Internal processes should enable not hinder AI projects
Too often financial institutions are hamstrung by internal processes which aim to control how projects are executed, but more frequently place blockers in the way of progress, particularly where new technologies are concerned.
With the advent of AI and other complementary technologies – cloud computing, big data analytics – it’s vitally important that governance, procurement and legal processes are enablers rather than hindrances.
We’ve seen first-hand where process has stepped in the way of progress, and some of the measures that firms have taken to make positive changes.
- Vendor Onboarding: Procurement teams within banks are often criticised for their bureaucratic, burdensome processes. To enable projects to proceed at pace, the organisation needs to come together to find a way to streamline the onboarding process in the most efficient and controlled manner. This is especially important where less established start-ups offering innovative solutions are concerned.
- IT Security: The firm’s IT security policies must allow third party solutions to either be deployed internally or hosted externally and securely connected to. The process must remain aligned to the business’ risk appetite but must not act as a blocker to AI engagements.
- Data Management: In a data environment recently dominated by GDPR, there must be controls around how data is shared within and outside the organisation. When starting an AI project you need to think about the data required and whether there are going to be any issues using that data in a way that is was not initially intended for. Each project should be able to rely on a robust data governance model and the controls it imposes to prevent compliance issues from unintended consequences of data use.
Where processes are not currently conducive to AI development, a dialogue should be opened between the areas of the organisation that own or input into the process to collaboratively initiate a change. Once agreed, senior process owners should champion the changes and continually monitor their effectiveness as AI projects are embarked upon.
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