Artificial Intelligence has become a strategic priority for companies across all sectors. Boards of directors are pushing for AI initiatives, executives are announcing digital transformations, and consultancies are selling the promise of exponential gains.
However, between enthusiastic rhetoric and effective implementation lies 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 AI 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. In this article, we'll understand the roots of this chasm and, above all, how to build the bridges that transform AI from promise into results.
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 AI has reached a level of maturity where any business problem can be solved with a well-crafted prompt.
"Technological guesswork"“
This distorted perception, which we can call "technological guesswork," has costly consequences. The ease of use of a conversational interface ends up being confused with the ease of solving complex business problems. Therefore, before choosing a technology, it is important to understand... Where to apply Artificial Intelligence in companies with criteria, impact, and realistic return expectations..
The consequences of unrealistic expectations.
It is not uncommon for companies to invest millions in AI initiatives without understanding the technology's fundamental limitations. Projects are designed 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.
Where AI delivers concrete value.
This doesn't mean that Artificial Intelligence is just hype. On the contrary: when applied well, it generates substantial and measurable value.
Automating 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.
Practical examples of AI proactivity by sector.
In retail, Artificial Intelligence can go beyond historical analysis and act proactively on price, inventory, and demand. A model can identify that a particular product is selling above expectations in a region and, based on this behavior, recommend price adjustments, inventory replenishment, or redistribution among units before a stockout occurs. In this case, the value of AI lies precisely in anticipating an operational need and allowing a response before the problem affects sales.
In sales and customer relationship areas, predictive models can identify signs of potential cancellation before the customer formally expresses their intention to terminate the contract. A drop in usage frequency, reduced purchases, increased calls, or changes in interaction patterns can indicate a higher risk of churn. Based on these signs, the system can prioritize these customers and alert the responsible team for more proactive and targeted retention efforts.
In industry, predictive maintenance is one of the clearest examples of this proactive approach. By monitoring operating data, intervention history, and equipment behavior, a model can identify changes that indicate an increased probability of failure. Instead of waiting for an unexpected shutdown, the system can signal the need for inspection or preventive maintenance, allowing the company to plan the intervention with less impact on operations.
The same logic can be applied to financial and control processes. A model can identify transactions, expenses, or behaviors that deviate from the expected pattern and forward these cases for analysis before the problem escalates. The benefit lies not only in detecting an anomaly but also in directing the team's attention to higher-risk situations, reducing the effort dedicated to manually checking routine operations.
Because these results don't arise by chance.
These results are the fruit of rigorous planning, disciplined experimentation, and, crucially, a realistic understanding of the iterative nature of AI projects. There is no shortcut that can replace this work.
The iterative nature of AI
Herein lies one of the biggest points of friction. Executives accustomed to traditional software projects expect a linear process: requirements defined, development executed, product delivered. This logic works well for transactional systems, but collapses when applied to Artificial Intelligence.
Why AI projects are experimental
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 variables will have the greatest predictive power.
The necessary change of mindset
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.
In practice, this approach can begin with controlled validation before scaling up. project developed by Paipe with Santista, For example, a Proof of Concept was used to validate an Artificial Intelligence application on corporate documents, combining summarization, content restructuring, and accessibility features. Instead of starting from a generic promise to "use AI," the project was structured based on specific operational needs and a scope that allowed for validating the solution before its evolution.
Building bridges
Mitigating this gap is a shared responsibility—it cannot fall to just one side.
What data scientists need to do
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 those models impact revenue, costs, or customer experience.
What managers need to do
Managers, in turn, must invest in education. Understanding basic concepts of machine learning, Artificial Intelligence, and the probabilities involved in processes performed by models is no longer optional for leaders who decide on digital transformation. It is necessary to understand the difference between simple automation and machine learning, between rule-based chatbots and generative language models, between correlation and causality.
The role of trust and partnerships
Strategic partnerships are essential in this process, as is trust in the companies and professionals who will implement Artificial Intelligence in the processes. Without trust, every uncertainty in the project becomes a source of conflict, instead of a collaborative learning experience.
AI is not magic, it's a method.
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.
How to recognize misalignment
Before building bridges, it's helpful to identify the signs that buyer and developer are not aligned. Among the most common are:
- The project is described by the technology ("we want an AI"), not by a business problem;
- The schedule is fixed and does not include any experimentation phases;
- The metrics for success are either vague or purely technical;
- A final result is expected, ready to go, without adjustment cycles;
- There is no clarity regarding the quality and availability of the data.
The more of these signs are present, the greater the risk of frustration — and the greater the need to align expectations before moving forward.
How to measure the success of an AI project
Part of the misalignment stems from poorly defined metrics. An AI project should not be evaluated solely on the technical accuracy of the model, but on the impact it generates on the business. This logic is close to... Decision Intelligence, an approach that connects data, analytical models, and business context to the decisions that need improvement..
Technical metrics vs. business metrics
Accuracy, precision, and recall determine if the model works; revenue generated, reduced costs, time saved, and risk avoided determine if it matters. Both dimensions need to go hand in hand, but it's the latter that justifies the investment.
The evaluation horizon
Since AI projects are iterative, evaluating them too early can lead to erroneous conclusions. Setting intermediate milestones and a realistic maturity horizon avoids both premature optimism and premature abandonment.
Who defines success?
The success of an AI project needs to be defined jointly by both the buyer and the developer before it begins. When both parties agree on what will be measured and how, the gap in expectations decreases dramatically.
Common mistakes when hiring AI projects
From the buyer's perspective, certain misconceptions frequently arise and widen the gap:
- Start with the desired technology, not the problem to be solved;
- to demand fixed deadlines and scopes for a process that is essentially experimental;
- underestimating the preparation effort and data quality;
- measuring the project solely by technical metrics, without linking them to the business;
- Treat the supplier as an order executor, not as a partner.
Avoiding these mistakes doesn't guarantee success, but it removes a good portion of the causes of frustration before the project even begins.
Frequently Asked Questions
Why do so many AI projects fall short of expectations?
Generally, this is due to a misalignment between buyers and developers: unrealistic expectations, poorly defined scopes, and metrics disconnected from reality, coupled with a lack of understanding of the iterative nature of technology.
What is the "ChatGPT effect"?
It's the perception, amplified after the launch of ChatGPT, that any business problem can be solved with a good prompt. This distorted view leads to investments without understanding the real limitations of the technology.
Why don't AI projects follow the linear software model?
Because they are experimental by nature. They evolve by learning from data, requiring tests, refinements, and adjustments that cannot be fully predicted at the start of the project.
How can we reduce the gap between AI buyers and developers?
With clear communication, leadership education, business fluency among technical teams, partnerships based on trust, and the acceptance that AI is a method, not magic.
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 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. And this commitment begins long before the first line of code: in an honest conversation about what AI can, and cannot, deliver.