By
Rachel Daggett
Artificial intelligence is everywhere. Every week, a new platform promises faster audits, smarter outreach, or effortless insights. Yet behind the branding, most tools are indistinguishable. They rely on the same large language models, wrap the same APIs, and differ only at the margins.
That makes most of them unsustainable. These are not products. They are features—features that will either be absorbed by larger platforms or vanish entirely. For executives, the challenge is not deciding which tool to chase, but deciding what infrastructure is worth building on top of.
Despite the marketing spin, most AI tools are thin wrappers. They take the same models, bolt on APIs, and add a layer of user interface. The differences are cosmetic: a scoring system here, a dashboard there.
We’ve tested across:
The lesson is clear: switching tools may feel different, but the operational outcome is almost always the same. Competitive advantage will never come from the tool itself. It comes from the people who use it, the processes that guide it, and the discipline with which it is governed.
If you automate a weak process, you don’t fix it—you simply break things faster and at greater scale. AI accelerates whatever system it touches, for better or worse. Leaders who focus first on people and process will extract value; leaders who skip straight to technology will multiply their problems.
The AI market is not expanding in clean lanes. It is collapsing in on itself. Zapier now formats your content. Notion writes your ideas. Even advertising giants like Meta, Google, and Amazon have launched AI-powered marketing tools. Every platform is moving laterally, encroaching on its neighbors and even duplicating its own integrations.
For end users, the effect is chaos. Marketing tech stacks balloon with redundant tools, dependencies multiply, and the risk of vendor collapse grows. What looks like optionality quickly becomes fragility.
Teams should treat most AI apps as temporary scaffolding, not permanent architecture. Systems must be designed to withstand tool failure or acquisition. Otherwise, every vendor shift will force expensive rebuilds.
AI often looks inexpensive. A $0.02 audit run appears trivial until you consider what it takes to get there.
In one day, I spent $62.62 debugging and modifying a Replit workflow—burning through my entire $40 Teams-level credit and dipping into additional budget. None of that spend created new value; it was consumed entirely by QA and chasing stability.
That is the real cost pattern: experimentation, debugging, and integration headaches that compound across teams. The financial hit matters, but the bigger consequence is opportunity cost. Every hour and dollar spent patching fragile systems is an hour and dollar not spent on building capabilities that actually drive growth.
This is why you should resist measuring tools by unit cost alone. The right question is: What is the total cost of ownership, including debugging, retraining, governance, and stack maintenance? A tool that looks cheap at first can quietly become one of the most expensive in the system.
If the tools themselves are interchangeable, the value comes from how they’re applied. At their best, AI tools accelerate time-consuming manual tasks, reduce human error, and free up teams for higher-value work. They can enhance creativity by generating new ideas, improve decision-making by surfacing patterns quickly, and elevate customer experience by delivering faster, more personalized responses.
But none of that happens automatically. The difference between promise and payoff lies in how leaders design the workflows, train their teams, and enforce quality control. AI magnifies whatever foundation it sits on: good processes get faster and smarter, weak processes break more quickly and more dramatically.
That’s why the leaders who win in the AI era won’t be the ones who adopt tools the fastest. They’ll be the ones who know what not to automate, who design resilient processes, and who align technology to strategy instead of the other way around.
Most AI apps will not survive the year. Some will be acquired, others replaced by native functions in CRMs, project management platforms, or cloud suites. The true survivors will not be the apps we see marketed today, but the invisible layers—core models, orchestration systems, and governance frameworks—that power everything else.
Leaders who chase novelty will spend quarter after quarter rebuilding fragile stacks. Leaders who prioritize adopting trusted systems to support proven processes will still be ahead when the model war ends.