• Written by Sonali Subhadarsini, Product Manager

A shift supervisor at a manufacturing plant is tasked with implementing a new productivity initiative using AsscherAi, a platform that lets the organisation query its own data in natural language and get answers in real time. The supervisor is eager to get started, but is unsure where to begin, and what to expect from the first thirty days of using the platform.

Choosing a first case

When getting started with productivity initiatives, it's tempting to try to tackle the most complex problems first. However, this approach often leads to frustration and disappointment. A better approach is to choose a first case that already has a known answer to compare against. For example, the shift supervisor might start by using AsscherAi to answer questions about the maintenance log, which is already well-documented and easily verifiable. This allows the supervisor to test the platform and refine the query process in a low-stakes environment.

This approach also helps to build trust in the platform and its results. By comparing the answers provided by AsscherAi to the known answers, the supervisor can verify the accuracy of the results and gain confidence in the platform's ability to provide reliable information. This, in turn, makes it easier to expand the use of the platform to more complex and critical areas of the business.

Time saved vs time redeployed

One of the most common mistakes people make when implementing productivity initiatives is to focus solely on time saved. While reducing the time spent on a particular task is certainly a benefit, it's only half the story. The real value of productivity initiatives comes from the time redeployed - that is, the time that is freed up to focus on more important tasks. For example, if the shift supervisor is able to use AsscherAi to automate the process of generating reports, the time saved can be redeployed to focus on more strategic initiatives, such as process improvement or employee development.

This distinction is important because it highlights the need to think critically about how time is being used, and to prioritize tasks accordingly. Simply saving time is not enough - the time must be redeployed in a way that adds value to the business. By focusing on time redeployed, organisations can unlock the full potential of productivity initiatives and achieve greater returns on investment.

Self-reported time savings

Self-reported time savings are often overstated, and can be misleading. When people are asked to estimate the time they save using a new platform or process, they tend to overestimate the benefits. This is because they are often enthusiastic about the new initiative, and want to believe that it is having a significant impact. However, this enthusiasm can lead to inaccurate estimates, which can in turn lead to unrealistic expectations and disappointment.

A more reliable approach is to use objective measures, such as data from the platform itself, to track time savings. For example, AsscherAi can provide data on the number of queries run, and the time spent on each query. This data can be used to calculate the actual time saved, and to identify areas where the platform is having the greatest impact. By using objective measures, organisations can get a more accurate picture of the benefits of productivity initiatives, and make more informed decisions about how to allocate resources.

What to stop doing

If the comparison between the answers provided by AsscherAi and the known answers fails, it's time to stop and reassess. This might involve revising the query process, or seeking additional training or support. It's also important to consider whether the platform is being used in the right way - for example, are the right people using the platform, and are they using it for the right tasks? By stopping and reassessing, organisations can identify and address any issues that are preventing the platform from delivering its full potential.

This process of stopping and reassessing is an important part of the productivity journey. It requires a willingness to be honest about what's working and what's not, and to make adjustments accordingly. By being willing to stop and reassess, organisations can avoid wasting time and resources on initiatives that are not delivering results, and focus on the things that are truly driving productivity.

What this does not do

AsscherAi is a powerful tool for querying an organisation's own data in natural language and getting answers in real time. However, it is not a magic bullet, and it's not the right choice for every situation. For example, if an organisation is looking to integrate data from multiple external sources, AsscherAi may not be the best option. In such cases, it's better to explore other options that are specifically designed for data integration.

Similarly, if an organisation is looking for a platform that can provide predictive analytics or machine learning capabilities, AsscherAi may not be the right choice. While AsscherAi can provide some basic analytics and insights, it is primarily designed for querying and reporting, rather than advanced analytics. By understanding the limitations of AsscherAi, organisations can make informed decisions about when to use the platform, and when to look elsewhere. For more information on how to get started with AsscherAi, or to discuss your specific needs and requirements, please contact us.