Modernising a financial system once meant updating software. Today, it’s about building the foundation for an AI-powered finance function that’s data-driven, agile and future-focused. And as AI and automation transform how finance teams manage, analyze and apply data, finance leaders can play a pivotal role in ensuring financial system modernisation projects deliver value.
Finance transformation initiatives can easily stall out or fail to deliver the expected return on investment (ROI) without appropriate planning, including a thoughtful approach to change management. They also require shared vision, focused leadership, and the right mix of external and internal expertise.
In short, whether you’re migrating to a cloud-based ERP system, integrating financial analytics tools or modernising legacy accounting platforms, the ultimate success of a financial system upgrade depends on more than technology. These six strategies can help guide your process so you can achieve your strategic objectives for AI readiness and broader transformation in the finance function.
Define the ‘why’ for change and what success should look like
Even when organisations recognise the need for financial system upgrades, they often struggle to articulate the true purpose of these projects and why they should be a high priority. So, consider the “why” for change. For example, is the goal to improve reporting speed, strengthen forecasting accuracy, build the data foundation required for AI-powered finance, or all the above?
A clearly defined business case keeps modernisation efforts focused on achieving measurable outcomes, not just adding new capabilities. So, before the start of any tech refresh or net-new implementation, finance leaders and their teams should document expected ROI, define performance metrics (such as time-to-close or forecasting accuracy) and determine how the project supports enterprise goals.
Use a phased strategy instead of a ‘Big Bang’ approach
The days of treating a financial system upgrade as a one-and-done event are over. Modern platforms like cloud-based ERP systems are built for continuous improvement, not static implementation. That means they require a road map that can evolve with changes in business priorities and technology.
Finance leaders should approach system modernisation for their organisation in phases. Begin with a minimum viable deployment, stabilise core processes, then expand to advanced capabilities such as AI-enabled forecasting, embedded analytics and process automation. Short, repeatable delivery cycles accelerate value realisation and improve adoption by incorporating user feedback at every stage—reducing the risk of disruption or costly rework.
A modular ERP architecture supports this approach. Instead of a “Big Bang” rollout, organisations can upgrade individual components—such as financial planning and analysis (FP&A), procurement or payroll—while maintaining business continuity. This incremental model makes it easier to drive adoption, measure success and demonstrate ROI, and pivot as priorities evolve.
Prioritise data governance to help enable AI readiness
The most advanced technology—whether a cloud-based ERP, financial analytics platform or AI solution—won’t deliver value if the data behind it is fragmented or unreliable. Many finance functions still grapple with siloed systems, manual reconciliations and inconsistent definitions of key metrics. These gaps create friction, slow decision-making and limit progress toward AI-powered finance.
That’s why strong data governance must sit at the centre of every modernisation effort for the finance function. Finance leaders should establish clear ownership of financial data, define master data standards and embed data quality controls early in the process.
Data governance isn’t just about compliance—it enables automation, analytics and trustworthy insight.
Data integration and harmonisation are often the hardest—and most underestimated—workstreams in a system modernisation or digital transformation project. So, start early, test often and consider data governance as a continuous discipline. Clean, connected data is the foundation for AI readiness and the advanced financial analytics tools that empower finance functions to deliver strategic value.
Align the right mix of internal and external expertise
The need for skilled professionals to support AI-powered finance functions is accelerating.
ERP projects also depend on high-performing teams that combine internal and external expertise. Key roles often include business analysts, data analysts, technical consultants and data migration specialists—a lineup that is not always possible for businesses to assemble entirely in-house.
Many organisations are turning to flexible talent models that offer quick access to experienced consultants and highly skilled interim professionals. Internal experts provide institutional knowledge and continuity, while external professionals bring transformation experience, implementation discipline, and advanced technical or analytical skills. Together, they form agile teams capable of bridging strategy and execution and delivering projects that generate value for the long term.
Lean into change enablement—and encourage experimentation
Change management helps guide people through a specific transition, like an ERP implementation. Change enablement goes further by empowering teams to embrace transformation as a continuous part of how they work. That distinction is often the deciding factor between success and failure in system modernisation—yet it remains one of the most underestimated.
Organisations pursuing financial system upgrades should strive to create a workplace culture of adaptability, where teams are supported and encouraged to evolve alongside new tools and processes. Finance leaders should communicate early, clearly and often about how modernisation will affect day-to-day work. Involving end users from the start helps build ownership, reduce resistance and strengthen adoption.
Encouraging teams to experiment with automation, analytics and emerging AI tools is crucial. For one, it promotes adaptability and helps drive innovation. But it will also become increasingly important as the use of agentic AI becomes standard practice in finance organisations, with intelligent systems taking on more proactive roles—identifying risks, generating forecasts and streamlining decision workflows. Finance teams that understand how to collaborate effectively and responsibly with more advanced AI capabilities will be well positioned to amplify their role as strategic partners to the business.
Measure success through improved agility, insight and continuous evolution
Many system modernisation initiatives still define success as on time and on budget. Today, that’s only the baseline. True success is measured by how modernisation enhances decision-making, accelerates financial cycles and builds long-term resilience across the organisation.
Finance leaders and their teams should track key performance indicators that link directly to strategic value—such as close-cycle time, forecasting accuracy, process efficiency, cross-functional visibility and the adoption of real-time analytics. These metrics demonstrate tangible business impact and reinforce the case for ongoing investment in technology and talent for the finance function.
Modern financial systems are designed to evolve. Regular updates, AI-enabled features and automation capabilities can extend value well beyond go-live. Forward-looking organisations can plan for this continuous evolution by dedicating resources to innovation, upskilling and post-implementation optimisation—turning each upgrade into another step toward an AI-powered finance function.
Successfully upgrade financial systems—and more—with help from Robert Half
Robert Half connects organisations with the specialised talent and consulting professionals they need to help build a modern finance organisation. Whether you’re focused on system modernisation, ERP transformation, AI implementation, or data and analytics integration, our flexible delivery model can help you align talent and strategy to achieve measurable results and stronger ROI.
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