• Written by Sonali Subhadarsini, Product Manager

A shift supervisor in a pharmaceutical manufacturing plant spends a significant amount of time reviewing batch records, investigating deviations, and managing change control processes, all of which are critical to ensuring the quality and safety of the products being produced. However, these processes can be time-consuming and may take away from other important tasks, such as optimizing production workflows or improving employee training. The introduction of AI productivity tools in the enterprise, such as AsscherAi, aims to alleviate some of this burden by providing real-time answers to questions about the organisation's own data.

Pharmaceuticals' unique challenges

The pharmaceutical industry is heavily regulated, and as such, is subject to strict documentation rules and validated processes. This can make it difficult to implement new technologies or processes, as any changes must be carefully evaluated and documented to ensure compliance with regulatory requirements. For example, a category manager may need to spend several hours reviewing and approving changes to a supplier's quality control process, which can take away from other important tasks. The overhead associated with change control, batch record review, and deviation investigation can be significant, and may limit the ability of pharmaceutical companies to adopt new technologies or processes.

The batch record review process, in particular, can be a time-consuming task, as it requires a thorough review of all production data to ensure that it is accurate and complete. This process can be further complicated by the need to investigate any deviations or discrepancies that are identified during the review process. The deviation investigation process can be lengthy and may require the involvement of multiple stakeholders, including production staff, quality control specialists, and regulatory affairs personnel.

Time saved versus time redeployed

While AI productivity tools may be able to save time by automating certain tasks or providing real-time answers to questions, this does not necessarily mean that the time saved will be redeployed in a meaningful way. For example, a shift supervisor may use the time saved from automated batch record review to focus on other tasks, such as optimizing production workflows or improving employee training. However, if the supervisor is not given the opportunity to redeploy their time in a meaningful way, the benefits of the AI productivity tool may not be fully realised.

In order to fully realise the benefits of AI productivity tools, pharmaceutical companies must be willing to rethink their workflows and processes, and to give employees the autonomy to redeploy their time in a meaningful way. This may require significant changes to the way that work is organised and managed, and may require ongoing support and training for employees. For more information on how AsscherAi can support your organisation, please visit our website at www.asscherai.com.

Self-reported time savings

Self-reported time savings can be a misleading metric, as they may not accurately reflect the actual time saved by the use of AI productivity tools. For example, an employee may report that they have saved several hours per week by using an AI-powered tool to automate a certain task, but this may not take into account the time spent on other tasks that are not directly related to the tool. Additionally, self-reported time savings may not account for the time spent by other employees or stakeholders who may be impacted by the use of the tool.

A more accurate measure of the benefits of AI productivity tools may be to look at the overall impact on the organisation, rather than just relying on self-reported time savings. This may involve looking at metrics such as productivity, quality, and employee engagement, and assessing whether the use of AI productivity tools has had a positive impact on these areas. By taking a more holistic approach to evaluating the benefits of AI productivity tools, pharmaceutical companies can get a more accurate picture of their impact and make informed decisions about their use.

Tasks worth removing first

When implementing AI productivity tools, it can be helpful to identify tasks that are repetitive, time-consuming, or prone to error, and to prioritize these tasks for automation or elimination. For example, a task such as data entry or document review may be a good candidate for automation, as it is repetitive and time-consuming, and may be prone to error if done manually. By removing or automating these tasks, pharmaceutical companies can free up employee time and reduce the risk of errors, which can have a positive impact on productivity and quality.

In order to identify tasks that are worth removing or automating, pharmaceutical companies may need to conduct a thorough review of their workflows and processes, and to assess which tasks are most amenable to automation or elimination. This may involve working with employees and stakeholders to identify areas where AI productivity tools can have the greatest impact, and to develop strategies for implementing these tools in a way that is effective and efficient. For more information on how to get started with AsscherAi, please contact us at www.asscherai.com/contact-us.

Limitations of AI productivity tools

While AI productivity tools can be a powerful way to improve productivity and quality in the pharmaceutical industry, they are not a panacea, and there are certain limitations and constraints that must be taken into account. For example, AI productivity tools may not be suitable for tasks that require a high degree of creativity or judgment, such as scientific research or clinical trial design. Additionally, AI productivity tools may require significant upfront investment in terms of time and resources, and may require ongoing support and maintenance in order to ensure that they continue to function effectively.

Furthermore, AI productivity tools may not be effective in situations where there is a high degree of uncertainty or ambiguity, such as in the early stages of product development or in situations where there is limited data available. In these situations, human judgment and expertise may be more effective, and AI productivity tools may not be able to add significant value. By understanding the limitations and constraints of AI productivity tools, pharmaceutical companies can make informed decisions about their use and ensure that they are deployed in a way that is effective and efficient.