Most enterprises can point to an AI pilot. Far fewer can point to a workflow where AI has permanently removed cost or unlocked a revenue line. That distance — between demonstration and production — is where the productivity gap lives, and closing it is now the more interesting engineering problem.
Why growth markets can move faster
Two structural advantages matter. First, fewer legacy systems: a bank that digitised in the last decade has cleaner data and a shorter integration path than one carrying forty years of mainframe accretion. Second, an acute labour-cost and capacity constraint in specialised functions, which raises the marginal value of automation.
The counterweight is compute access, data-residency rules and specialised talent retention. These are solvable, but they are procurement and policy problems, not model problems.
The questions the Technology & AI panel will ask
What has actually shipped? What did it replace? What did compliance require before it could go live? Those three questions separate a working deployment from a press release, and they frame the main-stage technology programming in September.
- AI
- Technology
- Enterprise
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