What It Actually Takes to Scale AI in Your Finance Function

Finance teams have always been asked to do more with less. But the nature of that ask is changing in a fundamental way. 

For decades, technology helped finance teams do the same work faster. Spreadsheets replaced ledgers. ERP systems replaced spreadsheets. Each successive wave compressed the time and manpower required to close the books. AI represents something different. It does not simply accelerate existing processes but creates the conditions for finance teams to move beyond them entirely; this redirects capacity toward the forward-looking, decision-shaping work that most finance functions have never had the bandwidth to pursue. 

The opportunity is significant. But so is the gap between organisations that are capturing it and those that are not. Closing that gap is not, as many assume, primarily a technology challenge.

Adoption Is Accelerating, But Scaling Remains a Challenge 

Many organisations have already begun integrating AI into finance processes and workflows. Research from APQC, surveying more than 600 finance leaders, found that nearly all organisations surveyed were already exploring or piloting AI in finance. At the same time, at least 60% of financial institutions in Singapore were reported to be using AI in production environments rather than simply test pilots. 

Yet, as organisations move from pilots to production environments, questions around governance, accountability, and trust become increasingly important. 

Finance operates in an environment where accuracy, accountability, compliance, and auditability are critical. As reliance on AI-generated outputs grows, organisations must address questions around trust, oversight, and risk. 

The challenge is therefore no longer about access to AI technology. It is about creating the governance and operating foundations needed to scale AI responsibly and sustainably.

Governance Is the Foundation for Scalable AI 

In 2025, the Monetary Authority of Singapore formalised this with its Guidelines on Artificial Intelligence Risk Management, placing board accountability, active AI assessments, infrastructure, and people at the center of responsible AI adoption. While the framework applies directly to financial institutions, it reflects a direction of travel relevant to all regulated industries; governance of AI outputs is becoming a baseline expectation, not a discretionary consideration. 

For finance functions specifically, governance is not a compliance exercise. It is the condition that makes AI valuable at all. 

Consider a straightforward example: An AI model that generates a reliable cash flow forecast is only useful if the organisation has built the accountability infrastructure to act on it with confidence. That means defining who owns the output, how it is validated before it informs a decision, and how it can be audited afterward. Without that infrastructure, the forecast exists – but the organisation cannot fully rely on it. 

The organisations that are genuinely ahead in using AI for finance are not the ones that have deployed the most tools. They are the ones that have built the governance foundation first and allowing technology decisions to follow from it. Each use case deployed within a well-governed architecture becomes easier to trust, easier to extend, and easier to audit. The foundation compounds in value as the programme scales. 

Governance built after deployment is a retrofit. And in finance, retrofitting accountability after AI outputs have already been acted on is a considerably harder and riskier exercise than building it in from the start.

Three Steps Finance Leaders Can Take This Quarter 

For finance leadership, organisations can take the following three actions this quarter to build a foundation to expand finance functions meaningfully and strategically.

• Commission an AI readiness assessment:

  Map your current finance processes and outputs against automation potential, with clear-eyed evaluation of data quality, governance maturity, and viable business cases. This establishes where you actually are before committing to where you want to go.

• Identify a use case to formulate a Proof-of-Concept:

  Define measurable ROI targets and scalability metrics before deployment — whether the use case is expense validation, bank reconciliation, or something specific to your context. The discipline of defining success criteria upfront is itself a governance practice.

• Establish a finance AI governance working group:

  Bring finance, legal, risk, IT, and audit together early. Define human-in-the-loop standards, accountability frameworks, and escalation pathways before they are needed. This is significantly easier than retrofitting governance after deployment — and significantly less risky.

Organisations that establish these foundations early will be better positioned to realise value from AI over the long term. 

How Temus Has Built These Foundations in Practice

At Temus, we have seen firsthand how successful transformation requires more than deploying new technologies. It requires the right combination of governance, operating models, and digital foundations.

Singapore Government Sector — Multi-Year Finance Domain Transformation 

Temus was appointed as the strategic partner for an ongoing, multi-year finance transformation programme with a large Singapore government client, spanning payments and receivables modernisation, finance transformation roadmapping, and financial planning, reporting, and modelling. The governance model, accountability framework, and transformation roadmap were established before any new capability was deployed — creating the foundation that allowed new capabilities to be introduced, trusted, and scaled progressively across the agency network.

Singlife — Digital Transformation at Scale

Temus supported Singlife’s long-term digital transformation journey, spanning data unification across legacy systems, governance frameworks, automated workflows, and AI-powered performance management for financial adviser agencies. The same governance-first approach applied: the technology layer was built on a foundation of clean data architecture and clearly defined accountability, which is what made it reliable enough to act on and scalable enough to extend as the programme evolved.

While these initiatives span different sectors and use cases, they share common foundations: strong governance, scalable infrastructure, operational efficiency, and responsible technology adoption. These same foundations will be critical for finance leaders seeking to unlock sustainable value from AI.

Resaro — Governance as a Capability  

Temus’ strategic partnership with Resaro is designed specifically to address the governance layer, helping organisations build the assurance and accountability infrastructure needed to take AI from a working pilot to a trusted, enterprise-wide deployment. This is the transition that organisations most consistently underestimate, and the one that most often determines whether an AI investment delivers lasting value.

The Structural Advantage Goes to Those Who Build Now 

The organisations that gain the greatest long-term advantage from AI will not necessarily be those that adopted it earliest. They will be those that built the governance, operating models, and organisational capabilities required to scale it responsibly — and who invested in that foundation before they needed it, not in response to something going wrong. 

The institutional knowledge, data maturity, and organisational trust that come from having scaled AI well in a finance function take time to build. The organisations building them now are accumulating a structural advantage that will be genuinely difficult to replicate later, not because the technology is inaccessible, but because foundations of this kind cannot be rushed. 

For finance leaders ready to move from ambition to action, the time to build is now. 

If your organisation is evaluating how AI can strengthen decision-making across the finance function, we’d like to hear from you.