Data-fueled tech enablement can drive cost optimisation

Making the business case for tech-driven continuous cost optimisation

Cost optimisation is crucial to business performance. Continually reducing spend while making smarter investments to drive profitability growth enables organisations to become more resilient and agile. However, research reveals that cost optimisation efforts in most organisations need substantial improvement. A 2024 survey of global finance leaders and professionals found that most cost optimisation efforts—from third-party spend risk assessments and automation initiatives to headcount reductions and outsourcing/offshoring strategies—failed to achieve meaningful, measurable progress in the previous 12 months. On the bright side, a majority of CFOs and finance leaders reported in the survey that they made significant progress with cost optimisation through technology rationalisation and utilisation of cloud-based systems and, for those who used it in finance operations, AI. 

These positive results point to the path of greatest potential success. How can CFOs and their organisations elevate the performance and outcomes of cost optimisation initiatives? The solution is deceptively simple: A majority of cost optimisation enablers consist of advanced technologies and tools. Leveraging the full power and value of AI, machine learning, other forms of automation, cloud-based systems and the analytics that support strategic sourcing initiatives requires a receptive technology environment. 

The bottom line is that more value is generated by these investments when there is minimal technical debt created by outdated legacy systems. This tees up the importance of technology modernisation strategies, which reduce technical debt while supporting investments that advance cost optimisation efforts.

Aligning technology enablement with business strategy

As organisations strive to achieve higher profitability through topline growth and strategic cost optimisation, they must first ensure technology investment and modernisation plans are aligned with business strategy and objectives. Such alignment better positions them to identify new opportunities and manage potential risks. The CFO should have a significant voice in this discussion, in collaboration with the CEO, CIO and other C-suite colleagues. 

In our view, the most effective technology modernisation efforts:

• Articulate the risks of sticking with the status quo. The costs of failing to modernise a technology environment often are severe: falling short of key business objectives, higher operational costs and process inefficiencies, subpar customer experiences that erode revenues, talent attraction and retention difficulties, and ultimately the loss of competitive advantage and shareholder value. In rapidly evolving markets, companies hamstrung by the proverbial ball and chain of legacy technology environments and systems cannot sustain the pace of competitors that leverage more advanced technologies.
• Identify a broad range of options and timetables. Technology modernisation takes many forms, and the approach undertaken for it should be considered carefully with an eye toward maximising value and reducing financial risk. It does not necessarily mean, for example, a complete ERP transformation. In addition to updating existing software to the latest versions and/or migrating systems to the cloud, organisations can build or buy new solutions that reflect the latest application development approaches and trends (e.g., microservices architecture, containerisation and serverless computing). Another technology modernisation approach could involve third parties—for example, acquiring another company with a modernised application stack, which can quickly deliver benefits via an effective integration effort. Also consider that in most cases, a phased approach to technology modernisation may be preferred to a massive transformation project placed on a tight, rigid schedule.
• Prioritise data quality and management. Data-driven analytics are a powerful tool that can help finance groups and the broader enterprise facilitate real-time collaborations, digital on-demand planning, integrated driver-based machine learning models, predictive and prescriptive insights, self-service reporting across mobile platforms and much more. Of course, this assertion presumes that the data used is not incomplete, disorganised, distorted or inaccurate. CFOs need to be an advocate in positioning data governance and management as a critical component of modernisation efforts by ensuring that the following questions are asked and addressed:
  • What data do we need?
  • Do we understand the sources of this data?
  • Do we have control over these sources?
  • Do we need to apply any transformations or normalisations to this data (i.e., cleanse, structure and scale the data into useful formats to make it more suitable for analysis to support decision-making)?
  • Do end users trust the data for their use and reporting?
• Leverage core technology principles to drive continuous modernisation. At its foundation, technology modernisation is driven by a handful of technology building blocks, including a simplified and decoupled architecture, support for modular and agile deployment, scalability and security (which enable rapid, secure deployments and updates), and as noted above, a unified and well-governed data repository that helps establish a single and reliable source of truth across the organisation. CFOs need to ask their CIOs about the degree to which modernisation efforts employ these enablers. Technology modernisation initiatives should also prioritise the incorporation into IT solutions of real-time and event-driven processes, user-centric design, and compliance considerations.

Enterprisewide technology modernisation activities also cover finance systems and applications, which is why finance technology roadmaps must be kept current. To that end, CFOs should prioritise technology investments (cloud-based financial systems, AI, machine learning and advanced analytics) that not only drive efficiency and innovation but also contribute to subsequent cost optimisation progress. 

Lastly, to no surprise, AI needs to warrant close consideration from CFOs. In the past year, more finance groups have moved beyond using generative AI to improve the efficiency of compliance and risk management activities to deploying generative AI solutions throughout the order-to-cash cycle and in expense management initiatives—and are posting lucrative long-term returns as a result. Those experiences, enjoyed by organisations that have moved beyond talking about AI to deploying it, need to be leveraged by the companies already in the game and recognised as the way forward by those just getting there. As promising as AI tools are today, agentic AI may soon generate even more business value as companies deploy digital labour to scale new operations and businesses quickly and more cost-efficiently.

Organisations burdened by legacy systems, data quality issues and integration challenges find fewer growth opportunities due to the technical debt they bear. This is why it's crucial for CFOs to lead efforts to drive technology modernisation, enablement and cost optimisation, adopting a mindset focused on consistently realising tangible value. In doing so, finance leaders should ensure that proposed investments in targeted use cases are backed by a strong business case that clearly articulates the value and ROI to be achieved.