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How to not fall for AI hype
Two industry leaders share their measured approach to AI adoption.
Dino Gane-Palmer
March 25, 2025 • Estimated Reading Time: 10 minutes
Hi, and happy Tuesday.
Imagine hiring 50 people a month, managing hundreds of job applications, and coordinating schedules—all without drowning in inefficiencies. Now picture a bidding process where a single miscalculation costs millions. AI is poised to revolutionize both—but how much of this is real vs. hype?
💡By the end of this post, you’ll learn about key considerations when adopting AI, such as clean data, and practical steps for adopting AI without falling for the hype.
🔥 Skip to the end if you just want to steal this week’s AI prompt.
✉️ Reply to this mail with your questions about this week’s content.
IN THE SPOTLIGHT
Imagine two drastically different scenes:
A busy logistics office, where coordinators juggle shipping schedules, fill urgent staff requests, and type endless entries into a transportation management system.
A massive steel mill, bustling with grinders and industrial furnaces, where workers apply protective coatings to keep the machinery running and reduce hazards.
These worlds intersect in the relentless pursuit of efficiency, accuracy, and staying ahead in fiercely competitive markets. In my last interview in Cabo (Mexico)☀️🕶️, I sat down with:
Danny Frisco, CEO and Co-Founder of Rapido Solutions Group, who runs a near-shore staffing agency in Mexico for U.S.-based logistics and supply chain companies.
Daniel Logan, CEO of N.A. Logan Inc., whose company specializes in industrial coatings, insulation, and leak repair for steel mills and refineries.
Their industries might look old-school on the outside, but each leader is leveraging new tech to solve real, immediate problems.
SO WHY NOT BUY EVERY AI SOLUTION ON THE MARKET?
After all, generative AI promises to answer every email and build your entire forecast at the click of a button, right?
Not so fast. These leaders agree that mindlessly slapping AI on every challenge can waste more time and money than it saves. It’s like deploying a high-end robotic vacuum on a carpet full of Legos—cool gadget, but it chokes the minute it encounters a real-world obstacle.
“AI is such a hype train, and you can get swept up. We want a tool that solves a specific problem rather than going all-in on the next shiny platform.”
“Human error is big in our bidding process, but there’s no point in a huge AI rollout if it doesn’t address that problem directly.”
We’ve all seen how easy it is to get caught up in demos that promise breakthroughs, but don’t work in practice. There’s a big question: How do you separate practical AI from the hype? That’s where this “problem-first” approach comes in, letting you see a clear path from identifying a real pain point to selecting the right solution.
DON’T FALL FOR AI HYPE—A PROBLEM-FIRST APPROACH
1. Pinpoint the Core Business Pain
Both Danny and Daniel started by clarifying exactly what was draining time and money:
For Danny’s staffing model, it was scheduling and screening hundreds of applicants each month—time-intensive tasks that begged for automation.
For Daniel’s industrial coatings bids, it was manually sifting through thousands of pages of project specs, a prime place for potential AI-driven error reduction.
“If there’s no real pain, we won’t introduce a complicated solution.”
2. Assess Potential Impact and Feasibility
They surveyed the market for AI tools that specifically addressed recruiting workflows and error-prone bidding steps. Therefore, they dodged the urge to become “bleeding edge” just for bragging rights.
3. Run a Targeted Pilot
Danny tested AI-driven scheduling software on a small subset of new hires. Daniel tried a specialized tool on a single project with complicated industrial coatings specs. They measured speed, cost savings, and user satisfaction at each step.
“We let the AI handle the routine tasks first and saw immediate relief for our recruiters.”
4. Measure, Then Decide
Both discovered tangible ROI—fewer scheduling errors for Danny’s recruiters, more accurate estimates for Daniel’s bids. That told them it was time to scale these solutions more broadly, having proven their worth in a controlled test.
YOUR AI ACTION PLAN : FROM BOTTLENECKS TO BREAKTHOUGHS
1. Identify Your Bottleneck
Look for repeated tasks that eat up hours and have clear metrics—like wait times or bid accuracy.
2. Evaluate Vendors Rigorously
Ask them: “How does your AI solve this specific pain point?” If they can’t answer, move on.
3. Pilot, Don’t Overhaul
A small-scale deployment is less risky and provides real-world data to validate the approach before wider adoption.
4. Keep Measuring
Collect metrics like reduction in errors, cost savings, or improved satisfaction. If results plateau or underperform, pivot or cut losses.
Not sure where to begin? Reply to this email to chat about your unique operations or processes.
Evaluating vendors and technology
Are you evaluating a new technology or vendor? Upload the relevant documents into your favorite AI chatbot and try this prompt:
Please evaluate the <attached documents or link> on this vendor/technology and provide questions I can ask to critique whether the solution lives up to what is marketed. Please also outline what clean data I will need to implement this solution and other steps I will need to undertake for adopting it.
Here’s highlights of what I got when I provided a link to Figure’s humanoid robot with the above prompt:
To critically assess whether Helix meets its marketed claims, consider posing the following questions:
Generalization to Unseen Objects: How effectively does Helix enable robots to identify and manipulate objects they haven't previously encountered?
Multi-Robot Collaboration: Can Helix consistently facilitate seamless collaboration between multiple robots on complex tasks?
Onboard Processing Capabilities: Given that Helix operates entirely on embedded low-power GPUs, how does it balance computational demands with real-time responsiveness?
Commercial Deployment Readiness: What real-world testing has been conducted to validate Helix's performance in commercial settings? Are there documented case studies or pilot programs demonstrating its effectiveness?
Implementing Helix requires careful preparation of clean data and adherence to specific adoption steps:
Data Collection: Gather high-quality visual and language datasets relevant to your operational environment. This includes images and descriptions of objects the robots will interact with, ensuring the data reflects real-world conditions.
Data Annotation: Accurately label collected data to facilitate effective training of Helix's vision-language models. Consistent and precise annotations are crucial for optimal performance.
Environment Preparation: Design your operational environment to accommodate the capabilities and limitations of humanoid robots. This includes organizing spaces to minimize obstacles and configuring objects to be within the robots' reach and manipulation abilities.
Pilot Testing: Conduct controlled trials to evaluate Helix's performance in your specific context.
If this newsletter was helpful, please pass it along to others in your network who might appreciate it too
Best,
Dino
From Our Interview Series with Daniel Logan and Danny Frisco
 | 🎯 What you’ll learn:
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 | 🎯 What you’ll learn:
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 | 🎯 What you’ll learn:
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