Modernisation That Matters
Turning Tech Overhaul into Competitive Advantage
Enterprise technology has never stood still, but the current pace of change is relentless - and for many financial services firms, it’s becoming a question of survival. The demands of scalability, compliance, AI readiness and customer expectations are rewriting the rules for IT investment. What was once an incremental process of upgrades has increasingly become a strategic imperative: modernise or fall behind.
Yet despite the urgency, systems modernisation often feels like yesterday’s news. It’s long been written about, spoken about and budgeted for without consistently delivering transformative results. So why revisit it?
Because the foundations for modernisation are changing so dramatically.
We’re no longer talking about swapping out servers or moving to the cloud as a checkbox capex to opex exercise. We’re talking about redefining how businesses operate, compete and grow in a world shaped by real-time analytics, generative AI and ever more interrelated and complex tightening regulations. The stakes are higher, the risks more nuanced and the rewards far greater.
In this thought leadership piece, we reposition tech stack modernisation not as a purely technical upgrade, but as intrinsic to a business growth strategy and identify a clear action plan for what leaders in financial services must do now to ensure their systems are working for them and continue to do so without blocking future advancement.
The urgency to modernise technology infrastructure is no longer a non-time critical strategic consideration; it has become a pressing operational reality. Across interconnected business operating units, firms are increasingly constrained by legacy systems that were never designed to support the scale, speed or complexity of today’s digital demands. The result is a growing performance gap between those with modern, flexible architectures and those relying on outdated technology stacks.
A significant proportion of enterprises, according to some industry analysis, as much as 80% have reported serious difficulties keeping pace with innovation, scaling requirements and compliance obligations solely attributable to out-of-date technology structures.
These challenges are compounded by fragmented data landscapes, rigid architectures and limited automation, all of which undermine agility and operational resilience.
Legacy environments frequently lack the modularity and interoperability required to support advanced technologies such as machine learning, real-time analytics or dynamic risk modelling. In many organisations, critical data remains siloed across regional systems, incompatible platforms or manual processes, preventing a unified view of customers, operations or risk exposure. This fragmented state impairs decision-making, slows down innovation and increases exposure to compliance failures.
For those financial services firms that have achieved cloud-native technology estates, they have benefited from offering real-time onboarding, automated decision making and highly personalised experiences which meet and exceed customer expectations across the board. Incumbents constrained by inflexible systems have found it increasingly difficult to respond. Product development cycles are often delayed by technical dependencies, while regulatory reporting to comply with regimes like GDPR, PSD2 or DORA requires data orchestration capabilities far beyond what legacy systems can support.
Financial Services is well known for the immense and rapidly growing volume of data requiring processing. However, without modern data infrastructure capable of handling high-volume, high-velocity inputs, this data remains seriously underutilised. By the time insights are derived through traditional ETL processes and on-premises analytics, opportunities for intervention have often passed. While AI tooling may provide valuable capabilities to improve this, it will only happen if the underlying technology stack is fit for that purpose.
Firms that postpone modernisation are prone to rising support costs, heightened security vulnerabilities, and increasing difficulty attracting digital talent. Technical debt accumulates quickly, creating friction across departments and slowing time to market. Even incremental improvements become more difficult and expensive to implement as ageing systems resist change and expose organisations to escalating risk.
Technology modernisation really must be seen as an enabler of strategic agility. It allows organisations to harness automation, enhance resilience and scale operations in line with evolving market demands. Critically, it also lays the groundwork for future capabilities such as generative AI, autonomous systems and embedded compliance frameworks.
"80% of firms report serious difficulties keeping pace with innovation, scaling requirements and compliance obligations due to outdated technology structures"
Cloud adoption has become a foundational step in most modernisation initiatives, but it is a mistake to view it as the destination. While cloud technologies offer undeniable advantages - elastic scalability, operational efficiency and global accessibility - the reality is that cloud migration alone rarely delivers the transformative outcomes organisations seek. In practice, cloud modernisation is most successful when it is part of a broader, integrated strategy that considers architecture, operations, culture and long-term business objectives. It is also more successful when the firm takes into account the full suite of solution offerings of the leading cloud providers, rather than just limiting itself to a few direct replacements for old legacy tech.
Some firms that overly focused on the push from capex to opex with a cloud migration have fallen foul of the variability of cloud pricing by not aligning the operational workloads to favourably priced solutions and only discovering their mistake over time. Recently, there has even been some reversion to on-premise, but this isn’t a reflection of higher quality and value, but rather a reflection of a poorly implemented initial cloud migration.
