The Hidden Cost of Manual and Repetitive Work
In every organization, a substantial portion of working time is consumed by manual, repetitive, low-value tasks: data re-entry, file reconciliation, form processing, follow-ups. These activities, invisible in the metrics, weigh heavily on productivity and on team engagement.
The finding is broadly shared: according to the 2025 Bpifrance Le Lab Barometer, while a large portion of French leaders want to accelerate the integration of AI and automation, only a minority actually leverages it. Yet the potential is immense—some analyses estimate that a majority of administrative processes can be automated, wholly or partially.
Beyond the time lost, manual work also generates errors and delays. A manual re-entry carries a risk of mistakes; a process that relies on successive human interventions slows down and becomes hard to trace. In a context of cost pressure and skill shortages, leaving teams to perform these tasks amounts to wasting a scarce resource.
This cost also has a human dimension that is too often overlooked. Repetitive and uninteresting tasks demotivate, generate fatigue and drive turnover. At a time when attracting and retaining talent is a major challenge, asking qualified employees to spend their days re-entering data is not only inefficient, but counterproductive. Automation thus becomes as much a matter of engagement as of productivity.
From Simple Automation to Hyperautomation
Automation is not new, but its nature has changed. The first wave, RPA (Robotic Process Automation), involves deploying software robots that mimic human actions on applications: clicking, copying, typing. Effective on repetitive, rule-based tasks, it quickly hits its limits once a process requires judgment or handles unstructured data.
The hyperautomation marks the next leap. The term, popularized by Gartner, refers to the orchestration of multiple technologies – RPA, artificial intelligence, machine learning, process analytics – to automate complex end-to-end processes, and not just isolated tasks. We move from automating individual gestures to automating entire value chains.
It is AI that drives this shift. Where RPA follows fixed rules, AI can process unstructured documents, manage exceptions, and even make decisions. In 2025–2026, the advent of AI agents pushes the logic further: they can understand a request expressed in natural language and execute a task without a pre-established script—what is called agentic automation.
This evolution also democratises automation. The low-code and no-code interfaces of modern platforms empower business users—not just developers—to design their own automations. Coupled with AI agents capable of interpreting everyday language, this accessibility greatly broadens the circle of people who can automate their work—provided that this autonomy remains governed, or else we risk recreating a chaotic proliferation of uncontrolled automations.
Why the Topic Is Gaining Ground Now
Several forces make automation a priority. The first is economic: RPA can deliver a return on investment of 30% to 200% in the first year, depending on sector analyses. Reductions in costs, errors and delays combine into gains that are quickly measurable.
The second is market maturity. The global market for RPA, hyperautomation and AIOps is estimated at $22.4 billion in 2025, with annual growth of roughly 19–20% (Grand View Research). Tools have become democratized, with low-code/no-code interfaces that open automation to business teams, not only to IT.
The third force is competitive pressure. Automated companies gain agility and responsiveness, creating a lasting advantage. Conversely, those who remain tied to manual processes accumulate cost and speed disadvantages, especially as competitors accelerate with AI.
Getting It Right: Target Before You Automate
Automation is not a magic wand. A figure to heed: around 70% of transformation and automation projects do not reach their objectives. Success depends not only on technology, but on the approach. A few guiding principles steer the start of the journey:
- Map the processes that are truly time-consuming and repetitive, rather than automating at random – process mining techniques help identify them objectively.
- Prioritize by ROI: begin with high-volume cases, clear rules and measurable gains to demonstrate value quickly.
- Engage IT and business teams: the business teams know the processes, IT guarantees robustness and security.
- Plan governance from the outset: a fleet of robots left unsupervised quickly becomes unmanageable.
The challenge for a leader is not to automate for the sake of automation, but to free teams from non-value-adding tasks to refocus on what truly matters—customer relationships, analysis, and decision-making. When well-executed, automation is not a threat to jobs but a redeployment of value: projections point to job reductions, but also a net positive global creation of employment. The real question isn’t “should we automate?”, but “which processes, in what order, and with what governance?”
A final message to teams: successful automation is built with them, not against them. Employees who know a process best are also best placed to identify what can be automated and what should remain human. Involve them early, show them that robots relieve them of tedious tasks rather than replace them, and train them on the new tools. This turns legitimate fear into buy-in. It is this human dimension, as much as the technology, that determines the success of an automation initiative.
This content is published by Mentioned
