The biggest AI wins in finance aren't always the ones that make the best demo. In fact, they're often the quiet improvements. The manual checks that take less time. The financial model that gets built faster. The document review that becomes more consistent. The month-end task that no longer consumes half a day of skilled attention.
In our recent webinar on automation and AI in modern finance functions, Scott McLiver, Chief AI Officer at PwC New Zealand, described three areas where AI is delivering value today: software development, customer service, and knowledge-worker productivity.
For finance leaders, the third bucket is where the conversation becomes most relevant.
AI has already changed how software is built. McLiver noted that organisations using the most advanced tools are seeing major productivity gains in software development lifecycles. Developers are still essential, but their role is changing. Rather than writing every line manually, they are increasingly guiding AI agents, reviewing outputs, testing quality, and shaping what gets built.
For SaaS providers like LOIS, that matters because it shortens the time between idea and execution. Product improvements, testing, bug checks, and quality assurance can move faster when skilled developers are supported by the right tools.
Customer service and contact centres are another major area of AI adoption.
AI can help triage queries, draft responses, surface relevant information, and support human teams with faster access to knowledge.
Both areas are powerful. But not every finance team builds software or runs a large contact centre. That is why the broader productivity opportunity matters.
AI has already changed how finance teams operate, but not every team has the same outputs and responsibilities.
Most finance teams are made up of skilled knowledge workers. Their days are filled with judgement, analysis, review, documentation, modelling, reporting, and internal advice.
This is where AI can compound, making 10-hour tasks into two-hour tasks, or finance modelling moving from 15 hours to five. These are not necessarily full process replacements. They're productivity lifts inside work that finance teams already understand.
That distinction matters.
AI is most useful when it accelerates the work without removing the professional judgement that makes the work reliable. A model can draft, compare, summarise, structure, and test. The finance professional still reviews, validates, and decides.
Finance transformation often focuses on large systems and major projects. AI value can look different.
If one recurring task saves three hours, it may not justify a board paper. But if 40 recurring tasks each become faster, cleaner, and easier to review, the impact becomes material. That's why AI productivity can be underestimated. The gains are distributed across the entire finance function.
Month-end preparation. Lease data review. Contract checks. Supporting workpapers. Board reporting. Audit evidence. Scenario modelling. Policy interpretation. Variance commentary.
None of these tasks need to disappear for AI to be useful. They just need to become less manual and more repeatable.
AI demos are easy to make impressive. Enterprise value is harder. Finance leaders should be cautious of tools that look clever but don't integrate with controls, audit trails, data security, or existing processes.
The real opportunity is more practical: reduce manual effort, improve consistency, and help finance teams spend more time on judgement and less time on repetitive preparation.
Good use-cases are usually repeated often, rely on structured information or documents, and allow a human to review the output before it affects reporting or compliance.
That is where AI is already delivering value.
Talk to LOIS Leasing about how automation and smarter lease accounting workflows can reduce manual effort while supporting IFRS 16 and AASB 16 compliance.