Some AI decisions are hard to make. This one is not.
Before finance teams commit to major AI builds or long transformation programmes, there's a simpler step that creates value and improves every later decision: get enterprise-grade frontier AI into the hands of your people, then train them properly.
In our recent webinar on automation and AI in modern finance functions, Scott McLiver, Chief AI Officer at PwC New Zealand, described this as the "no-regret" move for finance leaders.
It's practical, relatively low-commitment, and helps finance teams understand where AI can genuinely improve the way they work.
Many organisations are trying to choose the perfect AI project before their people have real fluency in the tools. That's risky.
In finance terms, leaders are being asked to make decisions about AI investment, risk, and process redesign before they've seen what modern tools can do in their own work. That leads to guesswork.
The better sequence is capability first. Give people access to enterprise-grade tools, build fluency and let them test the technology against real finance tasks, then decide where larger investment makes sense.
Building the capability of your finance team with frontier AI tools is a critical step in embedding responsible, effective use of AI in your business.
Proper upskilling takes more than a 45-minute session that people are half-heartedly paying attention to. Finance professionals don't become power users by watching someone else demonstrate AI. They need to use the tools, test prompts, challenge outputs, and understand the boundaries.
That means laptops-open training.
The goal is not to turn every accountant into a technologist. It's to help skilled knowledge workers accelerate work they already understand.
That might include drafting variance commentary, reviewing contracts, preparing workpapers, or building a human-led agent for a recurring task. That's not something you can train for in a single 45-minute session.
For business use, finance teams should be working with enterprise-grade versions of AI tools, not consumer versions. The data security, legal protections, and administrative controls are different.
That matters in finance, where teams handle commercially sensitive information, lease data, contracts, and audit evidence.
The practical options may include Copilot, ChatGPT Enterprise, Claude, or another enterprise-approved tool. The exact choice is less important than starting safely and intentionally.
Once finance teams understand the tools, the quality of AI decision-making improves.
People stop chasing generic ideas and start identifying specific, high-value tasks. They can see which processes are repetitive enough to support an agent, where human review is required, and where an enterprise SaaS provider should embed the capability.
This is where the no-regret move becomes strategic. Upskilling doesn't just create productivity gains. It creates better judgement.
The pace of AI development also changes the investment case.
If a finance team spends heavily building a narrow AI tool, that capability may be absorbed into a frontier model or enterprise platform within months. That doesn't mean organisations should never build. It means they should build only where the case is strong.
For many finance teams, the first investment should be in their people.
The most useful AI strategy is not the most ambitious one. It's the one that helps finance teams move safely, learn quickly, and apply the technology to real work. That starts with enterprise-grade tools, practical training, clear guardrails, and human-led adoption. From there, finance leaders can make better decisions about automation, agentic workflows, SaaS capabilities, and larger transformation priorities.
Capability first. Investment second. That's the no-regret move.
Find out more about how LOIS Leasing helps finance teams reduce manual work, improve control, and build more confidence in their lease accounting processes today.