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:
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
of agentic AI projects will be canceled by the end of 2027, Gartner predicts.
Gartner, press release, June 25, 2025
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:
- Real screenshots of the product, not illustrations of what it might look like.
- The full workflow activity library, category by category — down to the 34 activities in one connector's libraries.
- Complete connector directories — the Integration Connector Directory and the MCP Connector Directory — so you can check for your systems in seconds.
- White papers that explain how we govern AI, from GuardRailed Design to Contain the Model.
- Product comparisons and interoperability pages that give other platforms fair credit for what they do well — because an honest comparison is more useful to you than a flattering one.
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:
- Can it reach your real data — reading and writing, not just reading? Data is where AI projects most often break down. See how appRules handles enterprise data integration.
- Does it govern what the AI is allowed to do? Inadequate risk controls are a leading reason projects get canceled. See GuardRailed Automation.
- Does it catch errors before they're built — and before they reach your systems? Read GuardRailed Design for AI Workflows.
- Can you verify AI answers instead of trusting a single model? See Model Cross-Validation.
- Is there a complete, tamper-proof record of every run? See Logging & Statistics.
- Will it run reliably at production scale — not just in a pilot? See Large Scale & Long-Running Jobs.
- Does it work with the tools you already use? See Comprehensive Microsoft Integration and our product comparisons and interoperability pages.
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:
- Search+ — at the top of the page, with one-click topic pills for the subjects people ask about most.
- The Highlights menu — jump straight to the major feature areas.
- The Resources menu — white papers, product comparisons and interoperability, and this blog.
- Your AI assistant — we publish a complete, machine-readable summary of the platform at apprules.ai/llms-full.txt, so assistants can answer your questions about appRules accurately.
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.
- RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed (RR-A2680-1), August 2024.
- Gartner, "Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027", June 25, 2025.
- MIT NANDA, The GenAI Divide: State of AI in Business 2025, as reported in "MIT report: 95% of generative AI pilots at companies are failing", Fortune, August 18, 2025.
Start Digging
Explore the platform, read the white papers, or compare appRules with the tools you're considering.