🥛 The investor’s guide to AI adoption 🤖

GM. Welcome to Milk Road AI PRO, where we decode what AI is doing next and what that means for our portfolio.

In this edition, I'm going to map the 3 adoption layers of AI, score every major industry based on their actual ROI and, show you where the early winners will emerge in each sector.

This is for Milk Road AI PRO and PRO ALL ACCESS members only. Plus in the coming weeks, ALL ACCESS members will be getting our first ever Milk Road AI Portfolio picks, so stay tuned.

Our first report covered the fact that AI’s biggest problem isn’t intelligence - it’s power and physical infrastructure. Think, cables, cooling or transformers and switchgear.

Our second report detailed how AI will roll out in three waves:

• Wave 1: Data centers.
• Wave 2: AI (edge) devices like phones and laptops.
• Wave 3: AI robots (fleets) like robotaxis or humanoid robots.

Now comes the part that everyone skips in AI Twitter threads: Who actually buys this stuff?

Because AI doesn’t change the world when a model gets smarter.

It changes the world when a CFO sees a clear ROI on the budget line that reads “AI”.

Right now, we’re transitioning from AI hype to real adoption layers across industries. From ‘cool tech’ to ‘cool, our costs just dropped’.

And in this report, we’ll map which sectors will adopt first, which ones will drag their feet, and which ones will wake up in 2028 like: Wait a minute, the robots already took the night shift?

Here’s what we’re covering today:

• The industry impact score: A simple way to score which industries adopt AI and why.
• AI adoption layers: How AI moves from LLM copilots, to agents, to machines.
• The industry adoption ranking: The early adopters, the slow movers, and the “oh no” sectors.
• And finally, the investor takeaway: What the industry rankings mean for our investments.

WHAT IS THE “IMPACT SCORE”?

Most people talk about “AI adoption” like it’s a belief system.

It’s not. It’s actually a buying decision.

Industries don’t adopt “AI”. They adopt products and software that fit their budget and workflow.

So, I built a simple industry impact score formula that can be used to predict who adopts AI first:

Source: Gemini Nano-Banana

First off, yes, the math is on purpose. If either one of the multipliers is weak, the whole thing collapses:

• Huge ROI but zero readiness = PowerPoint pilots forever.
• Tiny ROI but huge readiness = cool demo but no budget.

The multiplication forces a simple truth: Real adoption requires both a reason to buy AI products and the ability to deploy them.

We divide by friction because it doesn’t stop you. It slows you down.

Think of regulation, safety, procurement, legacy systems, culture, etc. as anchors slowing adoption.

These don’t kill AI. They turn “this quarter” into “next fiscal year”.

So, more friction = slower adoption, even if ROI and readiness look great.

THE 3 BUCKETS THAT DETERMINE AI ADOPTION SPEED

Let’s dive into the 3 elements of the formula.

Importantly, these aren’t company KPIs. They’re an industry default setting.

Some industries are born digital and standardized. Others are messy, physical, regulated and allergic to change.

That’s what we’re measuring.

1. AI ROI

What it means: How much economic value can AI create?

Basically, it’s about how much of the industry’s cost and time sits in tasks AI is good at.

The ROI question: In this industry, does AI meaningfully remove labor, reduce errors, or speed up throughput at scale?

Source: Gemini Nano-Banana

2. AI readiness

What it means: How “AI-pluggable” the industry is by default.

Not whether one best-in-class player is ready, but whether the average company can deploy AI without rebuilding everything first.

The readiness question: Does this industry generally run on digital data, repeatable workflows, and systems that can be connected to new tools?

Source: Gemini Nano-Banana

3. AI friction

What it means: Everything that slows adoption across the industry, even if ROI and readiness look good.

Friction is what takes a great pilot and turns it into a project that gets stuck in reviews for 18 months.

The friction question: In this industry, how many rules, risks, and stakeholders slow AI from pilot to production?

Source: Gemini Nano-Banana

Uh, Oh… 😧 The rest of this report is exclusive to AI PRO & PRO All Access members!

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WHAT’S LEFT INSIDE? 👀

• The AI adoption layers and what gets deployed first, second, and last.
• The full, tiered industry leaderboard, plus how it affects the global AI rollout.
• The investor cheat sheet, who gets AI upside first, who lags, and where the biggest ROI pools are hiding.

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