Advances in artificial intelligence are transforming how work is performed across sectors, with growing interest in its ability to improve efficiency and productivity (Naqbi, Bahroun and Ahmed, 2024). Generative AI in particular enables the autonomous creation of content such as text, images, and data outputs, supporting a wide range of professional activities (Naqbi, Bahroun and Ahmed, 2024). Its use is associated with automating tasks, improving data analysis, and assisting decision-making processes (Naqbi, Bahroun and Ahmed, 2024).
However, productivity gains are not automatic. Research shows that outcomes depend on how technologies are implemented and combined with existing workflows and systems (Bughin, 2026). While AI can improve task execution and reduce manual effort, its effectiveness is shaped by organisational design, supporting tools, and how work is structured (Bughin, 2026).
Understanding these conditions is critical to using AI effectively in practice.
1. AI as a tool for reducing manual effort
Generative AI is widely recognised for its ability to automate tasks and support professional work across multiple sectors (Naqbi, Bahroun and Ahmed, 2024). It can assist with activities such as content creation, data analysis, and customer interactions, enabling faster execution of routine work (Naqbi, Bahroun and Ahmed, 2024).
Research identifies that AI contributes to productivity by:
- Automating repetitive tasks.
- Improving data analysis.
- Assisting decision-making.
- Reducing operational costs and errors.
These capabilities reduce the need for manual intervention in routine processes, allowing work to be completed more efficiently.
2. AI and Productivity Improvement
Evidence shows that AI has the potential to “dramatically boost productivity” by simplifying complex challenges and enhancing decision-making (Naqbi, Bahroun and Ahmed, 2024). In practice, this includes:
- Faster information processing.
- Improved accuracy.
- More consistent outputs.
In business and organisational contexts, AI is also used to streamline processes and improve information flow, contributing to operational efficiency (Naqbi, Bahroun and Ahmed, 2024).
However, the extent of improvement varies depending on how AI is applied. Studies highlight that productivity gains are not uniform and depend on factors such as implementation, context, and supporting systems (Bughin, 2026).
3. Automation and workflow redesign
AI improves productivity primarily by redesigning workflows rather than simply replacing human effort (Bughin, 2026). Technologies such as robotic process automation can:
- Execute routine tasks faster (Bughin, 2026).
- Improve task accuracy (by 10–15%) (Bughin, 2026).
- Reduce task completion time (by around 20%) (Bughin, 2026).
These improvements reduce manual workload, but benefits are only realised when workflows are adapted to integrate these technologies effectively (Bughin, 2026).
If processes remain unchanged, automation may not deliver expected gains.
4. The importance of combining technologies
A key finding across research is that productivity improvements depend on how technologies are combined rather than used in isolation (Bughin, 2026).
For example:
- AI alone may improve task execution.
- Remote tools improve coordination.
- Automation reduces routine workload.
When used together, these technologies enable better task allocation and more efficient use of resources (Bughin, 2026).
Organisations that combine AI, automation, and digital tools experience higher productivity growth than those adopting them separately (Bughin, 2026).
5. Task-based productivity gains
AI changes productivity by altering how tasks are performed. Instead of replacing entire roles, it reallocates tasks between humans and machines (Bughin, 2026).
- Routine tasks are more easily automated.
- Complex or non-routine tasks remain human-led.
This redistribution allows human effort to focus on higher-value work while AI handles repetitive processes (Bughin, 2026).
This shift is a key mechanism that reduces manual effort.
6. Limitations and conditions
Despite its potential, AI does not always lead to productivity gains (Hajikhani et al., 2025).
Research highlights several constraints:
- Effectiveness depends on workflow redesign (Bughin, 2026).
- Benefits require supporting infrastructure (Bughin, 2026).
- Coordination challenges can reduce gains (Bughin, 2026).
For example, technologies such as remote work tools can improve productivity, but may also introduce coordination challenges if not supported properly (Bughin, 2026).
Similarly, AI systems require appropriate integration and organisational readiness to deliver consistent results.
Focus on where manual effort is concentrated in current workflows. Identify tasks that are repetitive, time-consuming, or data-heavy, and assess whether AI tools can automate or support them. Ensure that processes are redesigned alongside technology adoption, as research shows productivity gains depend on how tools are integrated rather than used in isolation (Bughin, 2026).
Turning AI Into Real Productivity Gains
AI can reduce manual effort, but only when it is applied to appropriate tasks and methods. Evidence shows that productivity gains come from automating tasks, improving workflows, and combining technologies effectively - not simply introducing new tools (Naqbi, Bahroun and Ahmed, 2024; Bughin, 2026). This checklist focuses on its practical application, helping ensure AI is improving routine work rather than adding complexity.
Work through each row and assess honestly. If the answer is “no” or “partly,” this is where effort should be focused. Productivity gains from AI depend on redesigning work, not just adopting tools (Bughin, 2026).
|
Situation In Your Workplace |
What This Usually Means |
What To Do Next |
|
Teams are still doing repetitive manual tasks (e.g. reporting, emails, data entry) |
AI has not been applied to routine work yet |
Identify key repetitive tasks and explore AI automation |
|
AI tools are available, but people still work the same way |
Tools have been added without changing workflows |
Redesign processes to integrate AI into how work is done |
|
Outputs are faster, but work still feels inefficient |
AI is used in isolation |
Combine AI with other tools and systems supporting work |
|
Staff spend time gathering or analysing information manually |
AI is not being used for decision support |
Use AI to support data analysis and decision-making tasks |
|
Errors, rework, or inconsistencies are still common |
Processes are not supported effectively by AI |
Apply AI in ways that reduce errors and improve accuracy |
|
Work slows down when collaboration is needed |
Coordination challenges are limiting productivity |
Strengthen use of digital tools that support coordination |
|
Skilled staff are focused on routine or repetitive tasks |
Tasks are not effectively distributed between people and technology |
Reallocate routine tasks to AI where appropriate |
|
Different tools are used, but not aligned |
There is fragmented use of technology |
Ensure technologies are used together to support workflows |
|
Productivity gains are unclear or inconsistent |
AI impact varies depending on how it is used |
Review whether AI is improving efficiency and reducing errors in practice |
|
AI adoption feels like extra work rather than less |
There is poor implementation or unclear application |
Refocus AI use on simplifying tasks and reducing manual effort |