3 Conditions for a Successful AI Implementation
AI has outsized returns under specific conditions. Here's what separates the wins from the expensive experiments.
Everyone’s trying to use AI. Most are disappointed.
The problem isn’t the technology. It’s deployment. After building AI into dozens of workflows, I’ve noticed a pattern. The wins share three conditions. When all three are present, AI delivers outsized returns. When any is missing, you get expensive experiments.
1. Replace Something You’ve Done a Thousand Times
AI works best when you already know exactly what good output looks like.
This sounds obvious, but most people skip it. They throw vague prompts at AI and wonder why the results are mediocre. “Give me a good client persona report” produces generic garbage. Specifying the exact sections, formats, and constraints you’ve used in a hundred manual reports produces reliable output.
→ Generic output, missing key sections, inconsistent depth
→ Matches your manual template, every time
The specificity comes from experience. If you’ve written a hundred client personas, you know exactly which sections matter and what level of detail works. You can prompt for that. If you’ve never done the task yourself, you can’t judge whether AI did it well.
The rule: Automate what you’ve mastered, not what you’re figuring out.
2. Pair AI with Deterministic Automation
AI alone is unreliable. AI inside a well-structured system is powerful.
The key is context. When AI receives rich, consistent inputs assembled by deterministic automation, it performs dramatically better than when it’s working from scratch.
Step 2 (AI): Score candidate fit against each requirement criteria
Step 3 (Deterministic): Apply weights, calculate final score, route to hiring manager
The automation handles data gathering reliably. AI handles interpretation. Neither could do the other’s job well.
This is why “just ask ChatGPT” rarely scales. There’s no system feeding it consistent context. You’re manually assembling inputs every time, and the quality varies.
The rule: Use automation to build context, AI to interpret it.
3. Don’t Overcommit to AI
The systems that work have human checkpoints built in.
You have three options: manual prompting (slow), fully autonomous AI (risky), or human-in-the-loop (fast and safe). Most people try the first two and miss the third.
2. Paste product details
3. Ask for ad copy
4. Review, ask for changes
5. Copy to ad platform
6. Repeat for each ad
2. AI posts directly
3. No review
4. Off-brand copy goes live
5. Errors reach customers
6. Trust erodes fast
2. See draft in queue
3. Click Approve
(or quick edit)
4. Auto-posts on approve
5. Done in minutes
The best AI implementations feel like autocomplete, not conversation. The system does the work. You confirm or adjust. No context-switching required.
This also builds trust gradually. When you review AI outputs daily, you learn its failure modes. You tune the prompts. Over time, you might remove checkpoints for tasks where accuracy is proven. But you start with humans in the loop.
The rule: Build AI into the workflow, not beside it.
The Pattern
Every successful AI deployment I’ve built follows this:
- Known task → I’ve done it manually, I know what good looks like
- Rich context → Automation assembles inputs before AI touches them
- Human checkpoint → Approve/edit interface, not chat back-and-forth
When all three are present, AI saves hours daily. When any is missing, it’s a toy.
For a real example of this pattern in action, see how we built automated lead qualification with AI scoring—deterministic data gathering, AI interpretation, human review at the end.
One exception: conversational AI is genuinely useful for learning new topics. Exploring ideas, asking follow-up questions, building understanding. But that’s personal development, not business automation. Different use case, different rules.
Written by
Eduardo Chavez
Director, Costanera