Where Should You Start Your AI Journey?
Every organization wants to “do AI” but when everything from chatbots to predictive models is labeled “AI,” where should you actually start?
date
author
Danny Nguyen
Every organization wants to “do AI” but when everything from chatbots to predictive models is labeled “AI,” where should you actually start?
Start with your data, not your models. Before building Generative AI solutions or automating workflows, it’s essential to establish trusted enterprise data pipelines, ensure data quality, and define AI governance frameworks that keep innovation aligned with business goals and compliance standards.
Insight:
In our work at NextPhase.ai, we’ve seen that successful AI adoption follows a clear maturity curve:
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Data Foundation: Create reliable data pipelines and establish ownership and visibility across business units.
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AI Readiness: Build frameworks for governance, privacy, and model reliability which are the guardrails that enable scale.
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Value Creation: Only then introduce Generative AI and advanced analytics, focused on measurable outcomes tied to business value.
Without these foundational layers, AI projects often fail quietly; impressive demos that never make it into production because the data isn’t trusted or repeatable.
If your organization is asking “How do we start our AI journey?”, begin by asking “Can we trust our data?”
I’d love to hear where your company is on its AI maturity curve.
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