🤖

AI Readiness Reality Check

Is your organization ready for AI success, or are you about to automate dysfunction?

73% of AI projects fail due to poor foundations

$2.3M average cost of failed AI implementations

67% reduction in success when processes are broken

The question isn't "Can we implement AI?" It's "Are we ready for AI to succeed?"

Project Completion Reality

How often do your projects finish as originally scoped?

Almost always (80%+) - We're disciplined finishers

Usually (60-79%) - Most projects stay on track

Sometimes (40-59%) - It's a coin flip

Rarely (20-39%) - Scope creep is normal

Almost never (<20%) - Every project becomes something else

How long does it take to make routine business decisions?

Same day - We move fast

Within a week - Normal business pace

2-3 weeks - Things take time here

Over a month - We're cautious decision-makers

We avoid making decisions - It's safer that way

Do people trust your current data enough to make important decisions?

Yes, consistently - Our data is reliable

Usually, with verification - We double-check important stuff

Sometimes, depending on source - Some data is better than others

Rarely, too many inconsistencies - We know our data has issues

No, we always triple-check everything - Our data can't be trusted

When someone says "this project is successful," do others agree on what that means?

Always - We have clear, measurable definitions

Usually - Minor interpretation differences only

Sometimes - Depends on the project and people involved

Rarely - Everyone has different definitions of success

Never - Success is purely subjective here

When you implement new tools or processes, what typically happens?

Rapid adoption with measurable improvements

Gradual adoption with some initial resistance

Mixed adoption requiring lots of training

Slow adoption with significant pushback

Tools get abandoned after initial enthusiasm

AI Readiness Score

Read: The Art of AI-ing Better →

Part of the Reality Architect toolkit by Kartik Iyer