Is AI shifting technical work towards technical validation?

AI as an organisational variable

In recent years, the debate around artificial intelligence has focused primarily on its operational capabilities. The ability to generate documentation, process large volumes of information, support research activities, or contribute to the development of new technical solutions has prompted increasingly broad reflection on the role these tools might play within organisations.

In our sector, however, the more interesting question is not so much what artificial intelligence can do, but how it changes the way technical work is organised. When certain activities become significantly faster than they once were, the impact goes beyond a simple reduction in execution time: priorities shift, workloads are redistributed, and in some cases so is the allocation of resources within a project.

Observing how these tools have evolved and how they are being used in technical contexts, one consideration stands out as particularly relevant: the most significant impact of AI may not lie solely in the automation of individual tasks, but in the reshuffling of the balance between producing, analysing, and validating technical information. It is in this redistribution, in our view, that one of the more consequential developments in technical work is taking shape.

Where the concrete advantage lies

In our experience, the areas where artificial intelligence is delivering the most tangible results are predominantly in the early stages of a project. Document analysis, information gathering, research into existing solutions, preliminary verification of technical hypotheses, and documentation production are all activities where meaningful time savings can be achieved compared to traditional methods.

These activities rarely coincide with the decision-making core of a project, yet they play an important role in building the informational context needed to make well-grounded decisions. Reducing the time required for these tasks means reaching relevant information more quickly and arriving at the initial phase of a project with a more complete working foundation.

This matters in particular because it allows certain preparatory activities to be approached with a depth that, previously, time constraints would not have permitted.

When time saved gets redistributed

One of the less-discussed consequences of introducing AI into technical processes concerns what actually happens to the time recovered. There is a tendency to assume that faster execution translates directly into a proportional reduction in overall workload. In practice, at least from what we have observed, the picture is more nuanced.

When a given task takes less time, some of the freed-up capacity is indeed recovered as efficiency. At the same time, however, space opens up for closer attention to aspects that are typically compressed by project deadlines. Analysing alternative solutions, examining certain design choices in greater depth, evaluating different scenarios, or revisiting earlier assumptions all become more accessible from a time standpoint.

In this sense, automation does not eliminate technical work — it redistributes it. Some activities take less time than before, while others can benefit from the capacity that becomes available. The outcome is not simply faster execution, but a different allocation of attention across the project.

More information, more demands on evaluation

The acceleration of preliminary activities produces a further effect worth examining. When generating information, documentation, and working hypotheses becomes easier, the volume of material that needs to be assessed grows accordingly.

Having more information available is generally an advantage, but it also introduces a new requirement: distinguishing what is genuinely useful from what, while accurate or plausible, does not contribute meaningfully to solving the problem at hand. The production of information and its interpretation do not necessarily scale at the same rate.

For this reason, a portion of the time recovered in execution-focused tasks tends naturally to be absorbed by evaluation and selection. This is not a drawback — it is a direct consequence of having more options available. The more alternatives are generated, the greater the need to understand their implications and identify those best aligned with the project's objectives.

The problem of plausible errors

When the reliability of AI-based tools comes under discussion, attention tends to fall on the more obvious failures. In technical work, however, the most difficult situations are not necessarily those where an output is clearly wrong.

Far more challenging are cases where a result appears coherent, well-reasoned, and seemingly consistent with the problem being addressed. In these situations, the risk does not stem from an evident error, but from the possibility that certain implicit assumptions, constraints, or contextual factors have not been properly accounted for.

The difficulty is that results of this kind do not simply require formal checking. They require a form of assessment that takes into account the application context, the project's objectives, and the downstream consequences that a given choice might generate. It is precisely in these situations that the difference between having information available and being able to use it effectively becomes apparent.

The role of technical validation in decision-making

In many contexts, technical validation is understood as a control activity carried out at the end of a process. In technical work, however, the concept often takes on a broader dimension, running through the entire decision-making process — from the assessment of available information to the selection of solutions considered most consistent with the project's objectives.

In practice, this process draws on a combination of factors: technical knowledge, professional experience, reference to previous cases, and evaluation of the project's specific requirements. Some information can be accepted quickly because it aligns with established understanding; other information calls for additional investigation or more structured verification.

Technical validation is therefore an integral part of the decision-making process. It is not a stage separate from technical work, but one of the activities through which available information is translated into design decisions. The time devoted to validation should not be read as an overhead cost introduced by automation, but as one of the activities that gains in importance as the capacity to generate information and design alternatives increases.

A transformation in how work is organised

Much of the commentary on artificial intelligence tends to focus on its capacity to automate existing tasks. This is certainly relevant, but observing how these tools have developed reveals something else as well. The increasing speed at which information and analysis can be produced tends to shift the relative weight of the different activities that make up a project.

Tasks that have traditionally been time-intensive can now be completed more quickly, while activities connected to interpreting information, evaluating alternatives, and carrying out technical validation of solutions take on a more central role within the process. This does not necessarily imply a proportional increase in verification activities, nor a reduction in the value of existing technical expertise. It means, rather, that a share of available capacity can be directed towards different activities than before.

From this standpoint, the more interesting transformation is not the replacement of technical work, but the way in which that work is distributed across the process. Understanding how to make effective use of the time and information these tools make available — and how to integrate that information into technical validation processes that remain consistent with project objectives — is likely one of the more significant challenges that technical organisations will need to address in the years ahead.