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Common Causes of Failure

Most AI projects fail because companies skip the assessment phase. They see a shiny new tool and think "we need this" without asking "do we actually need this?" Governments are mandated to implement AI, but they don't even know where to begin.

Here are the common causes I see:

➡️ Companies implement AI solutions they don't actually need instead of those they really need
➡️ They implement AI without clear ROI
➡️ They don't take their own context into account (no strategy)
➡️ Businesses are either overhyped about AI or don't trust it. In short, they don't know what it can really achieve. The statistics back this up: 85% of AI projects never make it to production.

Case Study: How we solved our own problem using AI

Our Challenge:

At Turalabs, we're mainly composed of tech people: AI and data science engineers. Most of the business development and marketing falls on me. To get B2B clients, you don't have many choices: either you get clients organically through content and word of mouth, or you go knock on doors (cold outreach).

Assessing Our Business for AI

As explained in a previous article, we’ve developed a simple assessment framework:

🎯 Actionable Business Challenges - What problems need solving?
📈 Impact Potential - What's the potential business value?
🔄 Repetitive Task Automation - Which manual processes can be automated?
📊 Operational Data Readiness - Is your data AI-ready?
⚡ Implementation Complexity - How quickly can you implement?

This framework helps us avoid the "AI for AI's sake" trap for us and our clients.

So what does it look like for us:

Our A.I.R.O.I. Assessment:

✅ Actionable: Need to get clients for our B2B agency, so outreach at scale is one solution

✅ Impact: High - personalized subject lines get 50% higher open rates. We calculated a monthly cost of $160-200 for 5,000 highly personalized emails. One client gets us $1,000+ minimum. ROI is crystal clear.

Research shows that 77% of B2B buyers prefer email communication, and personalized content delivers 6x higher conversion rates. For our ticket size, this made perfect business sense.

✅ Repetitive: Manual prospect research takes 20+ minutes per lead
✅ Operational: LinkedIn data + company info readily available through scrapers
✅ Implementation: Medium complexity - we have the technical skills

What We Built

Instead of buying expensive AI outreach tools, we built exactly what we needed.

My context: I had a lead database with 5,000 emails monthly, so no need to buy a database. I wanted to manage outreach through a single tool, so I used Instantly.ai.

What was missing? 
➡️ LinkedIn profile scraping for both lead and company
➡️ Lead and company intelligence insights using AI with sales angles
➡️ Recent posts sentiment analysis
➡️ Personalized email generation using best cold outreach practices (we can make each email different!)

Cost: Fraction of hiring a salesperson or buying enterprise tools.

How we built it: Using n8n (an automation tool) Why n8n: It offers the right technical complexity for our use case Does everyone need the same solution? No. If you do B2C, this isn't for you. If you do very high-ticket B2B or enterprise contracts, this isn't for you either (you need more direct interactions).

The Key Insight

The best AI solution isn't the most sophisticated one. It's the one that fits your exact business context.

Before building any AI solution, ask yourself:

1. What specific problem am I solving?

2. What's the potential business impact?

3. Can I automate repetitive tasks?

4. Is my data ready? Or can I get it?

5. How complex does this need to be?

Most businesses need simple, focused solutions - not enterprise-grade complexity.

Bottom Line

AI works best when you match the technology to your actual business constraints, not the other way around.

Want to see this AI workflow or get a free assessment for your business? DM me on LinkedIn or send me a message at [email protected].

Until next time!

Amine