Kyle Schwietz — Founder of Uncommon Move
About

Kyle Schwietz —
AI Marketing Strategist
& Google Ads Expert.

Inside the Google Ads & YouTube ecosystem since 2008.

Nearly two decades inside the Google Ads and YouTube advertising ecosystem — not at the campaign level, but at the systems level: how Google's AI learns, what signals actually drive performance, and how execution connects to real business outcomes. That depth is where the Performance Max signal feedback loop argument comes from, and it's what the frameworks here are built on — tested in the Lab, in public.

The thinking that drives this site goes further than ad accounts. I'm interested in the bigger question most marketing conversations never reach: how do enterprise organizations become structurally more profitable through AI — not just faster at running ads? That's the conversation behind Cost Per Decision — the metric that was never measurable until agentic AI made it operable.

That means connecting organizational design, business strategy, agentic AI capability, and marketing execution into a single coherent picture. That intersection is where I spend most of my thinking time — and the case for what agentic AI actually means for marketing teams is the argument Uncommon Move is built around.

Uncommon Move publishes in two registers: thought leadership for the organizations rewriting how marketing uses AI, and working tools for the practitioners running PMax, measurement, and signal work.

Deep Expertise In
Google Ads & YouTube
Signal architecture, smart bidding, Performance Max, creative performance, full-funnel video strategy.
Agentic AI Strategy
How autonomous AI systems reshape marketing execution, org design, and business decision-making.
Enterprise Business Strategy
Connecting AI capability to organizational profitability — not just efficiency metrics.
Organizational Design
How to restructure enterprise organizations to actually operate at AI speed and scale.
AI Creative Strategy
Bridging creative thinking, AI generation, and performance signals into one loop.
Find Me
LinkedIn — @uncommonmoveai
Where I share weekly takes on AI, marketing, and enterprise strategy.
X — @uncommonmoveai
Shorter, sharper observations in real time.
The Bigger Picture

This isn't just about better marketing.

Most conversations about AI in marketing stay inside marketing. Better ads. Faster creative. Smarter targeting. That's all real — but it's a fraction of the actual opportunity.

The organizations that win over the next decade won't just have better campaigns. They'll have fundamentally restructured how they operate — with agentic AI embedded into their decision-making, their organizational design built around how AI actually works, and their financial outcomes connected directly to the signals their systems generate.

That's not a marketing conversation. That's a business architecture conversation. And most organizations aren't having it yet — which is exactly the opportunity.

"The question isn't how to use AI in your marketing. It's how to design your organization so AI makes the whole business more profitable."
Explore the thinking →
Three Levels of Impact
1
Execution

Google Ads & YouTube — working at full capability

The foundation. Getting the signals right, the creative strategy connected to performance, and Google's AI actually learning what you need it to learn. Most organizations aren't here yet.

2
Transformation

Agentic AI embedded into how work gets done

Beyond tools and prompts. AI agents that plan, execute, and iterate autonomously — reshaping workflows, team structures, and the speed at which decisions get made and acted on.

3
Architecture

The organization redesigned around profitability

The full picture. Business strategy, organizational design, AI capability, and marketing execution working as one compound system — with profitability as the north star, not efficiency.

How the thinking got built

Three moments that changed everything about how I read this market.

Eighteen years inside Google Ads is enough time to be confidently wrong about a lot of things. The frameworks on this site weren't theory before they were earned — they came out of specific accounts, specific quarters, and a few moments where the data forced me to throw out the model I was running.

The mistake

The illusion of platform efficiency vs. systemic business health.

Early in my career I treated Google Ads as an isolated efficiency engine, and I believed a plummeting Cost Per Acquisition was the ultimate marker of success. On a high-growth SaaS account, I radically over-optimized toward bottom-of-funnel brand terms and hyper-specific exact-match keywords — and drove CPAs to historic lows.

It took a brutal quarter of flatlining net-new revenue to realize I had not engineered a hyper-efficient acquisition strategy. I had built an expensive mechanism for harvesting existing demand that would have found the brand organically anyway. The dashboard was beautiful. The business was starving.

That quarter is the reason every framework on this site refuses to optimize at the campaign layer in isolation. Platform efficiency is a number. Systemic business health is the only thing that pays a P&L.

The contrarian belief

The bid is no longer on keywords. It's on data architecture.

Most practitioners still talk about Google Ads as a media-buying platform. I think that frame is already dead. In a privacy-first world, the practitioners who keep treating keywords, audiences, and creative as the controllable inputs are managing the steering wheel of a car that's already being driven by something else.

The real bid is happening upstream — on data architecture. The company with the most sophisticated first-party data infrastructure and margin-modeling will bankrupt the competitors who are still bidding on simple traffic. Not because they have better ads. Because they're feeding Google's AI a better signal, and the AI does the rest.

The shift

When Value-Based Bidding on margin rewrote my entire model.

My philosophy on market positioning shifted in a single account migration. A legacy marketplace client moved to Value-Based Bidding — but instead of using top-line revenue as the conversion value, we fed it profit margins.

Within days, the algorithm abandoned the historically high-volume keywords that had built the account. It started chasing obscure, low-volume search queries that happened to yield massive margins. The keywords I would have defended in a strategy review were the ones the machine quietly walked away from.

That moment dismantled my belief in traditional search-volume analysis. It also gave me the operating model behind the Cost Per Decision argument: Google Ads is no longer about choosing what to bid on. It's about choosing what to teach the system to value.

The principles those moments became

What drives every post and framework on this site.

One

Marketing and business strategy are the same conversation.

The divide between marketing execution and business strategy is artificial and expensive. The organizations that close that gap — by connecting signals, AI, and financial outcomes into one loop — are the ones compounding.

Two

Agentic AI requires a new organizational operating model.

When AI acts autonomously, the org chart built for human decision-making stops working. Enterprises that retrofit AI into old structures will underperform those that design from first principles around how AI actually operates.

Three

Google's AI is smarter than most teams using it.

The capability is there. The limitation is almost always the signal quality, the account structure, or the creative strategy feeding the machine. That's a human and organizational problem — and it's fixable when you see the full picture.

Where to go next

The frameworks on one side. The working tools on the other.

Thought leadership

For the organizations rewriting how marketing uses AI.

The 2026 Signal Architecture Framework — a clear-eyed view of what AI structurally changes inside the Google ecosystem, what it doesn't, and where the real margin lives.

Read the Framework →
Working tools

For the practitioners running PMax, measurement, and signal work.

The Lab and the Cost Per Decision calculator — live experiments, methods, and the math on what agentic AI does to your team's weekly capacity.

See the Lab →
Stay Sharp

Get the thinking before everyone else.

Google Ads depth. Agentic AI frameworks. Enterprise strategy that connects to profitability. When there's something worth saying.