The current challenge for digital transformation leaders lies in the gap between AI experimentation and reliable production. When employees bypass established systems to adopt independent AI tools, companies face inconsistent outputs and fragmented data. Flowfinity argues that real value emerges only when AI functions within the specific context of a user's workflow, eliminating the need for constant app-switching.
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Flowfinity Targets AI Sprawl by Integrating Assistants into Workflows
As organizations struggle to convert artificial intelligence pilots into measurable gains, Vancouver-based Flowfinity is pushing a strategy to curb shadow AI. By embedding assistants directly into existing operational systems, the company aims to move beyond standalone tools that fragment data and alienate frontline teams from core business processes.
Larry Wilson, Vice President at Flowfinity, emphasizes that standalone tools rarely improve field service performance. Instead, reliability stems from placing AI at the precise moment of decision-making. By constraining AI to trusted internal data, the platform maintains the human-in-the-loop oversight necessary for operational control. This approach allows enterprises to scale automation while ensuring that AI assistants remain tethered to the actual processes that drive their daily output.
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