Added to this, the shift from traditional, on-premises infrastructure to cloud-native environments requires more than a lift-and-shift approach. Organisations that treat cloud as a hosting alternative rather than an architectural enabler often struggle with further cost overruns, minimal performance gains and limited agility. Without refactoring applications, modernising workflows and adopting cloud-native principles such as microservices, containers and serverless computing, the benefits of the cloud remain superficial.
Moreover, cloud adoption introduces new challenges that extend beyond the technical domain. It affects governance models, security practices, team structures and budget allocations. In many cases, cloud migration is not a single project but an ongoing transformation effort. It touches everything from application lifecycle management to vendor procurement strategies and requires careful alignment across IT, security, operations and compliance teams.
Organisations that approach cloud adoption strategically are increasingly focusing on hybrid and multi-cloud environments to balance flexibility, risk management and cost control. This approach allows them to maintain control over sensitive workloads while leveraging the agility and innovation of public cloud services for others. However, managing these hybrid environments demands new levels of orchestration, visibility and policy enforcement to ensure interoperability and governance.
Operational maturity is also a key differentiator. Organisations with clearly defined DevOps practices, robust observability tools and well-governed automation pipelines are more likely to see meaningful returns from cloud investments. These capabilities ensure that development teams can deploy updates rapidly, monitor performance continuously and respond proactively to incidents - unlocking the agility the cloud was meant to deliver.
Culturally, the move to cloud requires a shift in mindset. Even today, it is fair to say that there remains a huge swathe of legacy thinking when it comes to best use of the cloud. Teams often still tend to relate activities traditional deployment approaches and struggle to adapt to new workflows, tooling and accountability structures. Upskilling, knowledge sharing and cross-functional collaboration are the essential enablers of success. Without this cultural alignment, the risk of stalled adoption or misalignment between technology and business goals increases significantly. This also has to go all the way up the management chain to the C level but often fails to do so.
Cloud modernisation should be seen as a platform, not a project. It is the infrastructure upon which modern digital capabilities are built, but it must be combined with agile methodologies, modern security frameworks and strong data governance to deliver sustained value. The goal is not simply to migrate, but to transform how technology supports the business - making it more adaptive, resilient and ready to scale in line with strategic ambitions.
Justify cloud modernisation across all management levels for cultural change.
Treat cloud modernisation as a platform, not a project, with agile practices.
Aim to transform technology to improve adaptability, not just migrate workloads.
Use hybrid and multi-cloud strategies for flexibility and risk management, requiring strong governance.
In many organisations, the gap between business ambition and IT delivery is not caused by a lack of vision, but by inefficient programme management, software development and deployment practices.
While cloud platforms and modern data tools often receive the spotlight in digital transformation efforts, DevOps remains one of the most critical - yet underutilised - levers for accelerating change. When implemented effectively, DevOps can unlock dramatic improvements in speed, reliability and operational resilience.
At its core, DevOps is about aligning development and operations teams to streamline the entire software delivery lifecycle - from planning and coding through testing, deployment and monitoring. But beyond technical practices, it represents a cultural shift: one that emphasises collaboration, shared responsibility and continuous improvement.
In legacy environments, release cycles regularly span weeks, months, even years - plagued by manual processes, duplicated environments and unpredictable deployment outcomes. These bottlenecks not only slow innovation but also increase the risk of defects, downtime and compliance failures. In contrast organisations that embrace DevOps move from episodic to continuous delivery, enabling faster time-to-market, more frequent updates and lower operational risk.
Automated testing, infrastructure-as-code and continuous integration/continuous deployment (CI/CD) pipelines form the technical foundation of DevOps. These tools allow teams to test code automatically, deploy it rapidly and roll back changes with minimal disruption if needed. The result is a system that is both high-velocity and high-reliability, where incremental improvements become routine and innovation cycles accelerate.
Importantly, DevOps also introduces a stronger focus on observability and feedback loops and brings an essential alignment between technology and the business operations. Monitoring tools integrated into the deployment pipeline allow teams to track performance, user behaviour and security metrics in near real-time. These insights enable faster identification of issues, better decision-making and the ability to adapt systems based on actual usage patterns rather than assumptions.
From a governance perspective, DevOps can support stronger compliance outcomes. When properly implemented, DevOps workflows embed policy enforcement, version control and audit logging directly into the delivery process. This reduces reliance on manual checkpoints and creates an auditable, transparent system that satisfies both operational and regulatory scrutiny.
Despite these advantages, DevOps adoption remains inconsistent. Some organisations lack the skills or organisational alignment to move away from traditional development models. Others underestimate the cultural change required – the legacy attitude of business users dropping a set of requirements on the technology teams desk and walking away remains worryingly pervasive.
