AI speeds up work but doesn’t always improve it. Here are the key issues

Although AI increases work speed, much of the time saved is spent on corrections, highlighting the need for organisational and training changes

by Gianni Rusconi

27 April 2026

5' min read

Translated by AI

Versione italiana

5' min read

Translated by AI

Versione italiana

Four in ten Italian workers say they save up to one day a week thanks to artificial intelligence. Yet behind this seemingly unequivocal figure lies a paradox: a significant proportion of that time (around half) is spent correcting, verifying and rewriting imperfect outputs. This is what emerges from Workday’s global research, “Beyond Productivity: Measuring the Real Value of AI”, presented a few weeks ago at the opening of the Californian multinational’s Innovation Lab in Milan. The trends worth reflecting on are clearly illustrated by two figures, which are as follows: while 92% of employees claim to be more productive thanks to AI, up to 40% of the time saved is, however, spent on reworking, with one in two employees specifically dedicating one to two hours a week to correcting results generated by algorithms deemed to be of inadequate quality. In other words, we are faced with a sort of ‘apparent productivity’, where the speed of task execution increases, but this does not always translate into a real improvement in the work process.

From enthusiasm to maturity in use

Italia is currently in the early stages of the AI adoption cycle, a fact seemingly confirmed by the fact that only 29% of workers use this technology on a daily basis, although the perception of its benefits is generally very high. According to Fabrizio Rotondi, Country Manager at Workday Italia, these indicators should nevertheless be interpreted with caution. ‘Comparing Italian data with global and US figures, we can say that in our country we are still in the hype phase, whilst in the United States there is already a higher level of scepticism towards artificial intelligence. In Italia,” the manager continues, “we are mainly seeing the most enthusiastic users, often young people, who have not yet developed a full understanding of how to manage the increased productivity brought about by technology.”

This imbalance is set to diminish over time, as adoption matures, but it raises an initial critical issue: the widespread use of AI does not automatically equate to the effective use of these tools. In fact, it is always the quality of the data that determines its value, to the extent that one of the main risks associated with the widespread adoption of generative models is what Rotondi calls ‘shallow AI’, i.e. systems that generate rapid outputs but are not sufficiently reliable. If many projects fail, Workday believes, it is because they are working with unclean or unstructured data; it is no coincidence that one of the American company’s ‘musts’ is to avoid this phenomenon, and the recipe for doing so is to use a cloud-based database built thanks to the platform’s more than 11,000 customers and the approximately 1.4 trillion transactions processed each year. Ultimately, without a solid database, the risk companies face is that of increasing productivity without improving accuracy, highlighting all the limitations (and the problem of rework is obviously among these) of an incomplete integration between technology, processes and information.

The organisation is struggling to keep up with technology

Another key aspect of the issue concerns the mismatch between tools and organisational models. In most companies, roles and processes have not evolved at the same pace as technology, a fact clearly highlighted by the contrast between employees now using tools released in 2025 within organisational structures still rooted in 2015-era systems. This dichotomy does nothing but increase the cognitive load and individual responsibility of staff, who are forced, against their will, to manage more output and carry out more checks.

Those most affected by the phenomenon of reworking are, in fact, younger workers (and theoretically the best qualified), whilst at the same time the most frequent users are also those under the greatest pressure, with over 77% of the sample of workers interviewed stating that they check the results produced by AI with the same level of attention, if not more, as they would for human work. “The real issue,” Rotondi pointed out in this regard, “is change management. Companies often fail to seize the opportunities offered by AI due to inertia or the difficulty of rethinking models and processes, and this confirms that the turning point is organisational rather than technological.”

The officers are arriving; we need to reinvest in people

Any discussion of the impact of AI on the workplace must necessarily take into account an evolving landscape that goes beyond the use of individual tools and points towards deeper integration between people and intelligent systems (agents). ‘We envisage a future in which artificial intelligence will be the new interface for business processes, with agents integrated throughout the organisation. The key issue – adds Rotondi – will be the governance of these assistants, and therefore how and when to activate them, in which areas and to what extent to delegate activities and tasks to them.” The model that is emerging is precisely that of a structured collaboration between humans and digital agents, faithful to the ‘human in the lead’ principle, in which the person not only acts as a supervisor but has full responsibility for managing processes and decisions.

However, this evolution among employees (and the managers who lead them) also implies a shift in the skills required: the focus is moving away from specialist knowledge towards soft skills such as leadership, critical thinking, interpersonal skills and decision-making. In this context, even human fallibility takes on a new value and, according to Workday’s Country Manager, ‘can become an asset, particularly in strengthening empathy and engagement’.

A managerial challenge, rather than a technological one

Returning, however, to what is perhaps the most significant finding of the research – the figure quantifying the time ‘saved’ through the use of AI, it is interesting to note that the difference between companies that achieve results and those that lag behind lies not so much in the degree of technology adoption itself, but rather in how employees utilise this time. In this regard, it turns out that the most advanced organisations reinvest these ‘savings’ in skills development, the review of job roles and the improvement of decision-making processes; conversely, many companies continue to allocate resources primarily to technology or to increasing the workload.

“The great opportunity we have,” concludes Rotondi, “is to help people use AI effectively, especially in tasks that require critical thinking and creativity.” This refers directly to the new Innovation Lab in Milan, a space for co-creation between companies, partners and experts to develop concrete use cases where design thinking is applied and prototypes of intelligent agents are built for field testing. The challenge to be overcome (for Workday and the thousands of Italian companies already experimenting with AI) is to manage its use through a parallel evolution of processes, skills and organisational models: the risk of generating merely partial efficiency is real, and management’s task is instead to transform the benefits brought about by technology into quality of work, decision-making capacity and competitive advantage.