AI is no longer an experimental technology. It’s becoming a baseline capability for modern businesses. The real question most teams should be asking is not “should we use AI?” but “how deeply should we engage with it?”
I’ve talked to many founders, CTOs, and operators over the past couple of years. The hesitation around AI usually comes from two places:
Teams that haven’t really tried AI and feel comfortable sticking with existing workflows.
Teams that rushed into AI, spent money, got disappointing results, and walked away.
Both often conclude: “AI isn’t for us.” That conclusion is understandable — but increasingly risky.
Many organizations still rely on manual or semi-manual processes: document handling, internal knowledge search, reporting, customer support triage. Everything appears to “work,” but it’s slow, hard to scale, and dependent on headcount rather than leverage.
AI isn’t magic, but it is a force multiplier. Ignoring it means accepting structural inefficiency while competitors gradually improve speed, quality, and decision-making.
One misconception I see a lot: that engaging with AI means building custom models or hiring a large ML team. In practice, AI today is closer to what spreadsheets or search once were — general-purpose tools that most teams can benefit from without deep specialization.
Instead of treating AI adoption as a yes/no decision, it’s more useful to think in levels.
Level 1: AI literacy Every company should be here. This is about enabling people, not systems: using tools like ChatGPT for research, drafting, summarization, and analysis; teaching teams how to verify outputs; and setting clear rules around sensitive data. Low risk, high return.
Level 2: AI-assisted workflows Here AI becomes part of everyday processes without replacing humans. Examples include internal AI assistants over documentation, AI-supported customer support, content generation, or analytics help. This is where many teams see the best ROI with relatively low complexity.
Level 3: AI-driven systems At this level, AI is embedded into products or core operations: RAG systems, agent workflows, forecasting, personalization. This requires clean data, evaluation, and operational discipline. Many failures happen here not because AI doesn’t work, but because teams skip the earlier foundations.
The biggest risk isn’t “doing AI wrong.” It’s not building AI fluency at all while the rest of the market moves forward.
Once AI systems are in production, new problems appear: cost control, reliability, hallucinations, latency, silent regressions. At that point, AI stops being a demo and becomes infrastructure.
For teams already dealing with production AI systems, we’ve been thinking a lot about observability and reliability in this space. Some of that work is shared here: https://optyxstack.com/ai
Curious how others on HN think about the “depth” question when it comes to AI adoption.
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