Discussions about AI are often guided by technical experts and news about increasingly capable models, platforms, and functionalities. However, AI solutions are system projects like any others, and their success depends on more than technical capability alone. In everyday work, what truly matters is how AI is connected to workflows and to human work.
In this blog post, I describe what AI-assisted productivity looks like in practice: what it requires from processes, how it changes ways of working, and why the human role remains essential.
What does AI-assisted productivity mean?
In expert discussions about AI, there is often a negative underlying tone: fear that machines will take people’s jobs or that only fragments of meaningful work will remain. Goals that emphasize efficiency and productivity reinforce the perception that humans will not be sufficient or able to compete with machines in the future. Productivity, however, can also mean cooperation between humans and AI, a form of coexistence in which one catalyzes the other.
When AI becomes part of everyday work, the way work is done changes. Traditionally, the roles of humans and AI are modeled along a continuum where, in automation, human effort is replaced by technology, and in augmentation, AI strengthens human capabilities.
In automation, AI aims to handle a process end to end, and humans step in mainly to supervise or resolve exceptions ("human-in-the-loop"). Automation speeds up processes and transfers certain stages to machines, but at the same time it can make human work more cognitively demanding. New types of workflows require investigation, and understanding the context takes time.
For many experts, a more appealing starting point is augmentation, where human work forms the basis and AI is used at points where it brings the right kind of efficiency and perspective to the task ("AI-in-the-loop"). In this approach, humans choose and direct. Human workflows remain self-directed, context is preserved, and flexibility emerges from different ways of using AI. This can speed up tasks and reduce workload.
When considering productivity between humans and AI, it's important to remember that becoming a professional in one’s field requires thinking and learning. If AI fully takes over a particular part of the work, learning and development may not occur. In the long term, this weakens both the expert’s professional identity and sense of meaningful work, as well as the organization’s future capabilities. From an efficiency-driven perspective outside the role itself, reshaping work can appear as a loss of control and a devaluation of expertise, leading people to avoid optimized tasks and to reshape their work independently in new directions.
Process and user understanding are the foundation of AI benefits
Discussions about AI often focus on technical capabilities, but organizations’ real challenges are usually elsewhere. The gap between what people do intuitively and what a machine needs as explicit instructions to produce the same outcome is often surprisingly large.
AI-assisted productivity requires strong understanding of workflows and processes. The AI era has brought a refreshing amount of discussion about the importance of workflows, and for good reason. If processes are unclear, AI solutions are difficult to integrate effectively or in a way that does not unintentionally disrupt the variation that exists in everyday work.
When the goal is to identify what information is truly used in work, user research and observation are critical ways of understanding the content of work. They make different ways of thinking, exceptions, and employees’ own shortcuts visible, meaning things that do not appear in process diagrams but can significantly affect the success of an AI solution.
Many organizations have noticed that documented processes and real-life work do not always align. People compensate for gaps with their own solutions, such as sticky notes or personal notes, which are not visible in official process descriptions. AI cannot take advantage of these hidden practices or the role of their content unless they are identified. This is why opening up workflows and describing even their smaller components is necessary. Before breaking down a workflow, it's worth asking whether the task should be assigned to AI at all. Not all tasks benefit from AI. Some are more efficiently handled as traditional code, and in others the human role is critical. Does the task require mathematics, rule-based logic, stylistic adaptation, qualitative judgment, or risk assessment? In practice, shaping the role of AI involves examining organizations’ traditional ways of collaboration and knowledge creation, the nature of information processing required by tasks, culture, teams, and how people interact with technology.
Breaking down workflows is therefore above all a learning process, and better understanding of work can in itself lead to improvements. When solutions are designed on the basis of strong user understanding, AI becomes a natural part of everyday workflows instead of remaining a detached add-on or even a bottleneck. Good design may not be visible, but it connects technical possibilities and human behavior in an effective way that fits the specific organization.
How to reap the benefits from AI-assisted productivity?
An old consulting saying applies to AI as well: you cannot scale what does not exist. Benefits must first emerge somewhere before they can be replicated elsewhere. Even a small investment is too much if it does not generate added value. In addition to time savings, it's important to measure factors such as accuracy, costs, and the consequences of errors, and to ensure that critical workflows are not disrupted.
Once benefits have been proven, replicating them can produce significant improvements. Scaling, however, is not only about technology, as it requires careful planning and adaptation of workflows. Good design solutions, usability, and seamless integration of AI into processes are just as important as in other software initiatives. If AI disrupts ways of working, small benefits can quickly disappear. Organizational learning and a culture of experimentation are key. Successful AI integrations often emerge iteratively, as the first phase enables learning and reveals real impacts. In this way, collaboration between humans and AI is also scaled, and organizations gain insight into what actually happens in everyday work.
Collaboration between humans and AI can deliver significant productivity benefits when the outcome is better than what either could achieve alone. This state requires learning and experimentation. Organizations should develop ways to assess the role of AI. Were decisions made with its help? Was feedback provided? Do employees have time to experiment? The integration of AI into workflows indicates that something is happening, but not yet whether it's working. It's necessary to monitor whether AI produces genuine added value or merely visible activity. The best measure is whether the outcome is better than before.
Could we help you build AI-assisted productivity? Contact us or send us an email: myynti@norrin.com!
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