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Why the appRules.ai Website Is So Big

It's on purpose. Choosing an AI automation platform is hard, and the stakes are high — so we show you everything.
By the appStrategy Team

Yes, It's Big

If you've spent any time on apprules.ai, you've probably noticed: there's a lot here. Visitors tell us so all the time. Dozens of feature sections, white papers, product comparisons, full connector directories, real screenshots, and a workflow activity library that lists activities by name. And there's more to come: as people keep sending us questions and suggestions, we keep adding the answers.

That's not an accident, and it's not a website that got away from us. It's a deliberate choice — and this post explains why.

Choosing an AI Automation Platform Is Hard — and the Stakes Are High

Enterprise AI is moving fast, and the results so far are sobering. Three widely cited studies make the point:

80%+

of AI projects fail, by some estimates — twice the rate of IT projects that don't involve AI.

RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, August 2024

40%+

of agentic AI projects will be canceled by the end of 2027, Gartner predicts.

Gartner, press release, June 25, 2025

~5%

of enterprise generative AI pilots achieve rapid revenue acceleration; most stall with little or no measurable P&L impact.

MIT NANDA, The GenAI Divide: State of AI in Business 2025, as reported by Fortune, August 2025

These figures measure different things — an estimate of project failure, a forecast of cancellations, and pilots without measurable financial impact — so they shouldn't be added together or compared directly. But they point the same way.

Gartner adds one more finding that matters to anyone shortlisting vendors: it estimates that only about 130 of the thousands of agentic AI vendors are real, and warns of "agent washing" — relabeling existing products as agentic without real agentic capability. When the label on the box can't be trusted, what's inside the box matters more than ever.

Most Failures Aren't About the Model

The research is consistent on one point: AI projects rarely fail because the model wasn't clever enough. RAND's interviews with 65 experienced data scientists and engineers found that projects often fail because organizations lack the data an effective model needs, lack the infrastructure to manage data and deploy models, or focus on the latest technology instead of solving a real problem. Gartner points to escalating costs, unclear business value, and inadequate risk controls. And MIT found the biggest returns not in flashy front-office uses, but in back-office automation.

In other words, the things that decide whether an AI project succeeds — data, integration, governance, operations — are exactly the things a polished demo doesn't show. You only discover them by digging.

So We Show You Everything, Up Front

We'd rather you dig on our website than discover a gap after you've committed. That's why apprules.ai includes:

Every claim on the site is something you can check before you ever talk to us.

What to Look For in Any AI Automation Platform

Whether or not you choose appRules, these are the questions we'd ask of any platform — each tied to a root cause of failure:

Big, But Easy to Navigate

A big site is only useful if you can find what matters to you. A few ways to get there fast:

Our Promise

We'd rather you find the answer on our website than discover a gap after you've committed. If something you need isn't here, tell us — and we'll add it.

Sources

Start Digging

Explore the platform, read the white papers, or compare appRules with the tools you're considering.