AIxBu · Own framework, Ladibu's intellectual property

Everything begins with the business, not the technology.

AIxBu is the judgment and execution framework Ladibu uses to decide when to invest in artificial intelligence, when not to, and when. It doesn't start from what the technology can do: it starts from where the business needs to accelerate, and from there decides whether artificial intelligence is the right answer.

Why it exists

It was born from looking for a framework I couldn't find in any university program.

Before writing AIxBu, I reviewed artificial intelligence programs at several universities, looking for a serious framework for deciding where to invest. I found the same flaw in almost all of them: they moved from technology toward the client, not from the client toward technology. The first question was always what the vendor could do, never what the organization needed to solve. AIxBu was born from that absence.

Felipe Labbé · Founding partner of Ladibu, author of the framework

The thesis

Artificial intelligence doesn't fix broken processes. It doesn't make up for missing data. It doesn't replace a strategic decision nobody has made.

The most common mistake

Implementing artificial intelligence on top of a process that doesn't work.

Artificial intelligence amplifies what already exists. If the process is confusing, it makes it faster but just as confusing. If the data is wrong, the model learns from the errors. That's why, before talking about models or tools, the framework works through three questions.

  • What decision is this actually trying to improve?
  • Where is time, a customer, or revenue being lost, and with what evidence?
  • What capacity exists today to adopt and sustain the solution?
When it makes sense Four conditions, not a hunch.

Artificial intelligence adds value when these conditions are met, not before.

  • There's volume and repetition: tasks done hundreds or thousands of times, where speed or precision make the difference.
  • There are patterns in the data: information that can be trained to predict, classify, or recommend with judgment.
  • There's measurable impact: processes where before and after can be quantified.
  • There's adoption capacity: teams that can use the solution and adjust it in real operation.

If these conditions don't exist yet, the work is to create them first. Get the business in order before deciding on the technology. And when they do exist, it's still necessary to choose the right type of artificial intelligence: a predictive model answers when the pattern already exists in the data, a generative one when the task is producing content or language, and an agent when the task is executing steps and taking actions on other systems. Confusing the three is a frequent cause of projects that never get off the ground.

The framework

Six work blocks that connect strategy with execution.

It's not a mandatory path or a rigid methodology. Not every organization goes through all six stages, and not always in the same order. What activates depends on the problem, the starting point, and the decisions that need to be made. Every stage ends in executive decisions and concrete deliverables, not a diagnosis that gets filed away.

  • Aligns the artificial intelligence agenda with the business's real priorities: where to invest first, which problems come first, and what won't be done, and why.

    Key decisions · Where to invest first? · Which problems come first? · What capabilities need to be built? · What won't be done, and why?

    Maturity diagnosis · priority map · investment recommendations

  • Identifies where artificial intelligence can generate the greatest measurable value, through an immersion in critical processes, data, and real friction points.

    Key decisions · Which use cases have the greatest impact? · Which are viable with current capabilities? · What needs to be built first? · What risks need to be mitigated?

    Prioritized opportunity map · use cases with estimated impact

  • Defines an architecture that works in the client's real environment, avoiding the ideal design that can never be executed.

    Key decisions · What technology is used, and why? · How does it integrate with current systems? · What data is needed, and where does it come from? · How is success measured?

    Documented architecture · data and governance plan · adoption strategy

  • Validates with real data and users that the solution solves the problem, before committing a larger investment.

    Key decisions · Does the solution solve the problem? · Do users adopt it? · Is the impact measurable? · What adjustments are needed before scaling?

    Validated prototype · initial impact metrics

  • Takes the solution into operation with a real pilot group, with close support and real-time adjustments.

    Key decisions · Is the solution stable in production? · Do teams use it without friction? · Does the impact justify the investment? · Is it ready to scale?

    MVP in operation · adoption and impact dashboard

  • Turns artificial intelligence into a sustainable organizational capability, not a project that ends: governance, industrialization, data quality, and continuous measurement.

    Key decisions · How does it scale to other processes? · What capabilities need to be internalized? · How is artificial intelligence governed in operation? · How is maturity and continuous improvement measured?

    Governance model · developed internal capabilities · maturity roadmap

AIxBu isn't theory. It's expert judgment applied inside the client's operation, with their teams, from their real context.

No grand promises. With pace, evidence, and adoption.

Next step

Let's see whether your artificial intelligence agenda is aligned with what your business actually needs.

A thirty-minute conversation, no proposal attached.

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