Dec 9, 2025
Alright, I'm calling bullshit on this one.
McKinsey just dropped data that should terrify every CTO and CFO in the room: 92% of companies are investing in AI. But only 1% have actually achieved any meaningful maturity with it.
Let that sink in. Nineteen out of twenty organizations are burning cash on AI initiatives with nothing to show for it.
And here's the thing nobody wants to admit: it's not because the models are bad. AI coding tools are incredible. The LLMs are insanely good. The AI is doing exactly what it's supposed to do.
The problem is every layer above the model.
Enterprise AI adoption is failing because companies are treating AI as a tool instead of a platform. They're bolting it onto existing workflows like a feature flag, not rethinking how work actually gets done.
More specifically: In enterprise AI adoption, organizations fail not because models lack capability, but because they skip deterministic automation, operate in silos without governance, and lack clear ownership structures that connect AI initiatives to measurable business outcomes.
I see this pattern constantly. A company buys an AI license. A few power users start experimenting in Slack. Someone builds a chatbot. Another team tries an agent. Six months later, they're asking "where's the ROI?" and the answer is always the same: nowhere.
Here's why that happens:
This is the one that kills me. Before you deploy any AI, you need deterministic automation. You need to understand the process cold. What are the inputs? The decision trees? The edge cases? The rollback scenarios?
Companies skip this step because it feels boring. Unglamorous. But it's where 60% of the ROI lives.
One automation engineer on Reddit put it perfectly: "Businesses want to jump straight to AI agents. They're skipping the planning, the audits, the strategic roadmaps. They're not sharpening the axe before they swing it."
He's right. Most organizations need 60% fully automated deterministic processes, 30% AI-assisted workflows, and 10% pure AI reasoning. They're trying to do it backwards.
Marketing tries one AI tool. Engineering tries another. Sales picks a third. Finance gets left out of the conversation entirely. Six months later, nobody knows what's running where, who owns the data, or what the actual spend is.
This is where enterprises lose control. Fast.
You need governance from day one. Clear policies. Clear ownership. Clear cost tracking. Not red tape, but actual structure.
One CTO told me: "We had to fire three different AI implementations before we realized we needed a single person who owned AI strategy across the org. That one hire turned everything around."
"We're using AI" is not a metric. "We deployed an agent" is not ROI. "Our team is experimenting with ChatGPT" is just expensive Slack usage.
The winning organizations measure one thing: does this reduce cost or increase revenue? Measurably. With a number attached.
Anything else is theater.
Here's what I'm watching unfold in real-time across the community:
One developer summed it up perfectly: "AI has made starting easy and finishing nearly impossible. That final 20% is the difference between a demo and a product."
That 20% is where enterprises are actually spending money. And they're doing it wrong.
They're letting the AI hallucinate the entire system, then throwing more AI at the broken parts. More tokens. More rewrites. More agents. It's a death spiral.
The winning move is different: AI generates the framework at lightning speed. But the last mile—taste, context, accountability, real-time collaboration, data integration—those require human judgment and stable infrastructure.
This is exactly why we built Weavy.
Enterprises were doing exactly this: spinning up AI agents to build chat systems, notification layers, file sharing, activity feeds, comment threads. All the things that make a product actually usable. And they were rebuilding these the same broken way every single time.
So instead of asking "how do I make AI write this better," we asked "why are we letting AI reinvent this at all?"
Drop-in collaboration components like Weavy handle the boring stuff—the deterministic, battle-tested stuff—so your team can focus on what actually needs AI reasoning. Chat, files, feeds, real-time features, AI context layers. Weavy's collaboration layer handles the plumbing so you don't burn tokens rebuilding it.
The math is brutal: the last 20% of a feature can consume 40% of your budget. AI doesn't fix that. Pre-built infrastructure does.
The 1% that actually has maturity? They all follow the same playbook:
1. They identified the deterministic baseline (60% fully automated before AI touches it)
2. They installed clear governance (one owner, clear budget, measurable outcomes)
3. They integrated with existing systems (not parallel universes, actual integration)
4. They used pre-built components for the boring stuff (not building chat from scratch, not rebuilding auth, not recreating notifications)
5. They measured ruthlessly (cost reduction, revenue increase, nothing else)
6. They iterated on what worked instead of chasing new models
That's it. No magic. No breakthrough architecture. Just discipline.
The companies that are still in the 92%? They skipped steps 1-4 and jumped straight to "let's build an AI agent."
If you're an engineer or leader reading this:
Stop asking "how can we use more AI?" Start asking "what are we actually trying to change about our business, and is AI the right tool?"
If it is: build deterministic first. Get governance in place. Don't let teams spawn AI experiments. Integrate with real systems. Measure real outcomes.
And for the parts that everyone rebuilds the same broken way—collaboration, real-time features, state management—use pre-built infrastructure. You're not losing points for code written by someone else. You're saving a year of debugging.
AI is brilliant right up until it isn't. Your job is knowing which side of that line you're on.
Weavy is the collaboration layer that lets AI build the easy 80 percent while you avoid the expensive 20 percent.
Finish what your AI starts.
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