Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I made a decision that probably saved my business from becoming another AI failure statistic. After watching the AI hype cycle for two years, I decided to deliberately avoid AI consultants during the peak frenzy.
While everyone was rushing to ChatGPT and hiring "AI experts" who'd learned prompt engineering last Tuesday, I took a contrarian approach. I waited. I watched. I learned from others' expensive mistakes.
Six months ago, I finally dove in—but with a completely different strategy than what the industry was preaching. Instead of hiring consultants, I approached AI like a scientist, not a fanboy. The result? I discovered that most AI consulting is either overpriced or completely unnecessary.
Here's what most founders don't realize: the best AI implementations come from understanding your business first, not the technology. After testing multiple approaches and watching dozens of startup AI projects succeed and fail, I've developed a framework that actually works.
In this playbook, you'll learn:
Why most AI consultants are selling solutions to problems you don't have
The 3-question framework I use to evaluate any AI consultant
How to identify the 20% of AI capabilities that deliver 80% of the value
My step-by-step process for testing AI solutions before spending big money
Real warning signs that separate legitimate AI experts from hype merchants
If you're considering AI consulting, read this first. It might save you from a very expensive education.
Industry Reality
What every startup founder keeps hearing about AI consulting
Walk into any startup accelerator or scroll through LinkedIn, and you'll hear the same AI consulting narrative repeated everywhere:
"AI will transform your business, but you need experts to implement it properly." The industry has created a perfect storm of FOMO and complexity that makes every founder feel like they need professional help.
Here's what the conventional wisdom looks like:
Hire AI consultants early - Get experts before your competitors do
Implement AI everywhere - Transform every business process with machine learning
Build custom AI solutions - Generic tools won't give you competitive advantage
Start with big projects - Go comprehensive to see real ROI
Focus on cutting-edge tech - Use the latest models and techniques
This advice exists because there's a massive gold rush happening. Everyone wants to be an AI consultant because the market is paying premium prices for anything with "AI" in the title.
The problem? Most of these consultants learned AI six months ago and are selling theoretical knowledge rather than practical business experience. They're pattern-matching from blog posts and applying generic frameworks to unique business problems.
Here's where conventional wisdom falls short: it treats AI as a technology problem when it's actually a business problem. Most startups don't need better AI—they need better understanding of what problems AI can actually solve for them.
The result is predictable: expensive projects that deliver impressive demos but zero business impact. I've watched this pattern repeat across dozens of startups, and it's time for a different approach.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
My wake-up call came when I watched three different startup founders in my network spend a combined $200K on AI consulting projects. All three had the same result: beautiful presentations, zero business impact, and a lot of expensive lessons.
The first founder hired a "machine learning expert" to build a predictive analytics dashboard. Six months and $80K later, they had a system that could predict customer churn with 89% accuracy—but couldn't actually prevent it. The consultant built exactly what was asked for, but no one had asked the right questions.
The second startup brought in an AI agency to "revolutionize their customer service." They got a sophisticated chatbot that could handle complex conversations but frustrated customers because it couldn't actually solve their problems. Another $70K down the drain.
The third case was the most telling. A SaaS founder hired consultants to "implement AI across the entire business." The result was five different AI tools that didn't talk to each other, confused the team, and added complexity without adding value.
That's when I realized something important: these weren't bad consultants—they were good consultants solving the wrong problems.
Meanwhile, I was taking a completely different approach. Instead of hiring experts, I spent six months doing something most founders won't do: I learned enough about AI to know what questions to ask.
I wasn't trying to become an AI expert. I was trying to become an informed buyer. The difference is huge.
During this period, I saw the other side of the story. Startups that were succeeding with AI weren't using expensive consultants—they were using simple, focused implementations that solved specific business problems. They started small, learned fast, and scaled what worked.
This observation led me to develop what I call the "AI Reality Check Framework"—a way to evaluate AI opportunities based on business impact rather than technical sophistication.
Here's my playbook
What I ended up doing and the results.
Here's the framework I developed after watching dozens of AI implementations succeed and fail. It's designed to help you think like an informed buyer rather than an overwhelmed founder.
Step 1: The Problem-First Audit
Before talking to any AI consultant, identify your actual business problems. Not the problems you think AI can solve—the problems that are costing you money or growth right now.
I create a simple spreadsheet with three columns:
Business Problem (what's actually broken)
Current Cost (time, money, or opportunity)
Manual Solution (how you'd solve it without AI)
If you can't fill out that third column, AI isn't going to magically solve it either. AI amplifies existing processes—it doesn't create them.
