Related article published on LinkedIn 2026-06-08. Read original →
AI adoption fails when only the tool changes and the people do not. A company cannot simply give people access to AI and expect good outcomes. Some employees will overuse it, some will underuse it, some will fear it, and some will use it carelessly.
AI readiness is not about chasing software. It is about preparing the business to use AI with purpose, discipline, and accountability. The companies that win with AI will connect the technology to real work, real people, real process, and real measurement.
Why This Matters
AI training is not just technical training. It should teach people how to think with the tool: when to use AI, when not to use it, what data is safe, what requires review, how to verify output, and how to keep human judgment in control.
This is an executive operating issue. AI can help a company move faster, but speed only matters when the direction is right. If the business problem is unclear, AI becomes noise. If the workflow is unclear, AI can automate confusion. If the data is weak, AI can create polished but unreliable output. If people are not trained, AI can increase risk instead of reducing friction.
What I Would Look For
I would start by looking at where the work slows down, where decisions repeat, where documents are recreated, where people copy information from one place to another, and where knowledge is trapped in people's heads. Then I would separate three categories: work AI can safely assist, work AI can help prepare but a human must approve, and work AI should not touch until governance is stronger.
Business Example
A dispatcher may need help drafting customer updates. A safety manager may need help creating toolbox talks. A sales rep may need follow-up emails. HR may need onboarding documents. Operations may need action plans from meeting notes. Training should be role-based so people can see how AI supports real work.
The lesson is simple: AI should support the work, not hide the problem. It should help employees organize information, communicate clearly, reduce repetitive effort, and improve execution. It should not become a substitute for leadership, judgment, or accountability.
Risk If Ignored
Without training, employees may expose confidential information, managers may approve bad output because it sounds polished, and teams may create inconsistent documents. Good people may avoid AI while careless users move ahead.
This is where many companies get AI wrong. They move too fast in the wrong places and too slowly in the places that matter. They buy tools before defining use cases. They experiment without rules. They measure activity instead of impact. That approach may look innovative, but it is not disciplined.
Action Checklist
Ask these questions before moving forward: What business problem are we solving? Who owns the workflow? What information is being used? What human review is required? What risk could be created? How will employees be trained? How will success be measured? Who is accountable if the output is wrong?
Closing Thought
AI should make businesses clearer, faster, and more disciplined. It should help people turn knowledge into action. But that only happens when companies treat AI adoption as an operating capability, not a technology trend.
I help teams build practical AI training that connects to the work people actually do - not abstract theory that gets forgotten.
Sources Consulted - Microsoft 2026 Work Trend Index: https://blogs.microsoft.com/blog/2026/05/05/how-frontier-firms-are-rebuilding-the-operating-model-for-the-age-of-ai/ - Microsoft WorkLab - Agents and human agency: https://www.microsoft.com/en-us/worklab/work-trend-index/agents-human-agency-and-the-opportunity-for-every-organization - McKinsey State of AI 2025: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai - NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework - IBM What is AI Governance: https://www.ibm.com/think/topics/ai-governance
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