In a recent LinkedIn post, Nick Curum discusses a common pitfall organizations face when adopting artificial intelligence: getting stuck in pilot mode rather than achieving widespread integration. Curum challenges the notion that companies are lagging in AI development, suggesting instead that they are failing to move past the initial testing phases.
Citing new Gallup data, Curum highlights a significant gap between AI experimentation and daily usage. According to the data, while 46% of US employees have tried AI, only 12% use it daily. This discrepancy, Curum points out, is particularly evident across different sectors, with tech teams utilizing AI daily 31% of the time compared to manufacturing at 9%.
The Pilot Paradox: Testing vs. Integration
Curum argues that the core issue lies in how companies approach AI integration. Many organizations, he explains, attempt to bolt AI onto existing processes and expect behavioral changes to follow. However, this approach often fails because employees default to their established workflows.
“Most companies bolt AI onto existing processes and hope behaviour changes. It does not. People default to the old way of working.”
This inertia, according to Curum, prevents AI from becoming a truly embedded part of daily operations. He contrasts this with the approach taken by successful tech teams.
Rebuilding Workflows for AI Integration
The key differentiator for teams achieving high daily AI usage, as Curum details, is not just implementing new tools but fundamentally redesigning workflows around AI capabilities. This involves a more profound restructuring than simply adding AI to existing steps.
Redefining Processes for Efficiency
Curum provides a compelling example from the manufacturing sector. He recounts how one team mapped a quality reporting process that initially required 90 minutes of manual work per week. By rebuilding this workflow with AI over three days, the process was streamlined to take only 12 minutes.
“That is 68 hours saved per person per year.”
This dramatic efficiency gain, saving individuals approximately 68 hours annually, underscores Curum’s point. He emphasizes that the goal of daily AI use isn’t perfection but demonstrable improvement over existing manual processes.
Moving Beyond Testing: A Call to Action
As companies continue to navigate the complexities of AI adoption, Curum urges a shift in strategy. He advises against prolonged testing phases and advocates for a focus on redesigning work itself.
“If you are still running pilots, this is the moment to stop testing tools and start redesigning work.”
To foster genuine AI integration, Curum suggests a practical approach: identify processes that are time-consuming and inefficient under current manual systems. According to Curum, these are the prime candidates for AI-driven transformation and the starting point for achieving consistent daily AI usage.
In Curum’s view, the path to successful AI adoption hinges on a willingness to re-engineer how work gets done, rather than expecting new technology to seamlessly fit into old structures. This proactive redesign, he concludes, is what truly unlocks the potential of AI within an organization.
📝 About This Content
This article is based on insights shared by Nick Curum on LinkedIn.
📅 Originally posted on January 28, 2026 | View original post on LinkedIn →