🇿🇦 English, 28 October 2026, enterprise adoption
Choosing the right AI path for banks and large enterprises
Most teams have only had broad access to modern generative AI for a couple of years, so maturity shows up as pockets of excellence rather than a consistent enterprise capability.
AI adoption inside large organisations still feels early, even though the hype is loud. In regulated industries like banking, that gap is more visible because security, compliance and governance requirements are non-negotiable. Enterprise adoption remains in its infancy, with many teams struggling to measure business value in a way that survives scrutiny from risk teams, finance and the board.
A practical way to frame AI strategy is the buy, build or boost decision: buy means off-the-shelf software or SaaS, build means custom engineering and integration, and boost is the hybrid that uses existing platforms, packages and data, then adding integrations or an abstraction layer to extend what you already own.
For big enterprises, boosting often beats launching a huge new program because there is already a large application estate with underused licences and partially adopted tools. Boosting focuses on sweating existing assets, improving efficiency and meeting new requirements without reinventing everything.