Niranjan Vijayaragavan, CPO & CTO at Nintex
In 2024, global IT spending in the financial services sector was projected to reach $694.4 billion over the following 12 months – a 9.3% year-over-year increase, fuelled largely by investments in AI, data analytics, and cybersecurity. But in the race to boost efficiency, cut costs, and scale operations, many organisations are rapidly buying software licenses without first addressing the foundational architecture beneath those investments.
Instead of consolidating, teams are proliferating — adding SaaS tools to solve specific issues without cross-functional coordination or integration planning. This has created countless disconnected tools, each solving one specific problem. The result is an overgrown, brittle environment. Data is fragmented across platforms. Interoperability is inconsistent. Identity and access policies become difficult to enforce. The push to accelerate transformation ends up holding them back.
Research shows that over a quarter of UK businesses employ new software in their technology stacks every two to three weeks – a reflection of just how rapidly tools are being added without strategic alignment or governance.
Before trying to thread AI into their operations, financial services firms must first make sense of their tech stacks. Without foundational simplification — both from a systems design and governance standpoint — AI initiatives risk poor ROI, reduced fidelity, and significant exposure.
When architecture breeds chaos
Today, mid-market firms use 100-300 SaaS tools and 41% are adding new tools every 1-3 weeks. That pace creates chaos. Each tool introduces its own data model, authentication logic, workflow engine (or lack thereof), and APIs. Over time, the tech ecosystem becomes a patchwork of point-to-point integrations, manual workarounds, and misaligned governance policies.
Its hitting businesses where it hurts: they’re bottom line. 87% of firms already report moderate to major impact from software sprawl. But that only scratches the surface. Beneath lies operational latency, reduced system resilience, and increased support overhead. Approvals slow down. Data re-entry becomes normalized. User access becomes harder to track and secure.
Efficiency doesn’t come from having countless tools. It comes from intentional design: connected systems, orchestrated processes, and streamlined governance.
AI demands structure, not disorder
If financial services firms want to make the most of AI – whether it’s fraud detection or revenue forecasting – they need access to timely, high-integrity data. But AI models can’t deliver results if they’re trained or run on incomplete, conflicting, or low-resolution inputs.
AI isn’t a miracle worker. When the data it’s working with is old, duplicated, or trapped in silos, models fall short of expectations. Signal-to-noise ratios fall. And explainability – the ability to trace outcomes back to inputs – becomes nearly impossible.
This isn’t just a data quality problem. It’s a systems architecture issue. When tech stacks lack a unified data fabric, AI becomes reactive at best and misleading at worst.
Limiting the attack surface
With every new tool comes added cost and increased complexity. New endpoints, inconsistent access protocols, legacy connectors, stale admin credentials – all of these expand a firm’s attack surface.
Disconnected or orphaned tools are especially dangerous. They fall outside IT’s typical purview but may still house sensitive data, retained credentials, or outdated permissions. It’s an open invitation for bad actors or accidental access by former employees.
Simplifying the stack makes enterprises more resilient to outside threats. It reduces integration points, improves observability, and ensures enforcement of least-privilege access. Stronger security isn’t always about more controls – it’s often about fewer exceptions.
From tech stack to business orchestration layer
The industry is approaching an inflection point. While investment is surging, architectural clarity has yet to catch up. Modernisation isn’t about accumulating more tools – it’s about building the orchestration layer of the enterprise: how systems connect, how data moves, and how processes are initiated, monitored, and governed.
This is where intelligent automation and AI deliver real value – but only if the foundation is in place. Finance and IT leaders must come together around critical questions:
- What tools are business-critical versus redundant?
- Where are the orchestration gaps across functions?
- What does a secure, connected, and observable stack look like in our org?
Without that alignment, AI becomes another layer of complexity rather than a driver of efficiency.
Getting the basics right
The project spend in financial services shows the sector is intent on innovating. From AI-led insights helping firms deliver real-time products and advice to smarter risk assessment and credit scoring, the benefits are clear as day. But businesses need to pause and ask: Are we investing in progress or complexity?
The answer lies in orchestration and simplification. Streamline your existing tech stack. Map where the risks are. Then AI can reach its potential, turning clean signals into smart decisions, at scale.
Because in tomorrow’s financial ecosystem – defined by agility, intelligence, and trust – simpler systems will always outperform sprawling ones.
