This week’s tip: 5 common missteps during AI implementation in businesses.
Companies are underwhelmed,
when they push their AI projects from prototype to production.
I've observed a common pattern:
What works in a controlled environment often struggles in the real world.
Why?
AI is a "comprehensive data product" and this is what it means:
1. Non-technical challenges dominate
About 70% of the hurdles revolve around strategy alignment, organizational restructuring, and operational integration.
2. Prototypes aren't production-ready
The remaining 30% of challenges are technical, but they reveal a crucial truth: prototypes are typically held together with the equivalent of bubble gum and duct tape.
3. AI and data strategy are inseparable
You can't treat your AI strategy as separate from your data strategy. They're intrinsically linked and must be developed in tandem for success.
4. Production demands robustness
Moving to production requires automated pipelines, feedback loops, robust DevOps practices, and the ability to handle live data streams.
5. Organizational readiness is key
Success isn't just about the technology - it's about preparing your organization for the changes AI will bring: process adjustments, and sometimes, cultural shifts.
If you're planning to move your AI projects into production:
- Take a step back and evaluate your readiness across these 5 dimensions.
It will make the difference between success and a costly disappointment.
Companies that recognize the challenges early and address them are far more likely to see returns on their AI investments.
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