• Written by Sonali Subhadarsini, Product Manager

This week, the shift supervisor at a large manufacturing plant must decide whether to adjust the production schedule to accommodate a backlog of maintenance tasks, and they currently base this decision on a combination of experience, manual checks of the maintenance log, and discussions with the maintenance team, all of which take up a significant amount of time that could be spent on more strategic tasks, such as optimising production workflows or improving product quality, which is where AI productivity in the enterprise can potentially make a difference in manufacturing.

Manufacturing complexities

The manufacturing sector presents unique challenges when it comes to implementing AI-powered productivity solutions, particularly in discrete and process plants that run to a tight schedule, where changeover losses can be significant, and multi-plant comparability is difficult to achieve due to differences in equipment, processes, and local conditions, and an ageing asset base can make it harder to integrate new technologies, all of which can limit the potential benefits of AI productivity in the enterprise.

For example, a category manager at a manufacturing company may struggle to compare the performance of different suppliers due to variations in their production processes and equipment, which can make it difficult to identify areas where AI-powered productivity solutions can be applied, and even when they are applied, the benefits may be limited by the need to work around existing systems and processes, which is why it is essential to carefully evaluate the potential benefits and limitations of AI productivity in the enterprise before implementing a solution.

Time saved vs time redeployed

While AI-powered productivity solutions can save time by automating routine tasks and providing real-time insights, the actual benefits may be limited by the ability of organisations to redeploy the saved time into more strategic activities, such as process improvement or innovation, rather than simply using it to catch up on existing workloads, which is why it is essential to have a clear plan for how the saved time will be used before implementing an AI-powered productivity solution, and to regularly review and adjust this plan as needed, which can be done by monitoring key performance indicators and gathering feedback from users.

In practice, this means that the shift supervisor may need to work with other teams, such as maintenance and production, to identify areas where the saved time can be redeployed to achieve the greatest benefits, and to develop new workflows and processes that take advantage of the capabilities of AI-powered productivity solutions, which can be a complex and time-consuming process, but one that is essential to achieving the full potential of AI productivity in the enterprise.

Self-reported time savings

Self-reported time savings are often used to measure the effectiveness of AI-powered productivity solutions, but these reports can be unreliable and may overstate the actual benefits, since they are based on user perceptions rather than objective measurements, and users may not always be aware of the full range of factors that affect their productivity, which is why it is essential to use more objective measures, such as data on task completion times or user engagement, to evaluate the effectiveness of AI-powered productivity solutions, and to regularly review and adjust these measures as needed.

For example, a user may report that an AI-powered productivity solution has saved them several hours per week, but this may not be borne out by objective data, which may show that the actual time savings are much smaller, or that the solution has simply shifted the workload to other tasks or teams, which is why it is essential to take a nuanced and multifaceted approach to evaluating the effectiveness of AI-powered productivity solutions, and to be aware of the potential limitations and biases of self-reported data.

Tasks worth removing

When implementing AI-powered productivity solutions, it is essential to focus on removing tasks that are repetitive, routine, or prone to error, rather than simply automating existing workflows, which can help to achieve the greatest benefits and minimize the risks of implementation, such as disruption to existing processes or job losses, and to identify these tasks, organisations can use techniques such as process mapping and user feedback, which can help to identify areas where AI-powered productivity solutions can add the most value, and to develop targeted solutions that address these areas, which can be done by working with users and stakeholders to understand their needs and priorities, and by reaching out to experts who can provide guidance and support.

For example, a manufacturing company may use AI-powered productivity solutions to automate tasks such as data entry or report generation, which can help to free up staff to focus on more strategic activities, such as process improvement or innovation, and to improve the accuracy and reliability of these tasks, which can help to reduce errors and improve overall productivity, and to achieve these benefits, organisations can use a range of techniques, including workflow analysis and user training, which can help to ensure that the AI-powered productivity solutions are used effectively and efficiently.

Limitations and wrong choices

AI-powered productivity solutions are not a panacea for all productivity challenges, and there are certain situations where they may not be the best choice, such as where tasks are highly creative or require a high degree of human judgment, or where the existing workflows and processes are highly complex or variable, which can make it difficult to implement AI-powered productivity solutions effectively, and in these situations, organisations may need to consider alternative approaches, such as process improvement or training, which can help to address the underlying productivity challenges and achieve the desired benefits, and to determine the best approach, organisations can use techniques such as cost-benefit analysis and user feedback, which can help to identify the most effective solution and ensure that it is implemented in a way that meets the needs of users and stakeholders.

In general, the decision to implement AI-powered productivity solutions should be based on a careful evaluation of the potential benefits and limitations, and should take into account the specific needs and priorities of the organisation, as well as the capabilities and limitations of the technology, which can help to ensure that the solution is effective and efficient, and that it achieves the desired benefits, such as improved productivity, reduced costs, and enhanced user experience, and to achieve these benefits, organisations can work with experts and stakeholders to develop targeted solutions that address the specific needs and challenges of the organisation, and to regularly review and adjust these solutions as needed to ensure that they remain effective and efficient over time.