Why AI Projects Fail Before They Start
I watched a company deploy AI to find leads similar to their existing clients. The AI delivered results. Wrong results.
The system could only work with complete client data. Since most client records had gaps, it created lead profiles based on the minority of clients with full information.
The AI amplified existing data problems into expensive mistakes.
The Real Problem With AI Implementation
Most companies rush to AI without fixing their foundation. 85% of AI projects fail, with data quality accounting for 43% of those failures.
AI needs clean data, clear insights, and well-defined KPIs. Without these aligned before implementation, results get muddy and skew toward whatever data the model finds most reliable.
This creates a dangerous feedback loop. Bad data trains AI to make bad decisions, which generates more bad data.
RevOps as Orchestrator, Not Executioner
Revenue Operations solves this by acting as the orchestrator, not the executioner. A thorough RevOps audit identifies data problems and suggests ways to enrich missing information.
The process puts systems in place across marketing, sales, and support to gather data at the right time by the right team. Failsafe processes, often AI-driven themselves, flag missing data to the correct team.
You’re using AI to prepare for AI. This creates a different foundation compared to organizations that skip the audit and try to clean data while implementing AI solutions.
The Attribution Chaos Nobody Talks About
Attribution represents the hardest challenge for most B2B SaaS companies. Multiple campaigns from marketing and sales create complexity. Customer success compensation adds another layer of confusion.
Less than 25% of B2B marketers feel confident they’re measuring performance correctly. Most companies either use the wrong revenue attribution model or use none at all.
I sequence this by identifying issues and fixing underlying systems first. Then I deploy AI to analyze and create attribution models based on the weights each company uses.
Business Model Determines Everything
The weights matter more than most realize. A referral-driven B2B SaaS with low churn assigns much higher weight to sales and support relationships.
A B2B2C company where volume of inbound marketing leads dictates growth weights those touchpoints differently.
Companies implementing AI without this RevOps foundation essentially use generic attribution models that don’t match their actual revenue drivers. They run attribution models that assign incorrect values, especially when overall data remains incomplete.
The Strategic Sequence That Works
Fix the foundation first. Build attribution models on top of fixed systems rather than using AI to fix broken ones.
Companies that switch from basic attribution to proper RevOps-foundation models can scale pipeline 2.4x in two quarters while cutting low-impact spend.
RevOps creates the infrastructure. AI accelerates what you build on top.
The sequence matters because AI amplifies everything. Give it broken processes, get amplified chaos. Give it clean systems, get amplified results.
