Polymath Michael Polanyi famously noted, “We can know more than we can tell,” a paradox that’s now showing up and being solved on the warehouse and retail floor.
Frontline workers possess deep, tacit knowledge of how operations run, mastering practical workarounds, managing exceptions and localized efficiencies, and situational problem-solving. This knowledge never makes it into an official manual, yet real-life experience and multiple academic studies say that up to 85% of company knowledge is tacit.
VP for Automation, EMEA at Zebra Technologies.
Because this knowledge relies on muscle memory, experience, ad hoc conversations during lunch and notes, traditional top-down standard operating procedures (SOPs) inherently fail to capture it. Tacit knowledge enables employee competence, autonomy and job satisfaction, but can be unevenly distributed or lead to gatekeeping to the detriment of others. And when experienced or long-serving employees retire or move to a new company, that tacit knowledge is lost.
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For years, businesses assumed their SOPs were rock-solid, but the rollout of frontline AI tools, such as AI knowledge agents trained on SOPs and other workforce onboarding and training materials, is bringing long-overlooked knowledge gaps and outdated guidelines to the surface.
Agents are ushering in a quiet evolution in how enterprises capture institutional knowledge and build connected frontline onboarding and training programs. It comes as workplace culture, learning and development take on new urgency with organizations increasingly understanding their importance for workforce AI transformation.
The new knowledge economy
Instead of relying on centralized teams to write SOPs, the new connected frontline knowledge economy is going bottom-up by leveraging AI tools to crowdsource the “corporate brain,” with SOPs acting as the “nervous system” where knowledge is a two-way street.
By empowering the people stacking shelves, processing orders, talking to customers, and running the warehouses to flag inconsistencies, gaps and workarounds, companies are initiating a proactive audit and refresh of their workforce operations data quality.
Workers can actively signal knowledge gaps through their interactions with AI assistants, and those tools are routing the signals into dynamic, intelligently automated updates for training programs and SOPs. Workforce and operations leaders are finally able to turn undocumented, implicit frontline expertise into structured, actionable micro-learning, better procedures, and shift from one-off onboarding to ongoing everboarding.
When frontline contributions are recognized and shared, the workforce becomes better equipped to serve today’s highly informed customers, whether they are stepping into a shop or making an online purchase.
As David Autor, an MIT economist and author of a widely cited paper on the Polanyi paradox and employment said, “The tasks that have proved most vexing to automate are those demanding flexibility, judgment, and common sense—skills that we understand only tacitly…tasks that cannot be substituted by computerization are generally complemented by it.”
AI with all the senses
Organizations can and should move beyond text heavy documents and process guidance. Broadly speaking, learning and development departments already adopt a mix of text, images, videos, and group discussion. This approach reflects how we learn and should be the preferred method when it comes to employee onboarding, everboarding, and capturing implicit knowledge.
Modern AI agents are multimodal, meaning they can receive and output intelligence via audio, text, image, and video. A frontline worker should be able to verbally highlight a gap, suggest an update or snap a photo of a shelf with the AI agent comparing it to the visual merchandising planogram and provide step-by-step corrective instructions.
And with smaller models, on-device inference is possible, meaning no data leaves the device or company network. Latency is eliminated, an additional layer of security is added, and workers and customers get the intelligence they need in the moment.
Moving from knowing to doing
The development, implementation, and ongoing guidance agents require cannot be done alone, in the same way that text heavy, top-down SOPs are no longer sufficient. The top-down methods that brought organizations to this point will not sustain them for tomorrow.
According to a report by MIT, businesses that partner with AI providers have an AI success rate of 66% compared to those who prefer internal development alone (33%). The best way to succeed is to move forward with partners who have industry expertise, AI models, agents, and developer building blocks. They can support the design, configuration, onboarding, and management of AI systems in retail, warehouse, and logistics settings.
And AI transformation cannot be done with an “IT alone” mindset. Stanford’s Digital Lab finds that a top factor (43%) for AI success is executive sponsorship that enables a culture of experimentation and permission to fail. AI’s value comes from the algorithms, the technology required to implement, but mostly it comes from rethinking the people and culture component.
Polanyi’s “knowing versus telling” gap has been brought to light and is finally being closed on the frontline. By crowdsourcing the corporate brain, businesses are capturing tacit knowledge and transforming it into customer and worker advantages that make work better every day.
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Jason Harvey
