The Executive's Guide to AI: What Your Vendors Won't Tell You

I've sat in hundreds of AI vendor pitches. The demos are always impressive. The ROI projections are always optimistic. And about 70% of the time, the project fails to deliver.

After advising 14 Fortune 500 companies on AI strategy, I've learned to spot the patterns that separate successful AI initiatives from expensive science projects.

The Three Questions That Kill Bad AI Projects

Before you greenlight any AI initiative, ask these questions:

1. "What happens when the model is wrong?"

Every AI system makes mistakes. The question isn't whether—it's how often, and what's the cost?

A fraud detection model with 95% accuracy sounds great until you realize that 5% error rate means thousands of angry customers or millions in missed fraud. The best AI teams design for failure modes from day one.

Red flag: If your vendor can't articulate the failure modes and mitigation strategies, they haven't thought hard enough about production deployment.

2. "Show me the data pipeline, not the model"

Here's a dirty secret: the model is usually the easy part. The hard part is getting clean, reliable data to the model in production.

I've seen brilliant ML models fail because:

The training data didn't match production data
Data pipelines broke silently for weeks
Feature engineering couldn't be reproduced in real-time

Red flag: If the conversation focuses on model architecture instead of data infrastructure, you're talking to researchers, not engineers.

3. "What's the simplest solution that might work?"

The most successful AI projects I've seen started with embarrassingly simple approaches:

A logistics company reduced delivery times 12% with basic route optimization before touching ML
A retailer improved recommendations 8% by fixing their search algorithm
A bank caught 40% more fraud by adding three simple business rules

Red flag: If the proposal jumps straight to deep learning without exploring simpler alternatives, you're paying for complexity you might not need.

The Real Cost of AI Projects

Vendors quote model development costs. Here's what they leave out:

Hidden Cost	Typical Range	Why It's Missed
Data cleaning & labeling	2-5x model cost	"We assumed your data was ready"
Integration engineering	1-3x model cost	"That's your IT team's job"
Monitoring & maintenance	30-50% annually	"We'll discuss that in phase 2"
Retraining pipeline	50-100% of initial	"Models don't need updates" (they do)

A $500K AI project often becomes a $2M commitment when you account for the full lifecycle.

What Good AI Partners Do Differently

The consultancies and vendors worth working with share common traits:

They say no. The best partners turn down projects that won't deliver value. If everyone's saying yes to everything, someone's not being honest.

They start small. A 6-week proof of concept with real data beats a 6-month roadmap with PowerPoint projections.

They transfer knowledge. Your team should be able to maintain and improve the system after the engagement ends. If you're permanently dependent on the vendor, that's a feature for them, not you.

They measure business outcomes. "Model accuracy improved 3%" means nothing. "Customer churn reduced by $2M annually" means everything.

The 90-Day Litmus Test

Here's my rule of thumb: if an AI initiative can't show measurable business impact within 90 days, it's probably not ready for production.

This doesn't mean the full system ships in 90 days. It means:

Week 1-2: Validate data availability and quality
Week 3-4: Build a minimal prototype with real data
Week 5-8: Test with actual users in a controlled environment
Week 9-12: Measure impact and decide on full investment

Projects that can't hit these milestones usually have fundamental issues that more time won't solve.

Questions to Ask Your Board

If you're presenting an AI initiative to your board, be ready for these questions:

What's the baseline? How does the business metric perform today without AI?
What's the minimum viable improvement? At what point does this become worth the investment?
What's the kill criteria? Under what conditions do we stop the project?
Who owns this after launch? Which team maintains and improves the system?
What's the competitive moat? Can competitors replicate this with off-the-shelf tools?

The Bottom Line

AI is a powerful tool, but it's not magic. The executives who succeed with AI treat it like any other major technology investment: with rigorous due diligence, clear success metrics, and healthy skepticism of vendor claims.

The ones who fail treat it like a lottery ticket—hoping that enough investment will eventually pay off.

Want a second opinion on your AI strategy? We offer complimentary 30-minute strategy calls for executives evaluating AI initiatives. Schedule a conversation.