For the last two decades, those of us designing user experiences have been faced with a choice: either personalize or automate. You could craft highly tailored, human-led experiences that couldn’t possibly scale, or create one-size-fits-all systems that ran on autopilot. But you couldn’t have both.
Personalization 1.0
I’ll cut to the chase — Personalization 1.0 was a joke.
Sure, the first email you received addressing you by name instead of “Dear Sir” was pretty neat. But then you got the one that said “Hi {{first_name}},” and the personalization wasn’t quite so magical and mysterious anymore.
Then there was the promise of marketing automation, where every prospect was supposedly going to receive “the right content at the right time.” In reality, what most of us built was a sequence of emails that got delivered without us having to hit the “send” button.
And how about the personalized onboarding experiences we were going to deliver thanks to choose-your-own-adventure flows? Those were probably an improvement over the single path — until we let the less common pathways get outdated and broken. (To all of you who chose the path less taken, sorry about that.)
The Structural Constraints of Personalization 1.0
The old systems were constrained by their own architecture. All inputs had to fit neatly into predefined fields, and every decision tree had to be hand-built, updated, and maintained. It was a step in the right direction, but we were kidding ourselves if we thought we were delivering a truly personalized experience at scale.
In reality, we were executing segmentation, not personalization. There was no way to tailor the experience to every unique visitor, but we could define a handful of segments and customize those interactions. It wasn’t easy, but we could (usually) send the SMB prospects in one direction and the enterprise folks in another, and we could (often) send the brand-new visitor different content than the long-time customer.
Well, the days of Personalization 1.0 are over. And — you guessed it — AI has something to do with it.
The Breakthrough: AI Turns “vs” into “and”
Now, the walls are coming down.
AI has finally made it possible to bring personalization and automation together — not as competing goals, but as complementary ones. You can have your cake and eat it too.
Large language models (LLMs) bring two capabilities that change everything:
- Semantic Understanding:
AI can actually understand human language — the intent, emotion, and context behind it. Users can share all of the nuance of their situation, and our systems can now understand it and act accordingly. - Reasoning and Logic:
AI doesn’t just retrieve responses; it applies reasoning to decide what makes sense. It can adapt tone, prioritize next steps, and personalize outcomes in real time.
Together, these abilities mean we no longer have to predefine every possible scenario (and in some cases, any scenario at all). We can create systems that learn, adapt, and reason like a great employee — but scale like software.
In other words, our instructions have gone from "take this set of predefined inputs and do one of these predefined actions" to "take this messy context and do the most appropriate thing."
What This Means for CX and Marketing Teams
This shift isn’t just a new feature set — it’s a new mindset. It changes how we think about every customer touchpoint:
- Conversations replace forms. Collect context naturally, not manually.
- Understanding replaces segmentation. Move from static cohorts to dynamic, individualized experiences.
- Coherence replaces consistency. Don’t just repeat a message — reason through what’s right now.
Automation no longer dilutes the human touch. It enables it.
That’s the future we’re building toward at Remark: AI systems that extend human-level care across every interaction, not replace it.
How to Build “Personal Automation” in Practice
Here’s how forward-looking teams are doing it today:
- Design for open-ended input. Let users express themselves freely through chat, email, or voice, then use AI to parse and understand that input.
- Capture meaning, not just data. Use natural language understanding to infer needs, preferences, and emotions.
- Deploy adaptive reasoning. Let the AI decide how to respond, not just which template to use.
- Train continuously. Feed the system outcomes, learn from every conversation, and refine performance over time.
- Measure experience quality. Blend automation metrics (speed, deflection) with experience metrics (CSAT, sentiment, resolution confidence).
From Tradeoff to Advantage
The old tension between personalization and automation was never strategic — it was technical. That limitation is gone. The question is no longer “Which should we prioritize?” but “How do we combine them to create something better?”
At Remark, we believe the answer lies in building systems that don’t just respond faster, but respond smarter. AI gives us the ability to scale understanding, and when you can do that, you can deliver experiences that feel one-to-one, even when they’re built for many.
Personalization and automation are no longer at odds. They’re finally in harmony, and the brands that embrace both will define the next era of customer experience.
Welcome to Personalization 2.0.
Share
.png)
