“Let Me Get Right on This”
That was my snarky headline for a keynote presentation where I shared an interesting request from a large industry trade group. For more than a decade, marketers have been chasing the mythical easy button. The fantasy goes like this:
- A single tool will analyze every page on your site.
- It will benchmark competitors in real time.
- It will tie keywords directly to revenue.
- It will sort opportunities by “most impact / least resistance.”
- And it will do it all so intuitively, you’ll never need training, never need to build new processes, and never need to justify another enterprise license fee.
Back in 2013, this fantasy sounded like “all-in-one SEO dashboards.” In 2025, it will be rebranded as “AI-powered marketing copilots.”
The dream is the same: enterprise-level insights, consumer-app simplicity, and a price tag your CFO won’t choke on.
The AMA Example
Here’s a perfect snapshot of this mindset from a real request sent maybe 10 years ago by the American Marketing Association from some of their members looking for a magical solution:
I color-coded the different elements of the request:
Features Requested in the AMA Ask
Onsite Internal Page Analysis
- Crawl and evaluate their website.
- Identify technical or structural issues.
- Ideally automatic, without much manual interpretation.
Keyword Analysis in Popular Search Engines
- Research demand and search volume.
- Benchmark current rankings vs. competitors.
Revenue Connection / Backend Integration
- Tie keywords directly to revenue.
- Prioritize terms by financial contribution.
- Automate ROI measurement.
Competitive Benchmarking
- Compare terms and categories against “the market.”
- Identify gaps and overlaps.
- Derive category strategy from competitor data.
Impact vs. Resistance Mapping
- Suggest “most impact with least resistance” opportunities.
- Automate ROI/effort tradeoffs.
All-in-One Software Solution
- Ideally one tool that does all of the above.
- Unified dashboard, minimal setup.
Ease of Use / Low Training Requirement
- “Intuitive enough” for non-specialists.
- No complex onboarding or data cleaning.
- Push-button insights.
Avoid Enterprise Tool Trap
- No enterprise pricing.
- No shelfware.
- Enterprise outcomes at DIY-tool cost.
Where the Snark Comes In
This is the perfect caricature of the “AI easy button” myth.
They want:
- Enterprise-grade insights without enterprise cost.
- Plug-and-play dashboards that magically connect to revenue.
- Competitive strategy analysis without analysts.
- Automated prioritization without judgment.
- And of course, no training required—because apparently learning is overhead.
In other words:
“Give us a crystal ball with an Excel export button. But make it cheap.”
This is exactly the kind of demand that fuels marketing copy promising “AI-driven everything.” The fantasy is that a tool can replace the hard work of analysis, tradeoffs, and organizational alignment. The reality? You just get a shinier report that nobody uses.
Parody Pitch: AI EasyRank™
Imagine the sales copy if this easy button actually existed:
Introducing AI EasyRank™ — Cosine-Powered Hreflang Harmony™!
Just push the button and let our patented AI engine:
- Crawl every page on your site.
- Rank every keyword by its revenue potential.
- Automatically identify your competitors’ winning strategies.
- Calculate the “least resistance path” to global domination.
- And map all your hreflang tags instantly with 99% accuracy.
No training required. No analysts necessary. Just pure, effortless growth.
Sounds amazing, right? Except…it doesn’t exist. It has never existed. And the more you believe it does, the more likely you are to end up with beautifully wrong outputs that make you feel confident while quietly wrecking your strategy.
Why This Is Bad
The problem with the easy button myth isn’t just that it oversells. It’s that it actively creates risk:
- Complex Problems Can’t Be Collapsed into One Click
SEO, revenue attribution, and benchmarking are messy because businesses are messy. No AI tool can flatten organizational politics or clean up your duct-taped CMS. - False Precision Is More Dangerous Than No Data
Dashboards tying “revenue potential” to keyword lists look authoritative. But if the attribution is wrong, you’re betting strategy on garbage math. - The Hard Work Gets Avoided
Buying the tool feels like solving the problem. Meanwhile, the real work of aligning DevOps, content, and product teams never happens. - Capability Erodes
By chasing “effortless AI,” organizations don’t build the internal knowledge they need. When the tool underdelivers, they’ve lost both time and muscle.
Case Study: The Similarity Paradox
Take international SEO as a concrete example. On a weekly basis for the past five years, I was asked why we did not have AI functionality built into Hreflang Builder. We would often receive outputs from developers who used AI to map their alternative pages. In a few cases, the outputs were not bad when there was consistency. Unfortunately, for most global brands, consistency across markets and products is a pipe dream.
Although I no longer own Hreflang Builder, I still receive inquiries about using Screaming Frog’s new functionality with AI connectors and cosine similarity. On paper, AI similarity analysis should make this easy. Run cosine similarity, and you’ll get precise scores showing which pages “match.” Pages that score 0.98 or 0.99 appear to be perfect alternates—done, right?
Not even close.
- Flavor Names Vary: “Peppermint” in one market is “Refreshing Mint” in another: nearly identical text, but distinct products.
- Package Sizes Differ: A 500ml bottle vs. a 16.9oz bottle—identical descriptions, different SKUs.
- Page Structures Conflict: The UK might have one master page with 30 variants hidden behind facets. Australia has 30 standalone pages. India has unique size and bundle differences layered in.
The algorithm happily declares them “the same.” However, when you put them in a table and send them to the brand for confirmation that they are alternates of each other, they become upset because, although they are very similar, they cannot and should not be considered alternates.
And here’s the kicker: even a 90%+ similarity score still requires human review. Someone has to define the rules for equivalence and manually map exceptions. Without that oversight, AI delivers high-confidence results that are catastrophically wrong.
In fifteen years of hreflang experience and over 30 years in global websites, I have only had a few companies where there was any remote consistency across markets. Especially with decentralization, there is rarely a consistent build. That Page structure reference above is ubiquitous – you have a main category in one market and individual pages in others. You are using Commerce Cloud in the US, but Adobe in the UK, and maybe Shopify in New Zealand, and no one thought to have any coordination. Yes, there is matchmatical similarity, but they are not alternatives.
Why Training Is Non-Negotiable
AI can accelerate the grunt work—scanning thousands of pages, clustering candidates, surfacing possible matches. What it can’t do is:
- Define the business rules for alternate-page equivalence.
- Catch subtle but critical product differences.
- Resolve structural mismatches across markets.
- Understand local regulatory, cultural, or contextual nuances.
Without training and oversight, AI outputs aren’t a strategy. They’re just shiny hallucinations in dashboard form.
The No Easy Button Truth
The demand for an easy button is what fuels all the “AI-driven everything” sales copy we see today. But here’s the truth:
- AI can speed things up, but it can’t decide for you.
- The messiness of global markets and organizational alignment cannot be automated away.
- Training isn’t overhead—it’s the very thing that keeps AI useful, rather than dangerous.
There is no easy button. There never has been. The closest you’ll get is a well-trained team that knows how to interrogate AI outputs, define rules, and act on the answers.
Final thought: If someone pitches you the “AI EasyRank™” version of an easy button, smile politely and walk away. Because the moment you trust the machine without judgment, you’re not automating strategy—you’re automating mistakes.
