• Written by Sonali Subhadarsini, Product Manager

A shift supervisor on the production line of a major automotive manufacturer is likely to be familiar with the constant pressure to meet tight production deadlines, while also dealing with the complexities of managing a tiered supply chain and implementing engineering changes to existing programmes. As they check the maintenance log and review the day's production schedule, they may wonder how AI productivity tools, such as those provided by AsscherAi, can help streamline processes and return time to their team.

Unique challenges in automotive

The automotive industry presents a number of unique challenges that can make it harder to implement AI productivity tools effectively. For example, supplier escalations can be a major issue, as any disruption to the supply chain can have a significant impact on production. Similarly, engineering change propagation can be a complex and time-consuming process, requiring careful coordination across multiple teams and stakeholders. Warranty root cause analysis is another area where AI productivity tools can struggle to make an impact, as it often requires a deep understanding of the underlying technical issues.

These challenges can make it difficult for AI productivity tools to deliver the expected benefits, as they may not be able to fully account for the complexities of the automotive industry. However, by understanding these challenges and adapting the implementation of AI productivity tools accordingly, it is possible to achieve significant productivity gains. The category manager, for instance, can use AsscherAi to compare data from different suppliers and identify potential issues before they become major problems.

Time saved vs time redeployed

One of the key differences between AI productivity tools and other types of productivity initiatives is the distinction between time saved and time redeployed. While AI productivity tools can certainly save time by automating routine tasks and providing quick answers to common questions, the real benefit comes from being able to redeploy that time to more strategic and value-added activities. For example, a shift supervisor who no longer has to spend hours reviewing production reports can use that time to focus on process improvements and employee development.

This requires a fundamental shift in how we think about productivity and the role of AI in the enterprise. Rather than simply looking to save time, we should be focused on how we can use AI to enable our teams to work more effectively and make better decisions. By doing so, we can unlock the full potential of AI productivity tools and achieve significant gains in productivity and efficiency. To learn more about how AsscherAi can help, visit our website for more information.

Self-reported time savings

Another issue with AI productivity tools is that self-reported time savings often overstate the effect. When teams are asked to estimate how much time they have saved by using a particular tool or system, they may be inclined to exaggerate the benefits in order to justify the investment. This can create unrealistic expectations and make it difficult to accurately assess the impact of the tool.

In reality, the actual time savings may be more modest, but still significant. By using AI productivity tools to automate routine tasks and provide quick answers to common questions, teams can free up time to focus on more strategic activities. However, it is essential to take a nuanced view of the benefits and to carefully evaluate the actual impact of the tool, rather than relying on self-reported estimates. The programme manager, for instance, can use data from AsscherAi to get a more accurate picture of how time is being spent and where improvements can be made.

Tasks worth removing first

When implementing AI productivity tools, it is essential to identify the tasks that are worth removing first. These are typically routine, repetitive tasks that can be easily automated, such as data entry or report generation. By removing these tasks, teams can free up time to focus on more strategic and value-added activities, such as process improvement and employee development.

For example, a category manager can use AsscherAi to automate the process of comparing data from different suppliers, allowing them to focus on more strategic activities such as negotiating contracts and managing relationships. By removing these routine tasks, teams can achieve significant productivity gains and improve overall efficiency. Some of the tasks that may be worth considering for removal include:

• routine data entry
• report generation
• information retrieval

Limitations and wrong choices

While AI productivity tools can be a powerful way to improve productivity and efficiency, they are not a silver bullet and may not be the right choice for every situation. For example, in situations where the underlying processes are fundamentally broken, AI productivity tools may not be able to make a significant impact. In these cases, it may be necessary to go back to the drawing board and redesign the processes from scratch, rather than trying to automate them.

In other cases, the data may not be available or of sufficient quality to support the use of AI productivity tools. In these situations, it may be necessary to invest in data collection and cleaning efforts before implementing AI productivity tools. To discuss your specific needs and determine whether AsscherAi is the right choice for your organisation, please contact us to arrange a consultation.