The chasm between those who develop Artificial Intelligence and those who buy it.

Artificial intelligence has become a strategic priority for companies in all sectors. Administrative councils They push for artificial intelligence initiatives, executives announce digital transformations, and consultancies sell the promise of exponential gains. However, between the enthusiastic rhetoric and effective implementation, there is a chasm that few dare to acknowledge: the fundamental misalignment between those who buy artificial intelligence solutions and those who develop them.

This gap isn't just technical. It's primarily one of expectations, language, and mutual understanding. On one side, managers see artificial intelligence as a magic wand capable of solving complex problems overnight. On the other, data scientists and engineers struggle to translate sophisticated algorithms into measurable business value.

The ChatGPT effect

The advent of generative artificial intelligence has dramatically widened this gap. Since the launch of ChatGPT in November 2022, many executives have come to believe that artificial intelligence has reached a level of maturity where any business problem can be solved with a well-crafted prompt. This distorted perception—which we can call "technological guesswork"—has costly consequences.

It is not uncommon for companies to invest millions in artificial intelligence initiatives without understanding the technology's fundamental limitations. Projects are conceived with unrealistic timelines, poorly defined scopes, and success metrics disconnected from operational reality. The result is predictable: widespread frustration, wasted investments, and a false sense of innovation that masks the absence of real transformation.

Real benefits, realistic expectations

This doesn't mean that artificial intelligence is just hype. On the contrary: when well applied, artificial intelligence generates substantial and measurable value. The automation of repetitive tasks frees up skilled professionals for strategic activities. Massive real-time data analysis reveals patterns impossible to detect manually. Recommendation systems personalize experiences at scale. Predictive models support critical decisions based on quantitative evidence.

Companies in various sectors They are already reaping the benefits. In retail, dynamic pricing algorithms optimize margins in real time. In healthcare, machine learning models assist in the early diagnosis of diseases. In industry, predictive maintenance significantly reduces operational costs.

But these results don't happen by chance. They are the product of rigorous planning, disciplined experimentation, and, crucially, a realistic understanding of the iterative nature of AI projects.

The interactive nature of AI

Herein lies one of the biggest points of friction. Executives accustomed to projects of traditional softwareThey expect a linear process: requirements defined, development executed, product delivered. This logic works well for transactional systems, but collapses when applied to artificial intelligence.

Artificial intelligence and data science projects are, by nature, experimental. They evolve as they learn from the data. Hypotheses are tested, models are refined, approaches are discarded or improved. There is no way to accurately predict, at the beginning of the project, which algorithm will be most effective or which features will have the greatest predictive power.

This inherent uncertainty demands organizational maturity. It also requires a change in mindset: from rigid contracts to collaborative partnerships, from closed scopes to flexible objectives, from final deliverables to continuous learning.

Building bridges

Bridging this gap is a shared responsibility. Data scientists need to step outside their technical comfort zone and develop fluency in business language. It's not enough to build accurate models; it's necessary to clearly articulate how these models impact revenue, costs, or customer experience.

Managers, in turn, must invest in education. Understanding basic concepts of machine learning, Understanding artificial intelligence in general and the probabilities involved in processes performed by AI models is no longer optional for leaders making decisions about digital transformation. It's necessary to understand the difference between simple automation and machine learning, between rule-based chatbots and generative language models, and between correlation and causality. Furthermore, strategic partnerships are essential in this process, as is trust in the companies and professionals who will implement artificial intelligence in the processes.

Most importantly: both sides must accept that artificial intelligence is not magic, it is a method. It is applied science with rigor, discipline, and patience. It is controlled experimentation, statistical validation, and constant refinement.

The road ahead

As artificial intelligence becomes increasingly central to business competitiveness, this gap between buyers and developers cannot persist. Companies that manage to build effective bridges—through clear communication, aligned expectations, and genuine collaboration— They will have a decisive competitive advantage.

Those that fail will remain trapped in a vicious cycle: huge investments, disappointing results, growing skepticism, and missed opportunities. Because, in the end, artificial intelligence only ceases to be a promise and becomes a concrete result when there is mutual understanding, shared responsibility, and commitment to reality—however complex it may be.