Why this matters for UK business teams
Finance teams are often asked to do more with the same resources, while auditors and leadership expect stronger visibility. A recent leadership perspective on creating an AI native finance function focuses on practical steps: where automation helps, how to keep controls tight, and how to evaluate ROI in real operating terms.
Five practical lessons for adopting AI in finance
1 Automate forecasting where the workflow is stable
One of the core lessons is to identify finance tasks that lend themselves to repeatable inputs and outputs, then automate forecasting using AI driven methods. The goal is to reduce manual effort while improving speed of planning cycles, rather than treating AI as a one off experiment.
2 Build stronger controls instead of weaker processes
The guidance emphasizes that AI adoption should come with stronger controls. That includes making the process more governed as you automate, so finance can manage risk and maintain integrity of outputs.
3 Treat AI ROI as an operating metric
A practical point is to measure ROI through outcomes that matter to finance operations. That means looking at improvements in efficiency, decision support, and reliability, not just whether a model can generate text or analysis.
4 Keep the finance function AI native, not AI overlayed
The lessons describe building an AI native function, which implies redesigning workflows around AI capabilities rather than bolting AI onto existing steps. This helps ensure adoption supports day to day operations.
5 Use automation to strengthen decision cycles
Finally, the approach highlights how automation can improve the rhythm of decision making in finance. When forecasting and related tasks run faster and with clearer controls, teams can iterate planning more effectively.
Next step for adopters: pick one forecasting or planning workflow, map the risks, define the controls you will use, then measure time saved and decision quality as your first ROI target.
How to use these lessons in your own rollout
- Start with a workflow that is stable enough to automate, such as forecasting or recurring planning tasks.
- Define control points before you deploy, so governance improves alongside automation.
- Set an ROI baseline and track outcomes tied to finance operations.
- Redesign the workflow so AI is embedded in how work runs, rather than added as an extra step.
- Run a short cycle, then iterate using both operational feedback and control outcomes.