• Written by Muhammed Abdulla NC, CEO

A new system is installed, trained, and validated, but months later, it remains underutilized, with many employees still relying on manual processes or workarounds. This scenario is all too common in the pharmaceutical industry, where the adoption of enterprise AI is hindered by the very processes that are meant to ensure quality and compliance. The consequences of this underutilization can be severe, leading to wasted resources, decreased efficiency, and a lack of trust in the system.

Pharmaceuticals' unique challenges

The pharmaceutical industry is subject to strict regulations and guidelines, which can make it difficult to implement new systems or processes. Change control overhead, batch record review, and deviation investigation time are just a few of the hurdles that must be overcome. These processes are necessary to ensure the quality and safety of pharmaceutical products, but they can also create a significant barrier to the adoption of new technologies. For example, a quality assurance manager may need to review and approve every change to a system, which can slow down the implementation process and make it more difficult to realize the benefits of the new technology.

The validation process for new systems can also be lengthy and complex, requiring significant documentation and testing. This can make it difficult to get a new system up and running quickly, and can also create a culture of risk aversion, where employees are hesitant to adopt new technologies due to the perceived risks and challenges. A shift supervisor may be reluctant to adopt a new system, for example, if they are concerned that it will require significant changes to their existing processes or procedures.

Building on existing routines

One way to overcome these challenges is to attach new capabilities to routines that already exist. By building on existing processes and systems, employees can more easily adapt to new technologies and see the benefits of their use. For example, a category manager may be able to use a new system to analyze data and make more informed decisions, but only if the system is integrated into their existing workflow. This can help to build trust and confidence in the new technology, and can also make it easier to demonstrate the value of the system to stakeholders.

By leveraging existing routines and processes, organizations can also reduce the risk of disruption and minimize the impact of change on employees. This can be especially important in the pharmaceutical industry, where consistency and reliability are critical. A well-designed system can help to ensure that new technologies are adopted quickly and effectively, and that employees are able to use them to improve their work.

Enabling the right questions

Another key factor in the successful adoption of enterprise AI is ensuring that the right people are able to ask the right questions. This is not just about providing access to the system, but also about ensuring that employees have the necessary training and support to use it effectively. For example, a maintenance technician may need to be able to ask questions about equipment performance or maintenance schedules, but may not have the necessary expertise to use the system without additional training.

By enabling the right questions, organizations can help to ensure that employees are able to get the most out of the system, and that they are able to use it to improve their work. This can involve providing training and support, as well as ensuring that the system is user-friendly and accessible. It is also important to recognize that different employees will have different needs and requirements, and to provide tailored support and training to meet those needs. For more information on how to implement a system that meets the needs of your organization, please contact us.

Rebuilding trust after mistakes

Even with the best systems and training, mistakes can still happen. The first wrong answer or incorrect result can be especially damaging, as it can erode trust and confidence in the system. However, it is possible to rebuild trust and restore confidence in the system. This can involve identifying and addressing the root cause of the mistake, as well as providing additional training and support to employees.

It is also important to recognize that mistakes can be an opportunity for growth and improvement. By analyzing what went wrong and making changes to prevent similar mistakes in the future, organizations can help to ensure that their systems are reliable and accurate. This can involve implementing new procedures or protocols, as well as providing additional training and support to employees. For example, a quality control specialist may need to review and update procedures for data entry or analysis, in order to prevent similar mistakes from happening in the future.

Limitations and exceptions

While enterprise AI can be a powerful tool for improving efficiency and decision-making, it is not a solution that is suitable for every organization or every situation. There may be cases where the costs and challenges of implementation outweigh the potential benefits, or where the system is not compatible with existing processes and systems. In these cases, it may be necessary to consider alternative solutions or approaches.

It is also important to recognize that enterprise AI is not a replacement for human judgment and expertise. While the system can provide valuable insights and recommendations, it is still important to have experienced and knowledgeable employees who can interpret and act on the results. By understanding the limitations and exceptions of enterprise AI, organizations can help to ensure that they are using the system effectively and getting the most out of their investment.