A weak process can become faster without becoming better. Automating it may increase the speed at which its weaknesses affect customers. The case for artificial intelligence therefore begins with understanding the task, rather than celebrating the tool.
For leasing businesses, potential applications include document handling, data checks and analysis. Whether those applications create value depends on the quality of the information, the suitability of the task and the ability to verify results. Time saved is only part of the calculation.
FINMA addressed governance and risk management for artificial intelligence in its supervisory guidance 08/2024. The leadership question is straightforward: who owns an application when its recommendation is wrong, and who has the authority to respond?
Putting a person at the end of a process may not provide effective oversight. That person needs information, competence and time to challenge the output. If the workflow encourages automatic acceptance, a formal review step may offer less protection than it appears to provide.
I would start with a problem. Define the desired result, the errors that matter and the conditions requiring intervention. Compare the application with an existing process. A useful test should reveal weaknesses before they become repeated decisions affecting customers or the balance sheet.
External dependencies deserve attention too. What happens if the provider changes the model, cannot explain a result or becomes unavailable? Swiss financial businesses should understand how operations continue and which responsibilities remain inside their own organisation.
As a CEO, I can delegate tasks. I still need to ensure that the process has accountable owners and a workable response to failure. A purchase agreement with a technology provider does not resolve those management questions by itself.
The strongest case for adoption includes an account of its limits.
Which AI supported decision could you explain and correct today if challenged?
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