Step 2: The 3-Question Consultant Filter
When evaluating any AI consultant, I ask these three questions in this exact order:
"What's the simplest non-AI solution to this problem?" (Good consultants explore alternatives first)
"Show me a similar project where the business impact was measured in dollars, not accuracy." (Results that matter)
"What happens if this AI solution breaks tomorrow?" (Sustainability planning)
If they can't answer these questions convincingly, they're selling technology, not business solutions.
Step 3: The Pilot Project Approach
Instead of big comprehensive projects, I insist on small pilots that can be evaluated in 30-60 days. The budget should be small enough that failure is a learning experience, not a business catastrophe.
My pilot criteria:
Budget under $10K
Clear success metrics defined upfront
Ability to measure business impact, not just technical performance
Exit strategy if results don't meet expectations
Step 4: The Education Investment
Here's my contrarian take: instead of spending $50K on consultants, spend $5K educating yourself. Take courses, attend workshops, read case studies. You don't need to become an AI expert, but you need to become an informed buyer.
The best AI implementations I've seen come from founders who understand enough about the technology to ask intelligent questions and spot BS when they hear it.
Step 5: The Internal-First Strategy
Before hiring external consultants, I recommend identifying internal team members who can become your "AI translators." These are people who understand both your business and enough about AI to evaluate solutions critically.
This approach has a huge advantage: your internal team knows your business problems intimately. They can spot when a consultant is solving the wrong problem or proposing unnecessarily complex solutions.
Red Flags
Watch for consultants who lead with technology instead of business problems, avoid showing real ROI examples, or push comprehensive solutions over focused pilots.
Right Questions
Ask about non-AI alternatives first, demand concrete business impact examples, and insist on small pilot projects with clear success metrics.
Smart Investment
Spend money on education and small pilots rather than big consulting projects. Understanding AI basics makes you a much better buyer.
Team Building
Develop internal AI literacy before hiring external help. Your team knows your business better than any consultant ever will.
After implementing this framework across multiple decisions, the results speak for themselves:
Cost Savings: By starting with education and pilots instead of comprehensive consulting projects, I've seen startups save 60-80% on their initial AI investments while getting better results.
Success Rate: Small pilot projects have a much higher success rate because they're focused on specific, measurable problems. When pilots work, scaling them is straightforward. When they don't, the learning cost is minimal.
Team Capability: Founders who invest in understanding AI basics become much better at evaluating future opportunities. They stop being victims of hype and start making strategic decisions.
The Unexpected Outcome: Many startups discover they don't need AI consultants at all. With basic education and the right tools, they can implement effective AI solutions internally.
One founder I advised used this approach to implement AI-powered customer service automation for under $8K—a project that consultants had quoted at $60K. The difference? He understood the problem well enough to choose simple, focused solutions over complex custom builds.
The framework also helps identify the 20% of consultants who are actually worth hiring. These are typically specialists who focus on specific industries or problems, have clear ROI case studies, and prefer small pilots over big comprehensive projects.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from testing this approach across multiple AI decisions:
Education beats delegation. Understanding AI basics makes you a dramatically better buyer of AI services.
Small pilots reveal truth faster than big plans. You learn more from a failed $5K experiment than a successful $50K presentation.
Business problems first, technology second. AI is a solution looking for problems, not the other way around.
Internal teams often outperform consultants. Your people know your problems better than any external expert.
Most AI consulting is temporary. Once you understand the basics, you rarely need ongoing external help.
Simple solutions usually win. Complex AI implementations fail more often than simple ones.
Timing matters more than technology. Waiting for the hype to settle often leads to better decisions.
The biggest lesson? Most startup AI problems are business problems, not technology problems. Consultants who understand this distinction are worth their weight in gold. The ones who don't are just expensive education.
If you're considering AI consulting, start with small pilots and internal education. The money you save on big consulting projects can fund multiple small experiments—and experiments are how you find what actually works for your specific business.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups specifically:
Start with customer support automation pilots
Focus on usage analytics before predictive features
Test AI content generation for marketing before product features
Evaluate consultants based on SaaS metrics experience, not just AI expertise
For your Ecommerce store
For ecommerce businesses:
Begin with product recommendation pilots
Test AI for inventory forecasting before customer-facing features
Prioritize email personalization over complex chatbots
Choose consultants with retail/commerce experience over pure AI expertise