Success depends on more than tooling - it requires executive sponsorship, clear ownership of end-to-end delivery and a commitment to iterative learning.
In the context of broader systems modernisation, DevOps is not optional - it’s foundational. It turns IT from a bottleneck into a catalyst, enabling teams to build, test, deploy and improve software at the speed required by today’s markets. For organisations seeking stability and speed in equal measure, DevOps offers a disciplined, scalable path to continuous transformation.
While infrastructure, data and security often dominate the agenda in systems modernisation, user experience (UX) and user interface (UI) design remain among the most underestimated drivers of digital success. In today’s environment, where user expectations are shaped by seamless consumer-grade applications, experience is not just a nice-to-have. It is a competitive differentiator, a productivity multiplier and in many cases, a decisive factor in technology adoption and ROI.
Legacy platforms, even those with robust functionality, often suffer from clunky, inconsistent or inaccessible interfaces. These experiences frustrate users, increase error rates and reduce engagement. Whether the end users are clients, employees, partners or regulators, a poor interface can undermine the value of even the most sophisticated back-end systems.
This is particularly significant in financial services and other regulated industries. Complex internal systems used for risk modelling, compliance reporting, or client onboarding are often packed with features but lack intuitive design. As a result, users rely heavily on training, workarounds, or manual intervention. These inefficiencies compound over time, increasing operational costs and reducing responsiveness.
Modern UX/UI design addresses this by placing usability, accessibility and responsiveness at the centre of system development. Well-designed interfaces reduce demand on the users and guide them intuitively through complex workflows. Importantly, they also adapt across devices and screen sizes, supporting mobile-first use cases and hybrid work environments.
Well-designed interfaces reduce demand on the users
From a strategic perspective, UX/UI modernisation is not just about aesthetics - it directly impacts business performance. A clean, responsive interface increases adoption rates, shortens training cycles and reduces support demand. It empowers users to make faster, more confident decisions, especially when interacting with complex datasets or navigating multi-step processes.
Moreover, UX/UI is a key enabler of digital innovation. Technologies such as embedded analytics, AI driven recommendations and natural language processing are only valuable if users can engage with them intuitively. A modern interface acts as the bridge between powerful technology and productive usage. Without it, advanced capabilities risk going unused or misunderstood.
In transformation programs, UX/UI must be embedded early - during discovery and design phases - not treated as an afterthought. Close collaboration between business stakeholders, developers and designers ensures that user journeys are aligned with strategic outcomes. Modern design systems and component libraries can also streamline development, ensure consistency and reduce time-to-market.
Ultimately, the interface is where digital strategy meets user reality. If the goal of modernisation is to unlock productivity, accelerate service delivery and delight customers, then UX/UI must be treated as a core pillar not a peripheral consideration. Organisations that prioritise design as part of their technology strategy will not only improve user satisfaction but also increase the return on investment in platform modernisation.
As organisations accelerate their investments in artificial intelligence, machine learning and predictive analytics, many encounter an unexpected barrier: no amount of advanced tooling can compensate for poor data foundations. Unlocking the potential of AI and advanced data analytics begins not with models or algorithms, but with a carefully considered data infrastructure: structured, accessible, governed and ready for real-time access.
Across industries not just Financial Services, data fragmentation remains one of the biggest barriers to modernisation. Data is routinely distributed across multiple systems, in different formats, maintained by different teams and governed by inconsistent policies. This creates a fragmented landscape where data quality is difficult to verify, lineage is unclear, and integration is costly and slow. In many cases, even basic reporting is time-consuming, let alone advanced analytics or AI driven automation.
For organisations pursuing AI and machine learning, this presents a major constraint. These technologies depend on reliable, high-quality and consistently structured data to generate accurate outputs. Inadequate data foundations lead to unreliable results, increased model drift and underwhelming business outcomes. Worse still, they often cause projects to stall entirely - resulting in wasted investment, lost credibility and missed opportunities.
Modernising the data layer is therefore a prerequisite to AI readiness. This begins with unifying data sources, centralising and/or integrating storage and introducing pipelines that can clean, transform and process information at scale. Cloud based data lakes, data warehouses and event driven architectures are increasingly being used to enable scalable, cost-effective access to large volumes of structured and unstructured data.
