost visibility, governance, compliance and the day-to-day complexity of managing hybrid estates. The opportunity is clear. Financial institutions that can master security, control costs and simplify operations will be better positioned to turn digital transformation into measurable returns.

Adoption only creates value when the business is ready to absorb it. Adding cloud, hybrid and AI capabilities on top of fragmented operations may increase capacity, but it rarely delivers the full commercial benefit. Real transformation is about reshaping how services are delivered, governed and improved so new capabilities can be used at scale and with confidence.

That starts with simplification. When institutions reduce duplication, clarify accountability and streamline handoffs across functions, they remove much of the friction that slows change. The payoff is tangible for clients and employees alike — faster product launches, more consistent service and fewer delays caused by internal complexity.

Transformation also creates the consistency needed to scale without losing control. Shared ways of working, clearer governance and better visibility across the estate make it easier to introduce new capabilities without creating fresh operational risk each time. For leadership teams, that means stronger resilience, fewer surprises and a firmer grip on both cost and compliance.

Just as importantly, transformation improves how talent is used. Instead of highly skilled teams spending their time on manual support, disconnected reporting and repeated workarounds, they can focus on growth, client experience and stronger risk oversight. That shift doesn’t just improve efficiency. It helps the organization respond more quickly to market changes and make better decisions with less noise.

This is the bridge between modernization and meaningful business outcomes. Cloud, hybrid platforms and AI can all expand what a firm can do, but only if the operating model is built to support them.

Transformation turns adoption from a collection of technology initiatives into a repeatable business capability — and that’s what enables firms to scale innovation without sacrificing trust or control.

The conversation has shifted from “should we try this” to “how do we scale responsibly and securely?” CIOs and IT leaders are being pushed to integrate AI into the trading, risk and compliance stack while keeping a sharp eye on costs, latency and regulatory obligations.

Agentic AI and automation are advancing front-to-back-office workflows. Instead of pilots that merely generate analytics, firms are deploying AI agents that can execute multistep tasks such as reconciliations, client reporting and trade surveillance.

However, while creating efficiency, this also raises new questions about oversight, auditability and alignment with regulatory expectations.

Multimodal AI and reasoning models are emerging as critical tools in risk and portfolio management. By fusing structured market data with unstructured feeds such as news, filings and even voice calls, these systems provide richer context for trading desks and compliance teams. The payoff is better scenario modeling and faster response to market shocks, but it requires heavy investment in data quality, model governance and compute infrastructure.

Regulators in the United States, the UK and the EU are pressing banks to demonstrate transparency in AI decision-making, explainability of models and the sustainability of their infrastructure. It’s not just about bias or privacy anymore. It’s about operational resilience and reputational risk if models misfire or drain resources unnecessarily.

Open ecosystems are also gaining traction. Many banks don’t want to be locked into a single provider. Open-source AI frameworks and shared industry standards are enabling interoperability between vendors, facilitating seamless integration into trading platforms and risk systems. This approach gives CIOs more leverage in cost control and vendor negotiations.

Infrastructure escalation is shaping strategy. Cloud capacity, custom chips and edge computation are being prioritized not just for speed but also for regulatory compliance and energy efficiency. Firms are weighing geopolitical risks in their supply chains alongside the business case for scaling AI workloads.

Together, these drivers are redefining what digital transformation means in the banking and capital markets sectors. It’s no longer about experimentation. It’s about building AI into the firm’s DNA while maintaining resilience and trust at its core.