Today, everyone is talking about artificial intelligence. The press, LinkedIn gurus, software vendors. And yes: AI has enormous potential to transform the way we work.
But when we look at the reality inside companies, a different story unfolds: projects stall, pilots never scale, initiatives spark excitement at first but quickly fizzle out.

The truth is simple: they don’t fail because of the technology. They fail because no one ever clearly defined what problem needed solving.

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The Common Mistake

Many organizations fall into the trap of starting with the solution:

• “Let’s build a chatbot with AI.”

• “Let’s put a predictive model in sales.”

• “Let’s buy the most expensive tool on the market.”

The result is usually an unfocused deployment: dashboards no one checks, bots that frustrate, models disconnected from real decisions.
The difference between playing with AI and transforming with AI lies in starting with the problem—not the hype.

Why Start with the Problem

AI is an amplifier:

• If your team has clarity, AI enhances that clarity.

• If your process is already chaotic, AI only accelerates and magnifies the chaos.

A well-defined problem:

• Aligns teams around a concrete priority.

• Gives the investment an owner and urgency.

• Makes it possible to measure real results, not just “cute experiments.”

How to Define the Problem Clearly

You don’t need a 200-page manual. A simple framework works:

• Context: Where are we today?

• Specific pain point: What hurts or holds us back?

• Expected impact: What will improve if we solve it?

• Measurement: How will we know it worked?

If your definition doesn’t include these four elements, it’s probably not ready to guide an AI project.

Concrete Examples

• Poorly defined: “We want to use AI in HR.”

  • Well defined: “We want to cut the time spent screening résumés by 40% without losing quality in hiring.”

• Poorly defined: “We need a chatbot.”

  • Well defined: “We want 70% of frequent customer inquiries resolved in under two minutes, without human intervention.”

• Poorly defined: “We want a sales model.”

  • Well defined: “We want to predict with 85% accuracy the likelihood of closing each lead so we can prioritize sales efforts.”

What Happens After Defining the Problem

When there’s clarity, implementation is no longer a leap into the dark:

• Choosing the right technology becomes obvious.

• Resources get invested where they matter most.

• The team gets excited because they understand the why.

Conclusion

AI is not an end in itself. It’s an amplifier. It can multiply the value of what you already do well… or multiply the waste of what you do poorly.

That’s why, before thinking about tools, vendors, or models, ask yourself this question:

What is the most important problem your company has yet to name?

Because when the problem is clear, AI stops being a vague promise and turns into results that change the business.

How Workana Helps

At Workana, we support companies on this journey: we help identify the problems worth solving and connect those challenges with the best remote talent in Latin America, specialized in AI and agile execution. That way, companies don’t just explore technology—they achieve concrete, measurable results.