Your company doesn't need "AI first" - it needs good use cases

A few months ago a statistic attributed to an MIT report went viral: most AI projects never translate into measurable P&L impact. That number was shared so many times it became a convenient idea: AI is just hype.

My conclusion, after months co-building an agents company, talking with venture funds, and advising enterprises, is different: AI does fail often, but almost always because of implementation, not technology.

The problem is not that "the models don't work." The problem is that many organizations are trying to drop a probabilistic technology into processes that demand reliability, without redesigning the system around it.

Below I break down where adoption cracks and how to convert hype into real utility.

The core idea: AI is probabilistic, your operation is not

An LLM is not a calculator. It produces the most likely output given a context. That makes it great for language, ambiguity, and variation. It also makes it risky for processes where "almost always" is not good enough.

When companies miss this difference, two things happen:

• They design expensive solutions for simple problems.
• They run pilots that look great in demos but die in production.

That is the root of most "failures."

The Aladdin lamp syndrome

The most common mistake starts with a sentence: "We need to add AI."

That vague language creates unrealistic expectations and inefficient architecture. APIs like GPT or Claude get treated like a magic lamp that grants unlimited wishes.

But every wish has real costs:

• Variable cost: every call has a price.
• Integration cost: systems, permissions, authentication, data.
• Control cost: evaluation, monitoring, guardrails, audits.
• Change cost: redesigning processes and team habits.

A typical example

"We should add AI to our marketplace to connect supply and demand automatically."

Many times that is solved better with deterministic software:

• SQL queries.
• Logical filters.
• Simple rules.
• Basic scoring.

Using an LLM for this is like using a flamethrower to light a candle:

• Slower: model latency is usually higher than a query or calculation.
• More expensive: every call has incremental cost.
• Less reliable: models can hallucinate. Math does not.

Discernment is adoption. Do not use a complex model for a basic engineering problem.

The cheap labor trap in Latin America

In Latin America there is an additional structural barrier: labor economics distort the incentive to automate.

In many contexts, highly automatable operational roles can be paid $250 to $400 per month. That pushes a false comparison:

• Option A: invest time and capital in a scalable solution.
• Option B: overload the team or hire someone cheaper.

Short term, option B "wins." Long term, it kills scalability.

Because there are two different economies:

• Technology has high upfront cost and low marginal cost.
• Operational labor has linear cost and scales with volume.

As your operation grows, the "cheap human solution" becomes a wall. You end up with more people, more coordination, more errors, and more friction.

Ethical resistance as a defense mechanism

On the worker side, the fear appears: "This will take my job, we should ban it."

Fear is valid. What does not help is staying in a static view. History suggests a constant pattern: society adopts what improves comfort and efficiency, even if tasks are displaced.

The useful questions are:

• What tasks get commoditized?
• What skills gain value?
• How does the role shift toward higher-judgment work and supervision?

In practice, AI replaces repetitive tasks first, not full human capability.

Implementation: back to first principles

Many companies repeat the classic startup sin: they have a solution and go looking for a problem.

You hear:

• "We need to be AI first."
• "We need to be AI native."

The right question is: what does that mean in metrics?

I have heard too many conversations like this:

• "We want to be more efficient."
• "Efficient in which process?"
• "Automate reports."
• "Why AI and not a dashboard, automation, or a process change?"
• "I do not know, my boss saw it in a video."

That path guarantees failure.

Before you open ChatGPT, define the problem precisely:

• What is the real bottleneck?
• What metric should move?
• What is the risk of failure?
• What part is deterministic and what part is ambiguous?

If the answer is not clear, you do not need AI. You need order.

A 60-second decision framework

Before "agents," decide between three options:

• Rules: if the problem is stable, repeatable, and has few exceptions.
• Classic automation: if the flow is clear and the input is structured.
• AI: if the input is unstructured, the domain is linguistic, or there are too many variations.

Prompt engineering: logic and context, not magic

Prompt engineering gets mystified as if it were alchemy. In most companies, it is simply clear communication.

The fundamental mistake is assuming the model "understands" your context. It does not. An LLM is a text predictor, not a mind reader.

If you ask a human designer, "Make this more premium," they will ask questions. With a model, if you give no context, you get a generic, convincing, and sometimes incorrect answer.

Treat AI like a brilliant intern with no context:

• Delimit the scope: what is in and what is out.
• Define the format: what structure should it return.
• Provide real context: audience, objective, constraints.
• Give examples: one good and one bad, even if short.

"Garbage in, garbage out" applies, with a dangerous twist: the garbage can sound very convincing.

Leadership: no desk generals

Real adoption does not happen by decree. It happens by cultural osmosis. If leadership does not use the tools, the rest of the organization ends up performing theater.

"It is too technical" is no longer a valid excuse. You do not need to be an expert, but you do need to be a user. The difference between top teams and obsolete teams is often leadership that tests, fails, and learns in public.

The minimum stack for the modern leader

Not to automate everything on day one, but to build judgment:

• To build MVPs: Lovable, v0, Replit.
• To create content: HeyGen, ElevenLabs, Gemini (image).
• To think and analyze: ChatGPT, Claude, Perplexity.
• To operate: n8n, Zapier.

Closing: what real adoption looks like

If you want a sentence that avoids months of smoke:

AI does not fix broken processes. It amplifies good ones.

A serious implementation almost always has this:

• A clear metric.
• Embedded in the workflow.
• Guardrails and fallback paths.
• Evaluation with real cases.
• A responsible owner.
• Less friction, not more.

And here is the most important part, because it ties back to the core idea of adoption:

The reality is that simply using these tools day to day, from leaders to contributors, lets everyone see what actually helps and what does not. Rather than forcing everyone to use a tool, each person discovers what applies to them.

That, to me, is the most effective way to actually make a company more efficient.