Equally important is the governance framework that sits around the data. As regulatory expectations grow, covering everything from privacy and consent to transparency and explainability organisations need to enforce consistent data classification, auditability and access control. A robust governance model enables organisations to innovate confidently while remaining compliant with data protection regulations. In this context, real-time data processing and streaming capabilities are becoming essential. Business decisions increasingly depend on up to the minute insights, not historical reports. Whether it’s detecting fraud, predicting customer churn or optimising operations, the value of data lies in its immediacy and relevance.
For many organisations, the path forward lies in adopting data fabrics or data mesh architectures that allow for decentralised access while maintaining enterprise-wide standards. These modern architectures support scalability, flexibility and the integration of advanced analytics without requiring wholesale replacement of existing systems.
The implications are clear: without foundational data modernisation, advanced technologies such as generative AI, NLP or autonomous decision-making will simply be out of reach. But with the right infrastructure in place; data platforms, integration pipelines, governance tools and real-time analytics - organisations gain more than insight. They gain the agility to act on that insight quickly, securely and at scale.
Data is often referred to as "the new gold" but it is also the engine of digital transformation. Treating it as such means investing in the infrastructure, discipline and strategy required to make it useful, trusted and transformative.
As enterprises accelerate systems modernisation, the role of security, governance and compliance must be fully integrated and not treated as parallel or downstream concerns. In today’s business environment trust has elevated to the point that it is a strategic asset. Maintaining it requires more than defensive security or annual compliance reviews; it demands continuous, embedded controls that evolve in lockstep with technology, regulation and risk.
Modern digital ecosystems, particularly those built on cloud native, distributed or hybrid architectures, introduce new exposure points. Traditional perimeter-based security approaches aren’t enough when data, users and applications are increasingly decentralised. A more robust and adaptive model is required, one that embeds protection into the architecture itself and assumes no element of the system is inherently secure. The shift toward Zero Trust is security is a sign of this evolution. It represents a mindset in which every access request is continuously verified, least-privilege access is enforced by default and policies are applied consistently across environments, regardless of where users or data reside. While requiring careful implementation, the Zero Trust approach enhances flexibility without compromising security - a crucial balance for organisations seeking to scale with confidence.
the Zero Trust approach enhances flexibility without compromising security
In parallel, governance must evolve from static control oversight into a dynamic, enabling capability. Legacy approaches that rely on manual processes, periodic audits or post-deployment reviews no longer provide adequate oversight. Instead, governance must be automated, integrated and real-time, capable of enforcing policies across systems without obstructing speed or agility. Modern platforms enable data lineage tracking, embedded audit trails and automated policy enforcement at every layer, from infrastructure-as-code to API access. These capabilities not only reduce compliance risk, but they also create a verifiable and defensible record of how data is handled, decisions are made and systems are maintained.
This evolution is particularly urgent in highly regulated industries like financial services, where the regulatory environment continues to expand and intensify. Frameworks such as GDPR, DORA and the upcoming AI Act in Europe, as well as sector-specific obligations around algorithmic transparency and operational resilience, are pushing firms to demonstrate both capability and intent. It isn’t enough simply to be secure; regulated firms must be able to clearly prove it. Those that can deliver clear, explainable governance over data, algorithms, access and infrastructure are in a stronger position to engage with regulators, partners and clients, all of whom have an expectation of transparency as a baseline.
Importantly, security and compliance are increasingly becoming commercial differentiators. Procurement processes regularly include detailed reviews of cyber resilience and data governance practices. Investors assess operational risk in terms of exposure to breaches or regulatory failures. Clients, particularly institutional ones, require verifiable proof that their data is handled securely, ethically and compliantly. In this context, robust security and governance practices don’t just protect reputation, they help secure client growth.
For modern enterprises, the ability to innovate cannot come at the expense of control. That is why successful transformation programmes do not treat governance as an afterthought. Instead, they build trust directly into systems architecture, development pipelines, data platforms and operational processes.
Security and compliance are not added on at the end, "security by design" is architected in from the beginning.
As regulatory frameworks continue to evolve and as the attack surface of digital business continues to expand organisations must adopt a model of continuous adaptation. Static policies and annual audits cannot address the pace of change in risk. Real-time monitoring, behavioural analytics, anomaly detection and automated remediation must become standard components of enterprise resilience. AI is a great adjunct to this – if it can be deployed in the firm’s technology environment.
As has been seen from the spate of recent corporate hacks, business continuity strategies must account for cyber scenarios just as rigorously as physical or financial ones.
Ultimately, the most successful modernisation efforts will be those that embed trust at every layer: technical, operational and strategic. By doing so organisations gain not only security and compliance, but also the freedom to innovate confidently, scale responsibly and lead in markets where trust is a prerequisite for participation.
